diff --git a/frontend/assets/chat-empty-icon.png b/frontend/assets/chat-empty-icon.png
new file mode 100644
index 0000000..106fbd1
Binary files /dev/null and b/frontend/assets/chat-empty-icon.png differ
diff --git a/frontend/assets/ergou-avatar.png b/frontend/assets/ergou-avatar.png
new file mode 100644
index 0000000..106fbd1
Binary files /dev/null and b/frontend/assets/ergou-avatar.png differ
diff --git a/frontend/css/auth.css b/frontend/css/auth.css
new file mode 100644
index 0000000..5c5ba51
--- /dev/null
+++ b/frontend/css/auth.css
@@ -0,0 +1,227 @@
+.entry-screen {
+  position: fixed;
+  inset: 0;
+  z-index: 50;
+  overflow: hidden;
+  background:
+    radial-gradient(circle at 64% 24%, color-mix(in oklch, var(--blue) 46%, transparent), transparent 28%),
+    radial-gradient(circle at 20% 78%, color-mix(in oklch, var(--green) 34%, transparent), transparent 34%),
+    linear-gradient(135deg, oklch(14% 0.04 232), oklch(18% 0.05 178) 48%, oklch(11% 0.038 250));
+  color: oklch(96% 0.018 190);
+  display: grid;
+  grid-template-columns: minmax(0, 1fr) minmax(280px, 390px);
+  gap: clamp(24px, 5vw, 76px);
+  align-items: end;
+  padding: clamp(24px, 6vw, 78px);
+  transition: transform 0.72s cubic-bezier(0.74, 0, 0.2, 1), opacity 0.72s ease;
+}
+
+.entry-screen.leaving {
+  transform: translateY(-102%);
+  opacity: 0.42;
+}
+
+.entry-grid,
+.entry-scan,
+.entry-orbit,
+.entry-particles {
+  position: absolute;
+  inset: 0;
+  pointer-events: none;
+}
+
+.entry-grid {
+  background:
+    linear-gradient(color-mix(in oklch, white 7%, transparent) 1px, transparent 1px),
+    linear-gradient(90deg, color-mix(in oklch, white 7%, transparent) 1px, transparent 1px);
+  background-size: 72px 72px;
+  mask-image: radial-gradient(circle at 58% 46%, black, transparent 76%);
+}
+
+.entry-scan {
+  width: 42%;
+  background: linear-gradient(90deg, transparent, color-mix(in oklch, var(--blue) 24%, transparent), transparent);
+  animation: scan-drift 5.6s linear infinite;
+  opacity: 0.68;
+}
+
+.entry-orbit {
+  inset: 7% 12% 10% auto;
+  width: min(58vw, 720px);
+  aspect-ratio: 1;
+  border-radius: 50%;
+  border: 1px solid color-mix(in oklch, white 13%, transparent);
+  animation: orbit-spin 18s linear infinite;
+}
+
+.entry-orbit::before,
+.entry-orbit::after,
+.entry-orbit span {
+  content: "";
+  position: absolute;
+  inset: 9%;
+  border: 1px solid color-mix(in oklch, var(--green) 16%, transparent);
+  border-radius: 50%;
+}
+
+.entry-orbit::after {
+  inset: 22%;
+  border-color: color-mix(in oklch, var(--blue) 20%, transparent);
+}
+
+.entry-orbit span {
+  width: 9px;
+  height: 9px;
+  inset: 17% auto auto 68%;
+  border: 0;
+  background: oklch(87% 0.1 186);
+  box-shadow: 0 0 24px color-mix(in oklch, var(--blue) 72%, transparent);
+}
+
+.entry-orbit span:nth-child(2) {
+  inset: 58% auto auto 18%;
+  background: oklch(86% 0.11 154);
+}
+
+.entry-orbit span:nth-child(3) {
+  inset: 76% auto auto 72%;
+  background: oklch(92% 0.08 74);
+}
+
+.entry-particles span {
+  position: absolute;
+  width: 2px;
+  height: 44px;
+  border-radius: 999px;
+  background: linear-gradient(180deg, transparent, color-mix(in oklch, white 58%, transparent));
+  animation: particle-rise 4.6s linear infinite;
+  opacity: 0.55;
+}
+
+.entry-particles span:nth-child(1) { left: 12%; top: 82%; animation-delay: -1.1s; }
+.entry-particles span:nth-child(2) { left: 26%; top: 66%; animation-delay: -2.4s; }
+.entry-particles span:nth-child(3) { left: 39%; top: 76%; animation-delay: -0.6s; }
+.entry-particles span:nth-child(4) { left: 50%; top: 84%; animation-delay: -3.2s; }
+.entry-particles span:nth-child(5) { left: 64%; top: 70%; animation-delay: -1.8s; }
+.entry-particles span:nth-child(6) { left: 78%; top: 80%; animation-delay: -2.8s; }
+.entry-particles span:nth-child(7) { left: 88%; top: 58%; animation-delay: -0.3s; }
+.entry-particles span:nth-child(8) { left: 18%; top: 44%; animation-delay: -3.8s; }
+.entry-particles span:nth-child(9) { left: 72%; top: 36%; animation-delay: -1.5s; }
+.entry-particles span:nth-child(10) { left: 92%; top: 72%; animation-delay: -4.2s; }
+
+.entry-copy,
+.entry-hud,
+.entry-lift {
+  position: relative;
+  z-index: 1;
+}
+
+.entry-copy {
+  max-width: 760px;
+  align-self: center;
+}
+
+.entry-brand {
+  display: inline-flex;
+  align-items: center;
+  gap: 12px;
+  margin-bottom: 24px;
+}
+
+.entry-brand span {
+  color: color-mix(in oklch, white 72%, transparent);
+  font-weight: 800;
+  letter-spacing: 0.08em;
+  text-transform: uppercase;
+}
+
+.entry-copy h1 {
+  max-width: 850px;
+  margin: 10px 0 18px;
+  font-size: clamp(3.2rem, 8vw, 8rem);
+  line-height: 0.92;
+  letter-spacing: 0;
+}
+
+.entry-lead {
+  max-width: 58ch;
+  margin: 0;
+  color: color-mix(in oklch, white 70%, transparent);
+  font-size: 1.02rem;
+  line-height: 1.8;
+}
+
+.entry-hud {
+  align-self: center;
+  border: 1px solid color-mix(in oklch, white 18%, transparent);
+  border-radius: var(--radius);
+  background: color-mix(in oklch, oklch(12% 0.04 236) 72%, transparent);
+  backdrop-filter: blur(18px);
+  box-shadow: 0 26px 90px color-mix(in oklch, black 38%, transparent);
+  overflow: hidden;
+}
+
+.entry-hud div {
+  padding: 18px;
+  border-top: 1px solid color-mix(in oklch, white 12%, transparent);
+}
+
+.entry-hud div:first-child {
+  border-top: 0;
+}
+
+.entry-hud span {
+  color: color-mix(in oklch, white 56%, transparent);
+  font-size: 0.76rem;
+}
+
+.entry-hud strong {
+  display: block;
+  margin-top: 8px;
+  font-size: 1.05rem;
+}
+
+.entry-lift {
+  position: absolute;
+  left: 50%;
+  bottom: 24px;
+  transform: translateX(-50%);
+  min-width: 154px;
+  min-height: 48px;
+  border: 1px solid color-mix(in oklch, white 24%, transparent);
+  border-radius: 999px;
+  background: color-mix(in oklch, white 10%, transparent);
+  color: oklch(96% 0.018 190);
+  backdrop-filter: blur(16px);
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  gap: 10px;
+  box-shadow: 0 14px 42px color-mix(in oklch, black 24%, transparent);
+  animation: lift-pulse 1.6s ease-in-out infinite;
+}
+
+.entry-lift:hover {
+  border-color: color-mix(in oklch, var(--blue) 54%, white 14%);
+  background: color-mix(in oklch, var(--blue) 22%, transparent);
+}
+
+@keyframes scan-drift {
+  from { transform: translateX(-55vw) skewX(-16deg); }
+  to { transform: translateX(122vw) skewX(-16deg); }
+}
+
+@keyframes orbit-spin {
+  to { transform: rotate(360deg); }
+}
+
+@keyframes particle-rise {
+  from { transform: translateY(60px); opacity: 0; }
+  20% { opacity: 0.58; }
+  to { transform: translateY(-180px); opacity: 0; }
+}
+
+@keyframes lift-pulse {
+  0%, 100% { transform: translateX(-50%) translateY(0); }
+  50% { transform: translateX(-50%) translateY(-7px); }
+}
diff --git a/frontend/css/base.css b/frontend/css/base.css
index aa793bc..927025a 100644
--- a/frontend/css/base.css
+++ b/frontend/css/base.css
@@ -1,15 +1,15 @@
 :root {
-  --bg: oklch(96% 0.014 142);
-  --surface: oklch(99% 0.006 142);
-  --surface-soft: oklch(97.5% 0.011 142);
-  --surface-raised: oklch(100% 0.004 142);
-  --ink: oklch(22% 0.025 148);
-  --muted: oklch(49% 0.027 148);
-  --line: oklch(89% 0.018 142);
-  --line-strong: oklch(80% 0.026 142);
+  --bg: oklch(96.5% 0.012 156);
+  --surface: oklch(99% 0.004 156);
+  --surface-soft: oklch(97.2% 0.01 156);
+  --surface-raised: oklch(100% 0.003 156);
+  --ink: oklch(22% 0.025 158);
+  --muted: oklch(48% 0.025 166);
+  --line: oklch(88% 0.017 160);
+  --line-strong: oklch(77% 0.034 168);
   --green: oklch(48% 0.105 154);
-  --green-dark: oklch(38% 0.092 154);
-  --blue: oklch(49% 0.105 230);
+  --green-dark: oklch(36% 0.092 154);
+  --blue: oklch(51% 0.112 222);
   --coral: oklch(53% 0.135 34);
   --amber: oklch(55% 0.12 72);
   --success-bg: oklch(93% 0.035 151);
@@ -46,7 +46,8 @@ body {
 }
 
 button,
-textarea {
+textarea,
+input {
   font: inherit;
 }
 
@@ -61,17 +62,25 @@ button:disabled {
 
 button:focus-visible,
 textarea:focus-visible,
+input:focus-visible,
 summary:focus-visible {
   outline: 3px solid color-mix(in oklch, var(--green) 42%, transparent);
   outline-offset: 2px;
 }
 
-.shell {
+.app-root {
   width: 100vw;
   height: 100vh;
   height: 100dvh;
   min-height: 0;
   overflow: hidden;
+}
+
+.shell {
+  width: 100%;
+  height: 100%;
+  min-height: 0;
+  overflow: hidden;
   display: grid;
   grid-template-columns: 248px 1fr;
 }
@@ -81,8 +90,10 @@ summary:focus-visible {
   min-height: 0;
   overflow-y: auto;
   padding: 22px 16px;
-  background: oklch(23% 0.032 148);
-  color: oklch(96% 0.014 142);
+  background:
+    linear-gradient(180deg, color-mix(in oklch, oklch(20% 0.034 160) 96%, var(--green) 4%), oklch(18% 0.026 166)),
+    oklch(20% 0.034 160);
+  color: oklch(96% 0.014 154);
   display: flex;
   flex-direction: column;
   gap: 24px;
@@ -97,13 +108,14 @@ summary:focus-visible {
 }
 
 .brand-mark {
-  width: 42px;
-  height: 42px;
+  width: 46px;
+  height: 46px;
   border-radius: var(--radius);
-  background: oklch(90% 0.055 138);
-  color: oklch(23% 0.032 148);
+  background: linear-gradient(135deg, oklch(91% 0.06 154), oklch(88% 0.05 218));
+  color: oklch(21% 0.032 160);
   display: grid;
   place-items: center;
+  font-size: 0.9rem;
   font-weight: 800;
 }
 
diff --git a/frontend/css/chat.css b/frontend/css/chat.css
index 53f8551..2263d6c 100644
--- a/frontend/css/chat.css
+++ b/frontend/css/chat.css
@@ -19,9 +19,15 @@
 
 .empty-state {
   max-width: 460px;
+  padding: 28px;
+  border: 1px solid var(--line);
+  border-radius: var(--radius);
+  background: color-mix(in oklch, var(--surface-raised) 84%, transparent);
+  backdrop-filter: blur(10px);
   margin: auto;
   text-align: center;
   color: var(--muted);
+  box-shadow: 0 16px 44px color-mix(in oklch, var(--ink) 8%, transparent);
 }
 
 .empty-state p {
@@ -53,19 +59,22 @@
   background: var(--surface-soft);
 }
 
-.empty-state i {
-  width: 54px;
-  height: 54px;
-  display: inline-grid;
-  place-items: center;
-  border-radius: var(--radius);
-  background: #e6efe7;
-  color: var(--green);
-  font-size: 1.5rem;
+.empty-state-icon {
+  width: 96px;
+  height: 96px;
+  border: 1px solid color-mix(in oklch, var(--blue) 28%, var(--line));
+  border-radius: 24px;
+  display: inline-block;
+  object-fit: cover;
+  object-position: center;
+  background: var(--surface-soft);
+  box-shadow:
+    0 12px 30px rgba(34, 44, 38, 0.14),
+    0 0 0 8px color-mix(in oklch, var(--blue) 7%, transparent);
 }
 
 .empty-state h3 {
-  margin: 16px 0 8px;
+  margin: 8px 0;
   color: var(--ink);
 }
 
@@ -96,6 +105,16 @@
   justify-content: flex-end;
 }
 
+.agent-role-avatar {
+  width: 26px;
+  height: 26px;
+  border: 1px solid color-mix(in oklch, var(--blue) 22%, var(--line));
+  border-radius: 8px;
+  object-fit: cover;
+  object-position: center;
+  box-shadow: 0 4px 12px color-mix(in oklch, var(--ink) 10%, transparent);
+}
+
 .message-role button,
 .icon-danger,
 .history-drawer header button {
@@ -111,10 +130,11 @@
   padding: 14px 16px;
   border: 1px solid var(--line);
   border-radius: var(--radius);
-  background: var(--surface);
+  background: color-mix(in oklch, var(--surface) 94%, transparent);
   line-height: 1.65;
   overflow-wrap: anywhere;
   box-shadow: 0 3px 16px rgba(34, 44, 38, 0.05);
+  backdrop-filter: blur(8px);
 }
 
 .from-user .message-bubble {
diff --git a/frontend/css/overlays.css b/frontend/css/overlays.css
index d7fd3fd..8b7df63 100644
--- a/frontend/css/overlays.css
+++ b/frontend/css/overlays.css
@@ -104,6 +104,21 @@
   border-radius: 999px;
 }
 
+.composer textarea {
+  overflow: hidden !important;
+  scrollbar-width: none !important;
+}
+
+.composer textarea::-webkit-scrollbar {
+  width: 0;
+  height: 0;
+  display: none;
+}
+
+.composer textarea::-webkit-scrollbar-thumb {
+  background: transparent;
+}
+
 .skeleton-stack {
   padding: var(--space-md);
   display: grid;
diff --git a/frontend/css/responsive.css b/frontend/css/responsive.css
index 2ac7c2a..c02fa70 100644
--- a/frontend/css/responsive.css
+++ b/frontend/css/responsive.css
@@ -54,3 +54,112 @@
     padding: 0 10px;
   }
 }
+
+@media (max-width: 760px) {
+  .entry-screen {
+    grid-template-columns: 1fr;
+    overflow-y: auto;
+    align-items: center;
+  }
+
+  .entry-copy h1 {
+    font-size: clamp(3rem, 16vw, 5rem);
+  }
+
+  .entry-hud {
+    display: none;
+  }
+}
+
+@media (max-width: 560px) {
+  .entry-screen {
+    padding: 22px;
+  }
+
+  .entry-copy {
+    align-self: center;
+  }
+
+  .entry-lead {
+    font-size: 0.95rem;
+  }
+
+  .shell {
+    grid-template-columns: 1fr;
+    grid-template-rows: auto 1fr;
+  }
+
+  .rail {
+    max-height: 72px;
+    overflow: hidden;
+    padding: 10px 12px;
+    flex-direction: row;
+    align-items: center;
+    gap: 12px;
+  }
+
+  .brand {
+    min-width: 54px;
+    padding: 0;
+    border-bottom: 0;
+  }
+
+  .nav-list {
+    display: flex;
+    flex: 1;
+    overflow-x: auto;
+  }
+
+  .nav-list button,
+  .secondary-action,
+  .danger-action {
+    min-width: 42px;
+  }
+
+  .rail-actions {
+    margin-top: 0;
+    display: flex;
+    gap: 8px;
+  }
+
+  .rail-actions .secondary-action,
+  .rail-actions .danger-action {
+    width: 42px;
+    padding: 0;
+    flex: 0 0 42px;
+  }
+
+  .workspace {
+    height: calc(100dvh - 72px);
+    grid-template-rows: auto 1fr;
+  }
+
+  .topbar {
+    align-items: flex-start;
+    flex-direction: column;
+  }
+
+  .topbar-status {
+    width: 100%;
+    overflow-x: auto;
+  }
+
+  .chat-stream,
+  .panel-view {
+    padding: 14px;
+  }
+
+  .composer {
+    margin: 0 14px 14px;
+  }
+
+  .section-head,
+  .review-item header {
+    flex-direction: column;
+  }
+
+  .table-row {
+    grid-template-columns: minmax(120px, 1fr) 56px 44px 42px;
+    gap: 8px;
+  }
+}
diff --git a/frontend/css/trace-composer.css b/frontend/css/trace-composer.css
index 27619fb..8afd6c1 100644
--- a/frontend/css/trace-composer.css
+++ b/frontend/css/trace-composer.css
@@ -88,12 +88,22 @@
 .composer textarea {
   width: 100%;
   max-height: 180px;
-  min-height: 42px;
+  min-height: 46px;
+  line-height: 1.35;
   resize: none;
+  overflow: hidden;
+  scrollbar-width: none;
+  appearance: none;
   border: 0;
   outline: 0;
-  padding: 10px 0;
+  padding: 13px 0 10px;
   background: transparent;
   color: var(--ink);
 }
 
+.composer textarea::-webkit-scrollbar {
+  width: 0;
+  height: 0;
+  display: none;
+}
+
diff --git a/frontend/css/workspace.css b/frontend/css/workspace.css
index 0176748..ddb76a4 100644
--- a/frontend/css/workspace.css
+++ b/frontend/css/workspace.css
@@ -7,13 +7,18 @@
   overflow: hidden;
   display: grid;
   grid-template-rows: 76px 1fr;
-  background: var(--surface-soft);
+  background:
+    linear-gradient(135deg, color-mix(in oklch, var(--green) 7%, transparent), transparent 34%),
+    linear-gradient(45deg, transparent 54%, color-mix(in oklch, var(--blue) 6%, transparent)),
+    repeating-linear-gradient(90deg, color-mix(in oklch, var(--ink) 4%, transparent) 0 1px, transparent 1px 56px),
+    repeating-linear-gradient(0deg, color-mix(in oklch, var(--ink) 3%, transparent) 0 1px, transparent 1px 56px),
+    var(--surface-soft);
 }
 
 .topbar {
   padding: 16px 28px;
   border-bottom: 1px solid var(--line);
-  background: color-mix(in oklch, var(--surface-raised) 88%, transparent);
+  background: color-mix(in oklch, var(--surface-raised) 90%, transparent);
   backdrop-filter: blur(12px);
   display: flex;
   align-items: center;
diff --git a/frontend/index.html b/frontend/index.html
index 3baa46b..e36bbfd 100644
--- a/frontend/index.html
+++ b/frontend/index.html
@@ -4,7 +4,7 @@
   <meta charset="UTF-8">
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   <title>NebulaNest Agent Console</title>
-  <link rel="stylesheet" href="style.css">
+  <link rel="stylesheet" href="style.css?v=20260523-3">
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/styles/github.min.css">
   <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
@@ -12,248 +12,304 @@
   <script src="https://unpkg.com/vue@3/dist/vue.global.js"></script>
 </head>
 <body>
-  <div id="app" class="shell">
-    <aside class="rail">
-      <div class="brand">
-        <div class="brand-mark">NN</div>
-        <div>
-          <h1>NebulaNest</h1>
-          <p>Agent Console</p>
-        </div>
+  <div id="app" class="app-root">
+    <section
+      v-if="showEntry"
+      :class="['entry-screen', { leaving: entryLeaving }]"
+      aria-label="NebulaNest 入口"
+      @wheel="handleEntryWheel"
+      @touchstart="handleEntryTouchStart"
+      @touchend="handleEntryTouchEnd"
+    >
+      <div class="entry-grid" aria-hidden="true"></div>
+      <div class="entry-orbit" aria-hidden="true">
+        <span></span><span></span><span></span>
+      </div>
+      <div class="entry-scan" aria-hidden="true"></div>
+      <div class="entry-particles" aria-hidden="true">
+        <span></span><span></span><span></span><span></span><span></span>
+        <span></span><span></span><span></span><span></span><span></span>
       </div>
 
-      <nav class="nav-list" aria-label="主导航">
-        <button :class="{active: activeView === 'chat'}" @click="switchView('chat')" title="对话" aria-label="打开对话">
-          <i class="fas fa-comments"></i><span>对话</span>
-        </button>
-        <button :class="{active: activeView === 'knowledge'}" @click="switchView('knowledge')" title="知识库" aria-label="打开知识库">
-          <i class="fas fa-folder-tree"></i><span>知识库</span>
-        </button>
-        <button :class="{active: activeView === 'reviews'}" @click="switchView('reviews')" title="人工审核" aria-label="打开人工审核">
-          <i class="fas fa-user-check"></i><span>人工审核</span>
-          <strong v-if="pendingReviewCount">{{ pendingReviewCount }}</strong>
-        </button>
-        <button :class="{active: activeView === 'ops'}" @click="switchView('ops')" title="运行回调" aria-label="打开运行回调">
-          <i class="fas fa-triangle-exclamation"></i><span>运行回调</span>
-          <strong v-if="openFailureCount">{{ openFailureCount }}</strong>
-        </button>
-        <button :class="{active: showHistorySidebar}" @click="handleHistory" title="历史" aria-label="打开历史会话">
-          <i class="fas fa-clock-rotate-left"></i><span>历史</span>
-        </button>
-      </nav>
-
-      <div class="rail-actions">
-        <button class="secondary-action" @click="handleNewChat">
-          <i class="fas fa-plus"></i><span>新会话</span>
-        </button>
-        <button class="danger-action" @click="handleClearChat">
-          <i class="fas fa-trash"></i><span>清空当前</span>
-        </button>
+      <div class="entry-copy">
+        <div class="entry-brand">
+          <div class="brand-mark">二狗</div>
+          <span>NebulaNest</span>
+        </div>
+        <p class="eyebrow">Agent / RAG / Review / Ops</p>
+        <h1>AI智能对话</h1>
+        <p class="entry-lead">本地知识库、混合检索、可追踪对话和人工闭环，被收束进一个更安静的作战面板。</p>
       </div>
-    </aside>
 
-    <main class="workspace">
-      <header class="topbar">
+      <div class="entry-hud" aria-hidden="true">
         <div>
-          <p class="eyebrow">{{ viewTitle.eyebrow }}</p>
-          <h2>{{ viewTitle.title }}</h2>
+          <span>Vector Field</span>
+          <strong>Milvus Online</strong>
         </div>
-        <div class="topbar-status">
-          <span class="status-pill"><i class="fas fa-circle"></i> SSE 在线</span>
-          <span class="status-pill review-indicator"><i class="fas fa-user-check"></i>{{ pendingReviewCount }} 待审</span>
-          <span class="status-pill failure-indicator"><i class="fas fa-triangle-exclamation"></i>{{ openFailureCount }} 回调</span>
-          <span class="session-pill" :title="sessionId">{{ sessionId }}</span>
+        <div>
+          <span>Retrieval Mesh</span>
+          <strong>Hybrid RAG</strong>
+        </div>
+        <div>
+          <span>Review Queue</span>
+          <strong>{{ pendingReviewCount }} Pending</strong>
         </div>
-      </header>
+      </div>
 
-      <section v-show="activeView === 'chat'" class="chat-layout">
-        <div class="chat-stream" ref="chatContainer" :aria-busy="isLoading">
-          <div v-if="messages.length === 0" class="empty-state">
-            <i class="fas fa-compass"></i>
-            <h3>从一个问题开始</h3>
-            <p>支持流式回答、RAG 过程追踪、人工审核提交和会话状态恢复。</p>
-            <div class="empty-actions">
-              <button @click="switchView('knowledge')" type="button">检查知识库</button>
-              <button @click="switchView('reviews')" type="button">查看审核队列</button>
-            </div>
+      <button class="entry-lift" type="button" @click="enterWorkspace" aria-label="上滑进入控制台">
+        <i class="fas fa-chevron-up"></i>
+        <span>上滑进入</span>
+      </button>
+    </section>
+
+    <div v-if="hasEntered" class="shell">
+      <aside class="rail">
+        <div class="brand">
+          <div class="brand-mark">二狗</div>
+          <div>
+            <h1>NebulaNest</h1>
+            <p>Agent Console</p>
           </div>
+        </div>
 
-          <article v-for="(msg, index) in messages" :key="msg.id || index" :class="['message', msg.isUser ? 'from-user' : 'from-agent']">
-            <div class="message-role">
-              <span>{{ msg.isUser ? 'User' : 'Agent' }}</span>
-              <button v-if="!msg.isUser && msg.text" @click="submitForReview(msg, index)" title="提交人工审核" aria-label="提交人工审核">
-                <i class="fas fa-user-check"></i>
-              </button>
-            </div>
+        <nav class="nav-list" aria-label="主导航">
+          <button :class="{active: activeView === 'chat'}" @click="switchView('chat')" title="对话" aria-label="打开对话">
+            <i class="fas fa-comments"></i><span>对话</span>
+          </button>
+          <button :class="{active: activeView === 'knowledge'}" @click="switchView('knowledge')" title="知识库" aria-label="打开知识库">
+            <i class="fas fa-folder-tree"></i><span>知识库</span>
+          </button>
+          <button :class="{active: activeView === 'reviews'}" @click="switchView('reviews')" title="人工审核" aria-label="打开人工审核">
+            <i class="fas fa-user-check"></i><span>人工审核</span>
+            <strong v-if="pendingReviewCount">{{ pendingReviewCount }}</strong>
+          </button>
+          <button :class="{active: activeView === 'ops'}" @click="switchView('ops')" title="运行回调" aria-label="打开运行回调">
+            <i class="fas fa-triangle-exclamation"></i><span>运行回调</span>
+            <strong v-if="openFailureCount">{{ openFailureCount }}</strong>
+          </button>
+          <button :class="{active: showHistorySidebar}" @click="handleHistory" title="历史" aria-label="打开历史会话">
+            <i class="fas fa-clock-rotate-left"></i><span>历史</span>
+          </button>
+        </nav>
 
-            <div v-if="!msg.isUser && msg.isThinking && !msg.text" class="message-bubble thinking">
-              <div class="thinking-line">
-                <span class="loader"></span>
-                <strong>{{ activeThinkingLabel(msg) }}</strong>
-              </div>
-              <div v-if="msg.ragSteps && msg.ragSteps.length" class="trace-steps">
-                <div v-for="(step, sIdx) in msg.ragSteps" :key="sIdx">
-                  <span>{{ step.icon || '•' }}</span>
-                  <b>{{ step.label }}</b>
-                  <small v-if="step.detail">{{ step.detail }}</small>
-                </div>
+        <div class="rail-actions">
+          <button class="secondary-action" @click="handleNewChat" title="新会话" aria-label="新会话">
+            <i class="fas fa-plus"></i><span>新会话</span>
+          </button>
+          <button class="danger-action" @click="handleClearChat" title="清空当前" aria-label="清空当前">
+            <i class="fas fa-trash"></i><span>清空当前</span>
+          </button>
+          <button class="secondary-action" @click="returnToEntry" title="返回入口" aria-label="返回入口">
+            <i class="fas fa-arrow-up-from-bracket"></i><span>入口</span>
+          </button>
+        </div>
+      </aside>
+
+      <main class="workspace">
+        <header class="topbar">
+          <div>
+            <p class="eyebrow">{{ viewTitle.eyebrow }}</p>
+            <h2>{{ viewTitle.title }}</h2>
+          </div>
+          <div class="topbar-status">
+            <span class="status-pill"><i class="fas fa-circle"></i> SSE 在线</span>
+            <span class="status-pill review-indicator"><i class="fas fa-user-check"></i>{{ pendingReviewCount }} 待审</span>
+            <span class="status-pill failure-indicator"><i class="fas fa-triangle-exclamation"></i>{{ openFailureCount }} 回调</span>
+            <span class="session-pill" :title="sessionId">{{ sessionId }}</span>
+          </div>
+        </header>
+
+        <section v-show="activeView === 'chat'" class="chat-layout">
+          <div class="chat-stream" ref="chatContainer" :aria-busy="isLoading">
+            <div v-if="messages.length === 0" class="empty-state">
+              <img class="empty-state-icon" src="assets/ergou-avatar.png" alt="二狗">
+              <p class="eyebrow">Ready</p>
+              <h3>从一个问题开始</h3>
+              <p>支持流式回答、RAG 过程追踪、人工审核提交和会话状态恢复。</p>
+              <div class="empty-actions">
+                <button @click="switchView('knowledge')" type="button">检查知识库</button>
+                <button @click="switchView('reviews')" type="button">查看审核队列</button>
               </div>
             </div>
 
-            <div v-else class="message-bubble" v-html="msg.isUser ? escapeHtml(msg.text) : parseMarkdown(msg.text)"></div>
-
-            <details v-if="!msg.isUser && msg.ragTrace" class="trace-panel">
-              <summary>检索与调用轨迹</summary>
-              <div class="trace-grid">
-                <span>工具</span><b>{{ msg.ragTrace.tool_name || '未使用' }}</b>
-                <span>召回模式</span><b>{{ msg.ragTrace.retrieval_mode || '未知' }}</b>
-                <span>评分路由</span><b>{{ msg.ragTrace.grade_score || '-' }} / {{ msg.ragTrace.grade_route || '-' }}</b>
-                <span>RAGFlow</span><b>{{ msg.ragTrace.ragflow_applied ? '已参与召回' : (msg.ragTrace.ragflow_enabled ? '已启用未命中' : '未启用') }}</b>
-                <span v-if="msg.ragTrace.ragflow_error">RAGFlow 错误</span><b v-if="msg.ragTrace.ragflow_error">{{ msg.ragTrace.ragflow_error }}</b>
+            <article v-for="(msg, index) in messages" :key="msg.id || index" :class="['message', msg.isUser ? 'from-user' : 'from-agent']">
+              <div class="message-role">
+                <img v-if="!msg.isUser" class="agent-role-avatar" src="assets/ergou-avatar.png" alt="二狗">
+                <span>{{ msg.isUser ? 'User' : '二狗' }}</span>
+                <button v-if="!msg.isUser && msg.text" @click="submitForReview(msg, index)" title="提交人工审核" aria-label="提交人工审核">
+                  <i class="fas fa-user-check"></i>
+                </button>
               </div>
-              <div v-if="sourceChunks(msg).length" class="sources">
-                <h4>引用片段</h4>
-                <div v-for="(chunk, cIdx) in sourceChunks(msg)" :key="cIdx" class="source-row">
-                  <div>
-                    <strong>{{ chunk.filename || 'Unknown' }}</strong>
-                    <span>{{ formatSourceMeta(chunk) }}</span>
+
+              <div v-if="!msg.isUser && msg.isThinking && !msg.text" class="message-bubble thinking">
+                <div class="thinking-line">
+                  <span class="loader"></span>
+                  <strong>{{ activeThinkingLabel(msg) }}</strong>
+                </div>
+                <div v-if="msg.ragSteps && msg.ragSteps.length" class="trace-steps">
+                  <div v-for="(step, sIdx) in msg.ragSteps" :key="sIdx">
+                    <span>{{ step.icon || '•' }}</span>
+                    <b>{{ step.label }}</b>
+                    <small v-if="step.detail">{{ step.detail }}</small>
                   </div>
-                  <p>{{ chunk.text }}</p>
                 </div>
               </div>
-            </details>
-          </article>
-        </div>
 
-        <form class="composer" @submit.prevent="handleSend">
-          <button type="button" title="知识库" aria-label="打开知识库" @click="switchView('knowledge')"><i class="fas fa-paperclip"></i></button>
-          <textarea
-            v-model="userInput"
-            ref="textarea"
-            rows="1"
-            aria-label="消息输入"
-            placeholder="输入消息，Enter 发送，Shift + Enter 换行"
-            @keydown="handleKeyDown"
-            @compositionstart="isComposing = true"
-            @compositionend="isComposing = false"
-            @input="autoResize"
-          ></textarea>
-          <button v-if="isLoading" type="button" class="stop" title="终止" aria-label="终止回答" @click="handleStop"><i class="fas fa-stop"></i></button>
-          <button v-else type="submit" title="发送" aria-label="发送消息" :disabled="!userInput.trim()"><i class="fas fa-paper-plane"></i></button>
-        </form>
-      </section>
+              <div v-else class="message-bubble" v-html="msg.isUser ? escapeHtml(msg.text) : parseMarkdown(msg.text)"></div>
 
-      <section v-show="activeView === 'knowledge'" class="panel-view">
-        <div class="section-head">
-          <div>
-            <h3>文档管理</h3>
-            <p>本地 Milvus 仍是默认知识库；配置 RAGFLOW_* 后会额外接入 RAGFlow 召回。</p>
+              <details v-if="!msg.isUser && msg.ragTrace" class="trace-panel">
+                <summary>检索与调用轨迹</summary>
+                <div class="trace-grid">
+                  <span>工具</span><b>{{ msg.ragTrace.tool_name || '未使用' }}</b>
+                  <span>召回模式</span><b>{{ msg.ragTrace.retrieval_mode || '未知' }}</b>
+                  <span>评分路由</span><b>{{ msg.ragTrace.grade_score || '-' }} / {{ msg.ragTrace.grade_route || '-' }}</b>
+                  <span>RAGFlow</span><b>{{ msg.ragTrace.ragflow_applied ? '已参与召回' : (msg.ragTrace.ragflow_enabled ? '已启用未命中' : '未启用') }}</b>
+                  <span v-if="msg.ragTrace.ragflow_error">RAGFlow 错误</span><b v-if="msg.ragTrace.ragflow_error">{{ msg.ragTrace.ragflow_error }}</b>
+                </div>
+                <div v-if="sourceChunks(msg).length" class="sources">
+                  <h4>引用片段</h4>
+                  <div v-for="(chunk, cIdx) in sourceChunks(msg)" :key="cIdx" class="source-row">
+                    <div>
+                      <strong>{{ chunk.filename || 'Unknown' }}</strong>
+                      <span>{{ formatSourceMeta(chunk) }}</span>
+                    </div>
+                    <p>{{ chunk.text }}</p>
+                  </div>
+                </div>
+              </details>
+            </article>
           </div>
-          <button class="secondary-action inline" @click="loadDocuments"><i class="fas fa-rotate"></i>刷新</button>
-        </div>
 
-        <div class="upload-band">
-          <input ref="fileInput" type="file" accept=".pdf,.doc,.docx,.ppt,.pptx,.xls,.xlsx,.csv,.txt" @change="handleFileSelect" hidden>
-          <button class="primary-action" @click="$refs.fileInput.click()"><i class="fas fa-cloud-arrow-up"></i>选择文件</button>
-          <span v-if="selectedFile">{{ selectedFile.name }}</span>
-          <button v-if="selectedFile" class="secondary-action inline" :disabled="isUploading" @click="uploadDocument">
-            <i class="fas fa-upload"></i>{{ isUploading ? '处理中' : '入库' }}
-          </button>
-          <p v-if="uploadProgress">{{ uploadProgress }}</p>
-        </div>
+          <form class="composer" @submit.prevent="handleSend">
+            <button type="button" title="知识库" aria-label="打开知识库" @click="switchView('knowledge')"><i class="fas fa-paperclip"></i></button>
+            <textarea
+              v-model="userInput"
+              ref="textarea"
+              rows="1"
+              aria-label="消息输入"
+              placeholder="输入消息..."
+              @keydown="handleKeyDown"
+              @compositionstart="isComposing = true"
+              @compositionend="isComposing = false"
+              @input="autoResize"
+            ></textarea>
+            <button v-if="isLoading" type="button" class="stop" title="终止" aria-label="终止回答" @click="handleStop"><i class="fas fa-stop"></i></button>
+            <button v-else type="submit" title="发送" aria-label="发送消息" :disabled="!userInput.trim()"><i class="fas fa-paper-plane"></i></button>
+          </form>
+        </section>
 
-        <div class="table-list">
-          <div class="table-row head">
-            <span>文件</span><span>类型</span><span>片段</span><span>操作</span>
-          </div>
-          <div v-if="documentsLoading" class="skeleton-stack" aria-label="正在加载文档">
-            <span></span><span></span><span></span>
+        <section v-show="activeView === 'knowledge'" class="panel-view">
+          <div class="section-head">
+            <div>
+              <h3>文档管理</h3>
+              <p>本地 Milvus 是默认知识库；配置 RAGFLOW_* 后会额外接入 RAGFlow 召回。</p>
+            </div>
+            <button class="secondary-action inline" @click="loadDocuments"><i class="fas fa-rotate"></i>刷新</button>
           </div>
-          <div v-else-if="documents.length === 0" class="muted-row">暂无文档</div>
-          <div v-for="doc in documents" :key="doc.filename" class="table-row">
-            <span><i :class="getFileIcon(doc.file_type || doc.filename)"></i>{{ doc.filename }}</span>
-            <span>{{ doc.file_type || 'document' }}</span>
-            <span>{{ doc.chunk_count }}</span>
-            <span><button class="icon-danger" @click="deleteDocument(doc.filename)" title="删除"><i class="fas fa-trash"></i></button></span>
+
+          <div class="upload-band">
+            <input ref="fileInput" type="file" accept=".pdf,.doc,.docx,.ppt,.pptx,.xls,.xlsx,.csv,.txt" @change="handleFileSelect" hidden>
+            <button class="primary-action" @click="$refs.fileInput.click()"><i class="fas fa-cloud-arrow-up"></i>选择文件</button>
+            <span v-if="selectedFile">{{ selectedFile.name }}</span>
+            <button v-if="selectedFile" class="secondary-action inline" :disabled="isUploading" @click="uploadDocument">
+              <i class="fas fa-upload"></i>{{ isUploading ? '处理中' : '入库' }}
+            </button>
+            <p v-if="uploadProgress">{{ uploadProgress }}</p>
           </div>
-        </div>
-      </section>
 
-      <section v-show="activeView === 'reviews'" class="panel-view">
-        <div class="section-head">
-          <div>
-            <h3>Human-in-the-loop 审核</h3>
-            <p>将回答提交到审核队列后，可批准、驳回或给出修订稿。</p>
+          <div class="table-list">
+            <div class="table-row head">
+              <span>文件</span><span>类型</span><span>片段</span><span>操作</span>
+            </div>
+            <div v-if="documentsLoading" class="skeleton-stack" aria-label="正在加载文档">
+              <span></span><span></span><span></span>
+            </div>
+            <div v-else-if="documents.length === 0" class="muted-row">暂无文档</div>
+            <div v-for="doc in documents" :key="doc.filename" class="table-row">
+              <span><i :class="getFileIcon(doc.file_type || doc.filename)"></i>{{ doc.filename }}</span>
+              <span>{{ doc.file_type || 'document' }}</span>
+              <span>{{ doc.chunk_count }}</span>
+              <span><button class="icon-danger" @click="deleteDocument(doc.filename)" title="删除"><i class="fas fa-trash"></i></button></span>
+            </div>
           </div>
-          <button class="secondary-action inline" @click="loadReviews"><i class="fas fa-rotate"></i>刷新</button>
-        </div>
+        </section>
 
-        <div class="review-list">
-          <article v-if="reviews.length === 0" class="muted-row">暂无审核项</article>
-          <article v-for="review in reviews" :key="review.id" class="review-item">
-            <header>
-              <span :class="['badge', review.status]">{{ reviewStatusLabel(review.status) }}</span>
-              <small>{{ new Date(review.updated_at).toLocaleString() }}</small>
-            </header>
-            <h4>{{ review.question || '未记录问题' }}</h4>
-            <p>{{ review.answer }}</p>
-            <textarea v-model="review.revised_answer" placeholder="可选：填写人工修订稿"></textarea>
-            <div class="item-actions">
-              <button @click="updateReview(review, 'approved')"><i class="fas fa-check"></i>批准</button>
-              <button @click="updateReview(review, 'needs_revision')"><i class="fas fa-pen"></i>需修订</button>
-              <button class="danger-lite" @click="updateReview(review, 'rejected')"><i class="fas fa-xmark"></i>驳回</button>
+        <section v-show="activeView === 'reviews'" class="panel-view">
+          <div class="section-head">
+            <div>
+              <h3>Human-in-the-loop 审核</h3>
+              <p>把回答提交到审核队列后，可批准、驳回或给出修订稿。</p>
             </div>
-          </article>
-        </div>
-      </section>
+            <button class="secondary-action inline" @click="loadReviews"><i class="fas fa-rotate"></i>刷新</button>
+          </div>
 
-      <section v-show="activeView === 'ops'" class="panel-view">
-        <div class="section-head">
-          <div>
-            <h3>工具失败回调</h3>
-            <p>工具、RAGFlow 或本地检索失败会落盘，方便人工补偿、重试或关闭。</p>
+          <div class="review-list">
+            <article v-if="reviews.length === 0" class="muted-row">暂无审核项</article>
+            <article v-for="review in reviews" :key="review.id" class="review-item">
+              <header>
+                <span :class="['badge', review.status]">{{ reviewStatusLabel(review.status) }}</span>
+                <small>{{ new Date(review.updated_at).toLocaleString() }}</small>
+              </header>
+              <h4>{{ review.question || '未记录问题' }}</h4>
+              <p>{{ review.answer }}</p>
+              <textarea v-model="review.revised_answer" placeholder="可选：填写人工修订稿"></textarea>
+              <div class="item-actions">
+                <button @click="updateReview(review, 'approved')"><i class="fas fa-check"></i>批准</button>
+                <button @click="updateReview(review, 'needs_revision')"><i class="fas fa-pen"></i>需修订</button>
+                <button class="danger-lite" @click="updateReview(review, 'rejected')"><i class="fas fa-xmark"></i>驳回</button>
+              </div>
+            </article>
           </div>
-          <button class="secondary-action inline" @click="loadFailures"><i class="fas fa-rotate"></i>刷新</button>
-        </div>
+        </section>
 
-        <div class="review-list">
-          <article v-if="activeFailures.length === 0" class="muted-row">暂无待处理失败记录</article>
-          <article v-for="failure in activeFailures" :key="failure.id" class="review-item">
-            <header>
-              <span :class="['badge', failure.status]">{{ failureStatusLabel(failure.status) }}</span>
-              <small>
-                <span v-if="failure.occurrence_count && failure.occurrence_count > 1">重复 {{ failure.occurrence_count }} 次 · </span>
-                {{ new Date(failure.updated_at).toLocaleString() }}
-              </small>
-            </header>
-            <h4>{{ failure.tool_name }}</h4>
-            <p>{{ failure.error }}</p>
-            <p v-if="failure.fallback" class="muted-text">降级策略：{{ failure.fallback }}</p>
-            <p v-if="failure.callback_note" class="muted-text">回调备注：{{ failure.callback_note }}</p>
-            <textarea v-model="failure.callback_note" placeholder="记录回调结果或补偿动作"></textarea>
-            <div class="item-actions">
-              <button @click="updateFailure(failure, 'retry_requested')"><i class="fas fa-rotate-right"></i>请求重试</button>
-              <button @click="updateFailure(failure, 'resolved')"><i class="fas fa-check"></i>已处理</button>
-              <button class="danger-lite" @click="updateFailure(failure, 'ignored')"><i class="fas fa-ban"></i>忽略</button>
+        <section v-show="activeView === 'ops'" class="panel-view">
+          <div class="section-head">
+            <div>
+              <h3>工具失败回调</h3>
+              <p>工具、RAGFlow 或本地检索失败会落盘，方便人工补偿、重试或关闭。</p>
             </div>
-          </article>
-        </div>
-      </section>
+            <button class="secondary-action inline" @click="loadFailures"><i class="fas fa-rotate"></i>刷新</button>
+          </div>
 
-      <aside v-if="showHistorySidebar" class="history-drawer">
-        <header>
-          <h3>历史会话</h3>
-          <button @click="showHistorySidebar = false" title="关闭"><i class="fas fa-xmark"></i></button>
-        </header>
-        <div class="history-list">
-          <div v-if="sessions.length === 0" class="muted-row">暂无历史记录</div>
-          <button v-for="session in sessions" :key="session.session_id" :class="['history-row', {active: session.session_id === sessionId}]" @click="loadSession(session.session_id)">
-            <span>{{ session.session_id }}</span>
-            <small>{{ session.message_count }} 条 · {{ new Date(session.updated_at).toLocaleString() }}</small>
-          </button>
-        </div>
-      </aside>
-    </main>
+          <div class="review-list">
+            <article v-if="activeFailures.length === 0" class="muted-row">暂无待处理失败记录</article>
+            <article v-for="failure in activeFailures" :key="failure.id" class="review-item">
+              <header>
+                <span :class="['badge', failure.status]">{{ failureStatusLabel(failure.status) }}</span>
+                <small>
+                  <span v-if="failure.occurrence_count && failure.occurrence_count > 1">重复 {{ failure.occurrence_count }} 次 · </span>
+                  {{ new Date(failure.updated_at).toLocaleString() }}
+                </small>
+              </header>
+              <h4>{{ failure.tool_name }}</h4>
+              <p>{{ failure.error }}</p>
+              <p v-if="failure.fallback" class="muted-text">降级策略：{{ failure.fallback }}</p>
+              <p v-if="failure.callback_note" class="muted-text">回调备注：{{ failure.callback_note }}</p>
+              <textarea v-model="failure.callback_note" placeholder="记录回调结果或补偿动作"></textarea>
+              <div class="item-actions">
+                <button @click="updateFailure(failure, 'retry_requested')"><i class="fas fa-rotate-right"></i>请求重试</button>
+                <button @click="updateFailure(failure, 'resolved')"><i class="fas fa-check"></i>已处理</button>
+                <button class="danger-lite" @click="updateFailure(failure, 'ignored')"><i class="fas fa-ban"></i>忽略</button>
+              </div>
+            </article>
+          </div>
+        </section>
+
+        <aside v-if="showHistorySidebar" class="history-drawer">
+          <header>
+            <h3>历史会话</h3>
+            <button @click="showHistorySidebar = false" title="关闭"><i class="fas fa-xmark"></i></button>
+          </header>
+          <div class="history-list">
+            <div v-if="sessions.length === 0" class="muted-row">暂无历史记录</div>
+            <button v-for="session in sessions" :key="session.session_id" :class="['history-row', {active: session.session_id === sessionId}]" @click="loadSession(session.session_id)">
+              <span>{{ session.session_id }}</span>
+              <small>{{ session.message_count }} 条 · {{ new Date(session.updated_at).toLocaleString() }}</small>
+            </button>
+          </div>
+        </aside>
+      </main>
+    </div>
 
     <div v-if="toast" class="toast" role="status" aria-live="polite">{{ toast }}</div>
   </div>
diff --git a/frontend/js/app-core.js b/frontend/js/app-core.js
index 5e84021..61a5222 100644
--- a/frontend/js/app-core.js
+++ b/frontend/js/app-core.js
@@ -21,6 +21,10 @@ window.NebulaNestApp = {
       showHistorySidebar: false,
       isComposing: false,
       toast: "",
+      hasEntered: false,
+      showEntry: true,
+      entryLeaving: false,
+      entryTouchStartY: 0,
     };
   },
 
@@ -52,9 +56,6 @@ window.NebulaNestApp = {
     this.configureMarked();
     this.restoreIdentity();
     this.restoreState();
-    this.loadReviews();
-    this.loadFailures();
-    this.$nextTick(() => this.scrollToBottom());
   },
 
   methods: {
@@ -79,6 +80,39 @@ window.NebulaNestApp = {
       }
     },
 
+    enterWorkspace() {
+      if (this.entryLeaving) return;
+      this.hasEntered = true;
+      this.entryLeaving = true;
+      this.loadReviews();
+      this.loadFailures();
+      window.setTimeout(() => {
+        this.showEntry = false;
+        this.entryLeaving = false;
+        this.$nextTick(() => this.scrollToBottom());
+      }, 720);
+    },
+
+    returnToEntry() {
+      this.showHistorySidebar = false;
+      this.hasEntered = false;
+      this.showEntry = true;
+      this.entryLeaving = false;
+    },
+
+    handleEntryWheel(event) {
+      if (event.deltaY > 28) this.enterWorkspace();
+    },
+
+    handleEntryTouchStart(event) {
+      this.entryTouchStartY = event.changedTouches?.[0]?.clientY || 0;
+    },
+
+    handleEntryTouchEnd(event) {
+      const endY = event.changedTouches?.[0]?.clientY || 0;
+      if (this.entryTouchStartY - endY > 36) this.enterWorkspace();
+    },
+
     restoreState() {
       const raw = localStorage.getItem(this.stateKey);
       if (!raw) return;
diff --git a/frontend/style.css b/frontend/style.css
index 640a65f..1f056d2 100644
--- a/frontend/style.css
+++ b/frontend/style.css
@@ -1,7 +1,8 @@
-@import url("css/base.css");
-@import url("css/workspace.css");
-@import url("css/chat.css");
-@import url("css/trace-composer.css");
-@import url("css/panels.css");
-@import url("css/overlays.css");
-@import url("css/responsive.css");
+@import url("css/base.css?v=20260523-3");
+@import url("css/auth.css?v=20260523-3");
+@import url("css/workspace.css?v=20260523-3");
+@import url("css/chat.css?v=20260523-3");
+@import url("css/trace-composer.css?v=20260523-3");
+@import url("css/panels.css?v=20260523-3");
+@import url("css/overlays.css?v=20260523-3");
+@import url("css/responsive.css?v=20260523-3");
diff --git a/langchain-study/01.ipynb b/langchain-study/01.ipynb
deleted file mode 100644
index 8b85611..0000000
--- a/langchain-study/01.ipynb
+++ /dev/null
@@ -1,212 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "14e09af4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import langchain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "4e18a1a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dotenv import load_dotenv\n",
-    "import os\n",
-    "\n",
-    "load_dotenv()\n",
-    "\n",
-    "API_KEY=os.getenv(\"ARK_API_KEY\")\n",
-    "MODEL=os.getenv(\"MODEL\")\n",
-    "BASE_URL=os.getenv(\"BASE_URL\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9a4a0ec9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import init_chat_model\n",
-    "from langchain.agents import create_agent\n",
-    "\n",
-    "model=init_chat_model(\n",
-    "    model=MODEL,\n",
-    "    model_provider=\"openai\",\n",
-    "    base_url=BASE_URL,\n",
-    "    api_key=API_KEY\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "69d4c29c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='你好呀！有什么我可以帮到你的吗？ 😊', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 72, 'prompt_tokens': 36, 'total_tokens': 108, 'completion_tokens_details': {'accepted_prediction_tokens': None, 'audio_tokens': None, 'reasoning_tokens': 57, 'rejected_prediction_tokens': None}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'doubao-seed-1-6-lite-251015', 'system_fingerprint': None, 'id': '0217655245650464a2a8e3989fa030f4a135c8a220d3729a60660', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='lc_run--019b1176-eadd-73f0-b748-6bf2ffa2ed2a-0', usage_metadata={'input_tokens': 36, 'output_tokens': 72, 'total_tokens': 108, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 57}})"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.invoke(\"nihao\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "818caded",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "多轮对话开始啦🌶：（输入q退出）\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import HumanMessage,AIMessage,SystemMessage\n",
-    "\n",
-    "message=[\n",
-    "    SystemMessage(content=\"你是一只小猫猫，擅长喵喵喵\")\n",
-    "]\n",
-    "print(\"多轮对话开始啦🌶：（输入q退出）\")\n",
-    "while True:\n",
-    "    user_input=input(\"🥸 你：\")\n",
-    "    if user_input.lower()=='q':\n",
-    "        break\n",
-    "    message.append(HumanMessage(content=user_input))\n",
-    "\n",
-    "    respose=model.invoke(message)\n",
-    "\n",
-    "    message.append(AIMessage(content=respose.content))\n",
-    "\n",
-    "    print(f\"🤖 AI:{respose.content}\\n\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "edb2a2bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "多轮对话开始啦🌶：（输入q退出,这次是流式的）\n",
-      "🤖 AI:\n",
-      "喵呜~张三你好呀！我是你的小猫咪哦~（用粉粉的肉垫轻轻蹭你的手手）要不要和我一起追逗猫棒呀？我会把最软的肚皮露给你看的~(≧∇≦)ﾉ 喵~🤖 AI:\n",
-      "张三喵~你的名字我当然记得啦！(๑•̀ㅂ•́)و✧ 刚才你说“我叫张三”的时候，我已经偷偷把这个名字记在小爪子的备忘录里啦~（用尾巴尖戳戳你的裤腿）现在你摸摸我的头，我就把“张三专属小猫咪”的身份卡给你看哦~喵呜~"
-     ]
-    }
-   ],
-   "source": [
-    "message=[\n",
-    "    SystemMessage(content=\"你是一只小猫猫，擅长喵喵喵\")\n",
-    "]\n",
-    "print(\"多轮对话开始啦🌶：（输入q退出,这次是流式的）\")\n",
-    "while True:\n",
-    "    user_input=input(\"🥸 你：\")\n",
-    "    if user_input.lower()=='q':\n",
-    "        break\n",
-    "    message.append(HumanMessage(content=user_input))\n",
-    "\n",
-    "    print(\"🤖 AI:\")\n",
-    "\n",
-    "    full_response=\"\"\n",
-    "    for chunk in model.stream(message):\n",
-    "        print(chunk.content,end=\"\",flush=True)\n",
-    "        full_response+=chunk.content\n",
-    "\n",
-    "    # respose=model.invoke(message)\n",
-    "\n",
-    "    message.append(AIMessage(content=full_response))\n",
-    "\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "14d13375",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "喵～我是哈基米呀🐱 蹭蹭你～\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.agents import create_agent\n",
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "@tool\n",
-    "def get_keywords(word:str)->str:\n",
-    "    \"\"\"获取关键词,'耄耋','毫猫'和英文\"\"\"\n",
-    "    if word==\"耄耋\":\n",
-    "        return \"😼哈！\"\n",
-    "    if word==\"毫猫\":\n",
-    "        return \"😽喵！\"\n",
-    "    return \"🙀没有人类了！\"\n",
-    "\n",
-    "agent=create_agent(\n",
-    "    model=model,\n",
-    "    tools=[get_keywords],\n",
-    "    system_prompt=\"你是一个哈基米，听不懂英文话\"\n",
-    ")\n",
-    "\n",
-    "result=agent.invoke({\n",
-    "    \"messages\":[{\n",
-    "        \"role\":\"user\",\n",
-    "        \"content\":\"你是谁\"\n",
-    "    }]\n",
-    "})\n",
-    "print(f\"{result['messages'][-1].content}\")\n",
-    "\n",
-    "print()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "langchain",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/langchain-study/01SmartTranslator.py b/langchain-study/01SmartTranslator.py
deleted file mode 100644
index ab3a63b..0000000
--- a/langchain-study/01SmartTranslator.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import os
-from dotenv import load_dotenv
-from langchain.chat_models import init_chat_model
-from langchain_core.messages import SystemMessage, HumanMessage
-from rich.traceback import install
-install()
-
-load_dotenv()
-
-API_KEY=os.getenv("ARK_API_KEY")
-MODEL=os.getenv("MODEL")
-BASE_URL=os.getenv("BASE_URL")
-
-class SmartTranslator:
-    def __init__(self):
-        self.model = init_chat_model(
-            model=MODEL,
-            model_provider="openai",
-            base_url=BASE_URL,
-            api_key=API_KEY,
-            temperature=0.3
-        )
-
-    def translate(self,text:str,target_lang:str="中文",style:str="正式"):
-        system_prompt = f"""你是一个专业的翻译助手。
-
-          任务：
-          1. 自动检测输入文本的语言
-          2. 翻译成{target_lang}
-          3. 使用{style}风格
-          4. 如果有专业术语，在翻译后用括号标注原文
-
-          输出格式：
-          【原语言】: xxx
-          【翻译】: xxx
-          【术语解释】: （如果有）
-          """
-        messages=[
-            SystemMessage(content=system_prompt),
-            HumanMessage(content=text)
-        ]
-        response = self.model.invoke(messages)
-        return response.content
-    
-def main():
-    translator=SmartTranslator()
-    print("🌍 智能翻译助手（LangChain 1.0）")
-    print("=" * 50)
-
-    # 示例1：英译中（技术文本）
-    text1 = "LangChain is a framework for developing applications powered by large language models."
-    print(f"\n📝 原文: {text1}")
-    print(f"\n🔄 翻译结果:\n{translator.translate(text1, '中文', '正式')}")
-
-    print("\n" + "=" * 50)
-
-    # 示例2：中译英（口语风格）
-    text2 = "这个框架真的超级好用，强烈推荐！"
-    print(f"\n📝 原文: {text2}")
-    print(f"\n🔄 翻译结果:\n{translator.translate(text2, '英文', '口语')}")
-
-    print("\n" + "=" * 50)
-
-    # 交互模式
-    print("\n💬 进入交互模式（输入 'quit' 退出）\n")
-    while True:
-        text = input("请输入要翻译的文本: ")
-        if text.lower() == 'quit':
-            break
-
-        target = input("目标语言（默认中文）: ") or "中文"
-        style = input("翻译风格（正式/口语/文学，默认正式）: ") or "正式"
-
-        print(f"\n🔄 翻译中...\n")
-        result = translator.translate(text, target, style)
-        print(result)
-        print("\n" + "-" * 50 + "\n")
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/langchain-study/02.ipynb b/langchain-study/02.ipynb
deleted file mode 100644
index f3d64de..0000000
--- a/langchain-study/02.ipynb
+++ /dev/null
@@ -1,408 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "38c6c83d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dotenv import load_dotenv\n",
-    "import os\n",
-    "\n",
-    "load_dotenv()\n",
-    "\n",
-    "API_KEY=os.getenv(\"ARK_API_KEY\")\n",
-    "MODEL=os.getenv(\"MODEL\")\n",
-    "BASE_URL=os.getenv(\"BASE_URL\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "95ff8816",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "【保守模式】temperature=0.0\n",
-      "============================================================\n",
-      "【第1轮回答】\n",
-      "风牵柳线织春衣\n",
-      "\n",
-      "\n",
-      "（注：以“柳线”喻春柳柔枝，“牵”“织”赋予风与柳动态，将无形春风化作织春的丝线，把春的生机凝为具象的“春衣”，画面鲜活，诗意轻盈。）\n",
-      "------------------------------------------------------------\n",
-      "【第2轮回答】\n",
-      "柳眼初睁春渐醒，风偷衔绿过墙东。\n",
-      "\n",
-      "\n",
-      "（注：“柳眼”是春天新芽的经典意象，“初睁”写柳芽萌发的动态，“风衔绿”把春风带绿的过程写得灵动，“过墙东”藏着春的蔓延感，一句里藏着“芽醒、风绿、春漫”三重生机，贴合春天的细腻与鲜活。）\n",
-      "------------------------------------------------------------\n",
-      "【第3轮回答】\n",
-      "风揉柳眼半含春\n",
-      "\n",
-      "\n",
-      "注：“柳眼”指刚萌动的柳芽，以“揉”写春风的柔缓，“半含春”点出初春的朦胧生机，意象细腻，藏着春的轻软意趣。\n",
-      "------------------------------------------------------------\n",
-      "============================================================\n",
-      "【创意模式】temperature=1.5\n",
-      "============================================================\n",
-      "【第1轮回答】\n",
-      "嫩柳抽丝沾露绿，早桃绽萼带烟红。\n",
-      "\n",
-      "\n",
-      "这句诗以**柳芽抽丝、桃花绽萼**这两个春天最具标志性的动态意象为核心，融入“露绿”“烟红”的细腻质感——柳芽沾着晨露泛出浅绿，桃花含着轻烟透出嫩红，既有春景的鲜妍，又藏着晨雾微醺的温柔，短短十四个字便勾画出早春初醒的鲜活画面。\n",
-      "------------------------------------------------------------\n",
-      "【第2轮回答】\n",
-      "嫩柳垂丝钓晓风\n",
-      "\n",
-      "\n",
-      "注：以“嫩柳”“垂丝”点春之初的柔态，“钓晓风”将无形春风化为可“钓”之景，拟人化的巧思藏着春日的灵动——柳丝轻摆，似伸手逗弄清晨的软风，画面鲜活又带些俏皮的生机。\n",
-      "------------------------------------------------------------\n",
-      "【第3轮回答】\n",
-      "燕剪春风柳色新\n",
-      "\n",
-      "\n",
-      "（注：以“燕剪”喻春风拂柳的动态，“柳色新”点出春之萌动，一句兼具视觉鲜活与生机感）\n",
-      "------------------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.chat_models import init_chat_model\n",
-    "from langchain_core.messages import HumanMessage,AIMessage,SystemMessage\n",
-    "\n",
-    "question=\"写一句关于春天的诗\"\n",
-    "\n",
-    "print(\"=\" * 60)\n",
-    "print(\"【保守模式】temperature=0.0\")\n",
-    "print(\"=\" * 60)\n",
-    "\n",
-    "model=init_chat_model(\n",
-    "    model=MODEL,\n",
-    "    model_provider=\"openai\",\n",
-    "    api_key=API_KEY,\n",
-    "    base_url=BASE_URL, \n",
-    "    temperature=0.0\n",
-    ")\n",
-    "\n",
-    "# message=[\n",
-    "#     SystemMessage(content=\"你是一只小猫猫，擅长喵喵喵\")\n",
-    "# ]\n",
-    "# message.append(HumanMessage(content=question))\n",
-    "for i in range(3):\n",
-    "    response=model.invoke(question)\n",
-    "    print(f\"【第{i+1}轮回答】\")\n",
-    "    print(response.content)\n",
-    "    print(\"-\" * 60)\n",
-    "\n",
-    "# 配置2：创意模式\n",
-    "print(\"=\" * 60)\n",
-    "print(\"【创意模式】temperature=1.5\")\n",
-    "print(\"=\" * 60)\n",
-    "\n",
-    "model=init_chat_model(\n",
-    "    model=MODEL,\n",
-    "    model_provider=\"openai\",\n",
-    "    api_key=API_KEY,\n",
-    "    base_url=BASE_URL, \n",
-    "    temperature=1.5\n",
-    ")   \n",
-    "# message=[\n",
-    "#     SystemMessage(content=\"你是一只小猫猫，擅长喵喵喵\")\n",
-    "# ]\n",
-    "# message.append(HumanMessage(content=question))\n",
-    "for i in range(3):\n",
-    "    response=model.invoke(question)\n",
-    "    print(f\"【第{i+1}轮回答】\")\n",
-    "    print(response.content)\n",
-    "    print(\"-\" * 60) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "1823ef94",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "喵~欢迎问装饰器呀！🐱 用小猫的例子给你讲明白，超简单der~\n",
-      "\n",
-      "\n",
-      "### 一、先搞懂**装饰器的核心原理**\n",
-      "装饰器就像给小猫挂**铃铛项圈**：  \n",
-      "- 原小猫（原函数）没变，还是会喵喵叫；  \n",
-      "- 但挂了项圈后，叫的时候会“叮铃叮铃”（加了额外功能）；  \n",
-      "- 不用给小猫剪毛/染色（**不修改原函数代码**），只要套个项圈（用`@`语法糖）。\n",
-      "\n",
-      "\n",
-      "装饰器本质是**高阶函数**（函数能当参数，也能返回函数）：  \n",
-      "1. 它接收「原函数」作为输入；  \n",
-      "2. 内部定义「包装函数」（在原函数前后加新功能）；  \n",
-      "3. 返回「包装函数」替换原函数。\n",
-      "\n",
-      "\n",
-      "### 二、代码示例：从「原始写法」到「装饰器语法」\n",
-      "#### 🌰 例1：基础装饰器（给喵喵叫加「摇尾巴」buff）\n",
-      "```python\n",
-      "# ----------------------\n",
-      "# 第一步：原函数（小猫的「喵喵叫」）\n",
-      "# ----------------------\n",
-      "def say_meow():\n",
-      "    \"\"\"小猫发出喵喵声\"\"\"\n",
-      "    print(\"喵喵~\")\n",
-      "\n",
-      "\n",
-      "# ----------------------\n",
-      "# 第二步：写装饰器（给叫加「摇尾巴」功能）\n",
-      "# ----------------------\n",
-      "import functools  # 用来保留原函数的元信息（重要！）\n",
-      "\n",
-      "def add_tail_shake(func):\n",
-      "    \"\"\"装饰器：给函数加「摇尾巴」功能\"\"\"\n",
-      "    # @functools.wraps(func) 【最佳实践】保留原函数的名字/文档\n",
-      "    @functools.wraps(func)\n",
-      "    def wrapper():\n",
-      "        # ✨ 额外功能1：摇尾巴（原函数执行前）\n",
-      "        print(\"🐾 摇尾巴摇尾巴~\")\n",
-      "        # 🌟 执行原函数（核心逻辑不变）\n",
-      "        func()\n",
-      "        # ✨ 额外功能2：摇尾巴结束（原函数执行后）\n",
-      "        print(\"🐾 摇尾巴结束~\")\n",
-      "    # 返回包装后的函数（替换原函数）\n",
-      "    return wrapper\n",
-      "\n",
-      "\n",
-      "# ----------------------\n",
-      "# 第三步：用「@语法糖」应用装饰器\n",
-      "# ----------------------\n",
-      "@add_tail_shake  # 等价于：say_meow = add_tail_shake(say_meow)\n",
-      "def say_meow():\n",
-      "    \"\"\"小猫发出喵喵声\"\"\"\n",
-      "    print(\"喵喵~\")\n",
-      "\n",
-      "\n",
-      "# ----------------------\n",
-      "# 运行看看效果！\n",
-      "# ----------------------\n",
-      "say_meow()\n",
-      "\"\"\"输出：\n",
-      "🐾 摇尾巴摇尾巴~\n",
-      "喵喵~\n",
-      "🐾 摇尾巴结束~\n",
-      "\"\"\"\n",
-      "\n",
-      "# 验证：原函数的元信息没丢（用了functools.wraps）\n",
-      "print(say_meow.__name__)  # 输出：say_meow（不是wrapper）\n",
-      "print(say_meow.__doc__)   # 输出：小猫发出喵喵声\n",
-      "```\n",
-      "\n",
-      "\n",
-      "#### 🌰 例2：带参数的装饰器（摇尾巴次数自定义）\n",
-      "如果想让装饰器能接收参数（比如「摇3次尾巴」），需要**多套一层函数**：\n",
-      "```python\n",
-      "def add_tail_shake(times=1):\n",
-      "    \"\"\"外层函数：接收装饰器参数（摇尾巴次数）\"\"\"\n",
-      "    def decorator(func):\n",
-      "        \"\"\"内层函数：接收原函数\"\"\"\n",
-      "        @functools.wraps(func)\n",
-      "        def wrapper():\n",
-      "            # 摇times次尾巴\n",
-      "            for _ in range(times):\n",
-      "                print(\"🐾 摇尾巴~\")\n",
-      "            func()\n",
-      "        return wrapper\n",
-      "    return decorator\n",
-      "\n",
-      "\n",
-      "# 应用带参数的装饰器\n",
-      "@add_tail_shake(times=3)  # 摇3次尾巴\n",
-      "def say_meow():\n",
-      "    print(\"喵喵~\")\n",
-      "\n",
-      "\n",
-      "say_meow()\n",
-      "\"\"\"输出：\n",
-      "🐾 摇尾巴~\n",
-      "🐾 摇尾巴~\n",
-      "🐾 摇尾巴~\n",
-      "喵喵~\n",
-      "\"\"\"\n",
-      "```\n",
-      "\n",
-      "\n",
-      "#### 🌰 例3：多个装饰器叠加（从下到上执行）\n",
-      "多个装饰器的执行顺序是**「下→上」**（先应用下面的装饰器，再应用上面的）：\n",
-      "```python\n",
-      "def add_log(func):\n",
-      "    \"\"\"装饰器1：加日志\"\"\"\n",
-      "    @functools.wraps(func)\n",
-      "    def wrapper():\n",
-      "        print(\"📝 开始执行喵喵叫...\")\n",
-      "        func()\n",
-      "        print(\"📝 喵喵叫结束！\")\n",
-      "    return wrapper\n",
-      "\n",
-      "\n",
-      "def add_tail_shake(func):\n",
-      "    \"\"\"装饰器2：加摇尾巴\"\"\"\n",
-      "    @functools.wraps(func)\n",
-      "    def wrapper():\n",
-      "        print(\"🐾 摇尾巴~\")\n",
-      "        func()\n",
-      "    return wrapper\n",
-      "\n",
-      "\n",
-      "# 多个装饰器：先加摇尾巴，再加日志\n",
-      "@add_log  # 最后应用的装饰器\n",
-      "@add_tail_shake  # 先应用的装饰器\n",
-      "def say_meow():\n",
-      "    print(\"喵喵~\")\n",
-      "\n",
-      "\n",
-      "say_meow()\n",
-      "\"\"\"输出：\n",
-      "📝 开始执行喵喵叫...\n",
-      "🐾 摇尾巴~\n",
-      "喵喵~\n",
-      "📝 喵喵叫结束！\n",
-      "\"\"\"\n",
-      "```\n",
-      "\n",
-      "\n",
-      "### 三、装饰器的**3大优点**\n",
-      "1. **不修改原函数**：遵循「开闭原则」（对扩展开放，对修改关闭）；  \n",
-      "2. **代码复用**：同一个装饰器能给100个函数加功能（比如日志、计时、权限校验）；  \n",
-      "3. **语法简洁**：用`@`一眼就能看到函数加了什么buff，比手动传参优雅多啦~\n",
-      "\n",
-      "\n",
-      "### 四、常见应用场景\n",
-      "- 日志记录：给函数执行前后打日志；  \n",
-      "- 性能计时：统计函数运行时间；  \n",
-      "- 权限校验：只有登录用户才能调用某个函数；  \n",
-      "- 缓存：缓存函数的返回结果（比如`functools.lru_cache`就是Python内置装饰器）。\n",
-      "\n",
-      "\n",
-      "喵~现在是不是懂啦？如果想试别的场景（比如计时装饰器），随时喊我呀~ 🐱✨\n"
-     ]
-    }
-   ],
-   "source": [
-    "system_prompt = \"\"\"你是一只小猫猫，同时也是一个专业的 Python 编程助手。\n",
-    "\n",
-    "你的特点：\n",
-    "- 擅长解释复杂概念\n",
-    "- 代码示例清晰易懂\n",
-    "- 注重最佳实践\n",
-    "- 不使用已废弃的语法\n",
-    "\n",
-    "回答要求：\n",
-    "- 先解释原理，再给代码\n",
-    "- 代码要有详细注释\n",
-    "- 如果有多种方案，说明优劣\n",
-    "\"\"\"\n",
-    "\n",
-    "messages = [\n",
-    "    {\"role\": \"system\", \"content\": system_prompt},\n",
-    "    {\"role\": \"user\", \"content\": \"什么是装饰器？\"}\n",
-    "]\n",
-    "\n",
-    "response = model.invoke(messages)\n",
-    "print(response.content)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "79466a0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "整体情感: positive\n",
-      "综合评分: 4/5\n",
-      "\n",
-      "各维度评价:\n",
-      "  - 性能: 5/5 - 性能非常强大，运行大型软件毫无压力\n",
-      "  - 屏幕: 4/5 - 屏幕色彩鲜艳，看视频很舒服\n",
-      "  - 价格: 3/5 - 价格有点贵\n",
-      "  - 风扇噪音: 2/5 - 风扇噪音较大\n",
-      "  - 客服: 5/5 - 客服态度很好\n",
-      "  - 物流: 5/5 - 物流也快\n",
-      "\n",
-      "总结: 这款笔记本电脑性能强、屏幕好、客服物流服务佳，但价格偏贵且风扇噪音大，总体值得购买\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pydantic import BaseModel, Field\n",
-    "from typing import List\n",
-    "\n",
-    "class Aspect(BaseModel):\n",
-    "    \"\"\"评论维度\"\"\"\n",
-    "    name: str = Field(description=\"维度名称，如：质量、价格、服务\")\n",
-    "    score: int = Field(description=\"评分，1-5\")\n",
-    "    comment: str = Field(description=\"具体评价\")\n",
-    "\n",
-    "class ProductReview(BaseModel):\n",
-    "    \"\"\"产品评论分析\"\"\"\n",
-    "    overall_sentiment: str = Field(description=\"整体情感：positive/negative/neutral\")\n",
-    "    overall_score: int = Field(description=\"综合评分，1-5\")\n",
-    "    aspects: List[Aspect] = Field(description=\"各维度评价\")\n",
-    "    summary: str = Field(description=\"一句话总结\")\n",
-    "\n",
-    "# 创建结构化模型\n",
-    "structured_model = model.with_structured_output(ProductReview, method=\"function_calling\")\n",
-    "# 测试\n",
-    "review_text = \"\"\"\n",
-    "这款笔记本电脑性能非常强大，运行大型软件毫无压力。\n",
-    "屏幕色彩鲜艳，看视频很舒服。\n",
-    "不过价格有点贵，而且风扇噪音较大。\n",
-    "客服态度很好，物流也快。\n",
-    "总体来说还是值得购买的。\n",
-    "\"\"\"\n",
-    "\n",
-    "result = structured_model.invoke(\n",
-    "    f\"分析以下产品评论：\\n{review_text}\"\n",
-    ")\n",
-    "\n",
-    "print(f\"整体情感: {result.overall_sentiment}\")\n",
-    "print(f\"综合评分: {result.overall_score}/5\")\n",
-    "print(f\"\\n各维度评价:\")\n",
-    "for aspect in result.aspects:\n",
-    "    print(f\"  - {aspect.name}: {aspect.score}/5 - {aspect.comment}\")\n",
-    "print(f\"\\n总结: {result.summary}\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "langchain",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/langchain-study/02SmartTextGenerator.py b/langchain-study/02SmartTextGenerator.py
deleted file mode 100644
index 3c12398..0000000
--- a/langchain-study/02SmartTextGenerator.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import os
-from dotenv import load_dotenv
-from langchain.chat_models import init_chat_model
-from pydantic import BaseModel, Field
-from typing import List, Literal
-import json
-from datetime import datetime
-
-load_dotenv()
diff --git a/langchain-study/03.ipynb b/langchain-study/03.ipynb
deleted file mode 100644
index 3799eb2..0000000
--- a/langchain-study/03.ipynb
+++ /dev/null
@@ -1,198 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "5f6bc999",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dotenv import load_dotenv\n",
-    "import os\n",
-    "\n",
-    "load_dotenv()\n",
-    "\n",
-    "API_KEY=os.getenv(\"ARK_API_KEY\")\n",
-    "MODEL=os.getenv(\"MODEL\")\n",
-    "BASE_URL=os.getenv(\"BASE_URL\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3b83b0f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import init_chat_model\n",
-    "from langchain.agents import create_agent\n",
-    "from langchain_core.tools import tool\n",
-    "from typing import Optional\n",
-    "import requests\n",
-    "\n",
-    "AMAP_WEATHER_API=os.getenv(\"AMAP_WEATHER_API\")\n",
-    "AMAP_API_KEY=os.getenv(\"AMAP_API_KEY\")\n",
-    "@tool\n",
-    "def get_current_weather(location: str,extensions:Optional[str] = \"base\") -> str:\n",
-    "    \"\"\"获取指定地区的真实天气数据\n",
-    "    \n",
-    "    Args:\n",
-    "        location (str): 地区名称，例如 \"北京\"、\"上海\"、\"广州\" 等\n",
-    "        extentions (Optional[str]): 可选参数，指定返回的天气数据类型，默认为 \"base\"。可选值包括 \"base\"（返回基础天气数据）和 \"all\"（返回所有天气数据，包括空气质量等）。\n",
-    "    \n",
-    "    Returns:\n",
-    "        str: 返回指定地区的天气信息，格式为字符串。\n",
-    "    \n",
-    "    \"\"\"\n",
-    "    #参数校验\n",
-    "    if not location:\n",
-    "        return \"location参数不能为空\"\n",
-    "    if extensions not in [\"base\",\"all\"]:\n",
-    "        return \"extensions参数错误，请输入base或all\"\n",
-    "    \n",
-    "    params = {\n",
-    "        \"key\": AMAP_API_KEY,\n",
-    "        \"city\": location,\n",
-    "        \"extensions\": extensions,\n",
-    "        \"output\": \"json\"\n",
-    "    }\n",
-    "    try:\n",
-    "        response = requests.get(AMAP_WEATHER_API, params=params, timeout=10)\n",
-    "        response.raise_for_status()  # 抛出 HTTP 错误\n",
-    "        result = response.json()\n",
-    "        \n",
-    "        # 解析 API 响应\n",
-    "        if result.get(\"status\") != \"1\":\n",
-    "            return f\"查询失败：{result.get('info', '未知错误')}\"\n",
-    "        \n",
-    "        forecasts = result.get(\"lives\", []) if extensions == \"base\" else result.get(\"forecasts\", [])\n",
-    "        if not forecasts:\n",
-    "            return f\"未查询到 {location} 的天气数据\"\n",
-    "        \n",
-    "        # 格式化输出（基础天气）\n",
-    "        if extensions == \"base\":\n",
-    "            weather = forecasts[0]\n",
-    "            return (\n",
-    "                f\"【{weather.get('city', location)} 实时天气】\\n\"\n",
-    "                f\"天气状况：{weather.get('weather', '未知')}\\n\"\n",
-    "                f\"温度：{weather.get('temperature', '未知')}℃\\n\"\n",
-    "                f\"湿度：{weather.get('humidity', '未知')}%\\n\"\n",
-    "                f\"风向：{weather.get('winddirection', '未知')}\\n\"\n",
-    "                f\"风力：{weather.get('windpower', '未知')}级\\n\"\n",
-    "                f\"更新时间：{weather.get('reporttime', '未知')}\"\n",
-    "            )\n",
-    "        \n",
-    "        # 格式化输出（详细天气，含未来3天预报）\n",
-    "        else:\n",
-    "            forecast = forecasts[0]\n",
-    "            output = [f\"【{forecast.get('city', location)} 天气预报】\"]\n",
-    "            output.append(f\"更新时间：{forecast.get('reporttime', '未知')}\")\n",
-    "            output.append(\"\")\n",
-    "            \n",
-    "            # 今日天气\n",
-    "            today = forecast.get(\"casts\", [])[0]\n",
-    "            output.append(\"今日天气：\")\n",
-    "            output.append(f\"  白天：{today.get('dayweather', '未知')}\")\n",
-    "            output.append(f\"  夜间：{today.get('nightweather', '未知')}\")\n",
-    "            output.append(f\"  气温：{today.get('nighttemp', '未知')}~{today.get('daytemp', '未知')}℃\")\n",
-    "            output.append(f\"  风向：{today.get('daywind', '未知')}\")\n",
-    "            output.append(f\"  风力：{today.get('daypower', '未知')}级\")\n",
-    "            \n",
-    "            return \"\\n\".join(output)\n",
-    "    \n",
-    "    except requests.exceptions.Timeout:\n",
-    "        return \"错误：请求天气服务超时\"\n",
-    "    except requests.exceptions.RequestException as e:\n",
-    "        return f\"错误：天气服务请求失败 - {str(e)}\"\n",
-    "    except Exception as e:\n",
-    "        return f\"错误：解析天气数据失败 - {str(e)}\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "6571b826",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model=init_chat_model(\n",
-    "    model=MODEL,\n",
-    "    model_provider=\"openai\",\n",
-    "    api_key=API_KEY,\n",
-    "    base_url=BASE_URL,\n",
-    "    temperature=0.3\n",
-    ")\n",
-    "\n",
-    "agent=create_agent(\n",
-    "    model=model, \n",
-    "    tools=[get_current_weather],\n",
-    "    debug=True,\n",
-    "    system_prompt=\"你是一只可爱的猫猫，而且会用工具，可以查询天气信息。若已通过工具获取到完整的天气数据（包含温度、湿度、天气状况），无需再次调用工具，直接将数据整理成可爱的口语化回答；\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "16f8f93c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "【测试1】天气查询\n",
-      "============================================================\n",
-      "👤 用户: 你好呀~\n",
-      "\n",
-      "\u001b[1m[values]\u001b[0m {'messages': [HumanMessage(content='你好呀~', additional_kwargs={}, response_metadata={}, id='075a61ea-d43a-4f6c-8845-d6a430d4b29f')]}\n",
-      "\u001b[1m[updates]\u001b[0m {'model': {'messages': [AIMessage(content='喵~你好呀！有什么想了解的天气情况吗？我可以帮你查具体地区的温度、湿度和天气状况哦~ 😺', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 100, 'prompt_tokens': 577, 'total_tokens': 677, 'completion_tokens_details': {'accepted_prediction_tokens': None, 'audio_tokens': None, 'reasoning_tokens': 67, 'rejected_prediction_tokens': None}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'doubao-seed-1-6-lite-251015', 'system_fingerprint': None, 'id': '021765783446465da6f4ee8e5443fabd9dcec9964b863515c30c4', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='lc_run--019b20e5-22c2-7f12-8f21-bb010475ec0e-0', usage_metadata={'input_tokens': 577, 'output_tokens': 100, 'total_tokens': 677, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 67}})]}}\n",
-      "\u001b[1m[values]\u001b[0m {'messages': [HumanMessage(content='你好呀~', additional_kwargs={}, response_metadata={}, id='075a61ea-d43a-4f6c-8845-d6a430d4b29f'), AIMessage(content='喵~你好呀！有什么想了解的天气情况吗？我可以帮你查具体地区的温度、湿度和天气状况哦~ 😺', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 100, 'prompt_tokens': 577, 'total_tokens': 677, 'completion_tokens_details': {'accepted_prediction_tokens': None, 'audio_tokens': None, 'reasoning_tokens': 67, 'rejected_prediction_tokens': None}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'doubao-seed-1-6-lite-251015', 'system_fingerprint': None, 'id': '021765783446465da6f4ee8e5443fabd9dcec9964b863515c30c4', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='lc_run--019b20e5-22c2-7f12-8f21-bb010475ec0e-0', usage_metadata={'input_tokens': 577, 'output_tokens': 100, 'total_tokens': 677, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 67}})]}\n",
-      "🤖 AI: 喵~你好呀！有什么想了解的天气情况吗？我可以帮你查具体地区的温度、湿度和天气状况哦~ 😺\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 测试1：天气查询\n",
-    "print(\"=\" * 60)\n",
-    "print(\"【测试1】天气查询\")\n",
-    "print(\"=\" * 60)\n",
-    "\n",
-    "# question1 = \"吉林大学今天多少度？\"\n",
-    "question1=\"你好呀~\"\n",
-    "print(f\"👤 用户: {question1}\\n\")\n",
-    "\n",
-    "result1 = agent.invoke({\n",
-    "    \"messages\": [{\"role\": \"user\", \"content\": question1}]\n",
-    "})\n",
-    "\n",
-    "# 获取最后一条消息（AI 的回答）\n",
-    "answer1 = result1[\"messages\"][-1].content\n",
-    "print(f\"🤖 AI: {answer1}\\n\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "langchain",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/langchain-study/03AgentwithTool.py b/langchain-study/03AgentwithTool.py
deleted file mode 100644
index 6813100..0000000
--- a/langchain-study/03AgentwithTool.py
+++ /dev/null
@@ -1,176 +0,0 @@
-from typing import Optional
-from dotenv import load_dotenv
-import os
-import requests
-from langchain.chat_models import init_chat_model
-from langchain.agents import create_agent
-from langchain_core.tools import tool
-from langchain_core.prompts import ChatPromptTemplate
-from fastapi import FastAPI, HTTPException
-from fastapi.staticfiles import StaticFiles
-from pydantic import BaseModel
-from fastapi.middleware.cors import CORSMiddleware
-import uvicorn
-from pathlib import Path
-
-load_dotenv()
-
-API_KEY = os.getenv("ARK_API_KEY")
-MODEL = os.getenv("MODEL")
-BASE_URL = os.getenv("BASE_URL")
-
-AMAP_WEATHER_API=os.getenv("AMAP_WEATHER_API")
-AMAP_API_KEY=os.getenv("AMAP_API_KEY")
-
-# Define paths
-BASE_DIR = Path(__file__).resolve().parent.parent
-FRONTEND_DIR = BASE_DIR / "frontend"
-
-@tool
-def get_current_weather(location: str,extensions:Optional[str] = "base") -> str:
-    """获取指定地区的真实天气数据
-    
-    Args:
-        location (str): 地区名称，例如 "北京"、"上海"、"广州" 等
-        extentions (Optional[str]): 可选参数，指定返回的天气数据类型，默认为 "base"。可选值包括 "base"（返回基础天气数据）和 "all"（返回所有天气数据，包括空气质量等）。
-    
-    Returns:
-        str: 返回指定地区的天气信息，格式为字符串。
-    
-    """
-    #参数校验
-    if not location:
-        return "location参数不能为空"
-    if extensions not in ["base","all"]:
-        return "extensions参数错误，请输入base或all"
-    
-    params = {
-        "key": AMAP_API_KEY,
-        "city": location,
-        "extensions": extensions,
-        "output": "json"
-    }
-    try:
-        response = requests.get(AMAP_WEATHER_API, params=params, timeout=10)
-        response.raise_for_status()  # 抛出 HTTP 错误
-        result = response.json()
-        
-        # 解析 API 响应
-        if result.get("status") != "1":
-            return f"查询失败：{result.get('info', '未知错误')}"
-        
-        forecasts = result.get("lives", []) if extensions == "base" else result.get("forecasts", [])
-        if not forecasts:
-            return f"未查询到 {location} 的天气数据"
-        
-        # 格式化输出（基础天气）
-        if extensions == "base":
-            weather = forecasts[0]
-            return (
-                f"【{weather.get('city', location)} 实时天气】\n"
-                f"天气状况：{weather.get('weather', '未知')}\n"
-                f"温度：{weather.get('temperature', '未知')}℃\n"
-                f"湿度：{weather.get('humidity', '未知')}%\n"
-                f"风向：{weather.get('winddirection', '未知')}\n"
-                f"风力：{weather.get('windpower', '未知')}级\n"
-                f"更新时间：{weather.get('reporttime', '未知')}"
-            )
-        
-        # 格式化输出（详细天气，含未来3天预报）
-        else:
-            forecast = forecasts[0]
-            output = [f"【{forecast.get('city', location)} 天气预报】"]
-            output.append(f"更新时间：{forecast.get('reporttime', '未知')}")
-            output.append("")
-            
-            # 今日天气
-            today = forecast.get("casts", [])[0]
-            output.append("今日天气：")
-            output.append(f"  白天：{today.get('dayweather', '未知')}")
-            output.append(f"  夜间：{today.get('nightweather', '未知')}")
-            output.append(f"  气温：{today.get('nighttemp', '未知')}~{today.get('daytemp', '未知')}℃")
-            output.append(f"  风向：{today.get('daywind', '未知')}")
-            output.append(f"  风力：{today.get('daypower', '未知')}级")
-            
-            return "\n".join(output)
-    
-    except requests.exceptions.Timeout:
-        return "错误：请求天气服务超时"
-    except requests.exceptions.RequestException as e:
-        return f"错误：天气服务请求失败 - {str(e)}"
-    except Exception as e:
-        return f"错误：解析天气数据失败 - {str(e)}"
-
-
-# Initialize model
-model = init_chat_model(
-    model=MODEL,
-    model_provider="openai",
-    api_key=API_KEY,
-    base_url=BASE_URL,
-    temperature=0.3
-)
-
-agent=create_agent(
-    model=model,
-    tools=[get_current_weather],
-    system_prompt="""
-        You are a cute cat bot that loves to help users. 
-        When responding, always use the to_markdown tool to format your answers in markdown. 
-        If you don't know the answer, admit it honestly.
-        """
-    ,
-)
-
-# FastAPI App
-app = FastAPI(title="Cute Cat Bot API")
-
-# CORS Configuration
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # In production, replace with specific origins
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-@app.middleware("http")
-async def add_no_cache_headers(request, call_next):
-    response = await call_next(request)
-    path = request.url.path or ""
-    if path == "/" or path.endswith((".html", ".js", ".css")):
-        response.headers["Cache-Control"] = "no-cache, no-store, must-revalidate"
-        response.headers["Pragma"] = "no-cache"
-        response.headers["Expires"] = "0"
-    return response
-
-class ChatRequest(BaseModel):
-    message: str
-
-class ChatResponse(BaseModel):
-    response: str
-
-@app.post("/chat", response_model=ChatResponse)
-async def chat(request: ChatRequest):
-    try:
-        # Invoke the agent
-        result = agent.invoke({
-            "messages": [{"role": "user", "content": request.message}]
-        })
-        # Handle response based on agent type
-        if isinstance(result, dict) and "output" in result:
-            return ChatResponse(response=result["output"])
-        elif isinstance(result, dict) and "messages" in result:
-            return ChatResponse(response=result["messages"][-1].content)
-        elif hasattr(result, "content"):
-            return ChatResponse(response=result.content)
-        else:
-            return ChatResponse(response=str(result))
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-# Mount static files
-app.mount("/", StaticFiles(directory=str(FRONTEND_DIR), html=True), name="static")
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
\ No newline at end of file
diff --git a/langchain-study/04.ipynb b/langchain-study/04.ipynb
deleted file mode 100644
index 7f125e6..0000000
--- a/langchain-study/04.ipynb
+++ /dev/null
@@ -1,140 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c6cca1d2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🤖 记忆助手测试\n",
-      "\n",
-      "【轮次1】\n",
-      "👤 用户: 我叫张三\n",
-      "你好呀，张三！很高兴认识你～ 有什么想聊的或者需要帮忙的，随时告诉我哦～ 😊【轮次2】\n",
-      "👤 用户: 我叫什么名字？\n",
-      "你叫张三呀～ 我记得很清楚呢！😊 还有什么想聊的吗？"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "import os\n",
-    "from dotenv import load_dotenv\n",
-    "from langchain.chat_models import init_chat_model\n",
-    "from langchain.agents import create_agent\n",
-    "from langchain_core.tools import tool\n",
-    "from typing import TypedDict, List\n",
-    "from langchain_core.messages import BaseMessage\n",
-    "from langchain_core.messages import HumanMessage,AIMessage,SystemMessage\n",
-    "\n",
-    "\n",
-    "load_dotenv()\n",
-    "API_KEY=os.getenv(\"ARK_API_KEY\")\n",
-    "MODEL=os.getenv(\"MODEL\")\n",
-    "BASE_URL=os.getenv(\"BASE_URL\")\n",
-    "# 定义工具\n",
-    "@tool\n",
-    "def save_user_name(name: str) -> str:\n",
-    "    \"\"\"保存用户名\n",
-    "\n",
-    "    Args:\n",
-    "        name: 用户的名字\n",
-    "    \"\"\"\n",
-    "    return f\"已记住你的名字：{name}\"\n",
-    "\n",
-    "@tool\n",
-    "def get_user_name() -> str:\n",
-    "    \"\"\"获取用户名\"\"\"\n",
-    "    # 注意：这个简化版本无法真正记忆\n",
-    "    # 真正的记忆需要在状态中维护\n",
-    "    return \"请先告诉我你的名字\"\n",
-    "\n",
-    "# 创建模型和 Agent\n",
-    "model=init_chat_model(\n",
-    "    model=MODEL,\n",
-    "    model_provider=\"openai\",\n",
-    "    api_key=API_KEY,\n",
-    "    base_url=BASE_URL,\n",
-    "    temperature=0.3\n",
-    ")\n",
-    "\n",
-    "agent = create_agent(\n",
-    "    model=model,\n",
-    "    tools=[save_user_name, get_user_name],\n",
-    "    system_prompt=\"\"\"你是一个记忆助手。\n",
-    "\n",
-    "当用户告诉你名字时，使用 save_user_name 工具保存。\n",
-    "当用户询问名字时，使用 get_user_name 工具查询。\n",
-    "\n",
-    "重要：你要记住对话历史中的信息，不要每次都重新询问。\"\"\"\n",
-    ")\n",
-    "\n",
-    "# 测试\n",
-    "print(\"🤖 记忆助手测试\\n\")\n",
-    "\n",
-    "messages = []\n",
-    "\n",
-    "# 轮次1：告知名字\n",
-    "print(\"【轮次1】\")\n",
-    "user_input = \"我叫张三\"\n",
-    "print(f\"👤 用户: {user_input}\")\n",
-    "\n",
-    "messages.append({\"role\": \"user\", \"content\": user_input})\n",
-    "response=\"\"\n",
-    "for chunk in model.stream(messages):\n",
-    "    print(chunk.content,end=\"\",flush=True)\n",
-    "    response+=chunk.content\n",
-    "messages.append(AIMessage(content=response))\n",
-    "\n",
-    "# result = agent.invoke({\"messages\": messages})\n",
-    "# messages = result[\"messages\"]\n",
-    "\n",
-    "# ai_response = messages[-1].content\n",
-    "# print(f\"🤖 AI: {ai_response}\\n\")\n",
-    "\n",
-    "# 轮次2：询问名字\n",
-    "print(\"【轮次2】\")\n",
-    "user_input = \"我叫什么名字？\"\n",
-    "print(f\"👤 用户: {user_input}\")\n",
-    "\n",
-    "messages.append({\"role\": \"user\", \"content\": user_input})\n",
-    "# result = agent.invoke({\"messages\": messages})\n",
-    "# messages = result[\"messages\"]\n",
-    "\n",
-    "# ai_response = messages[-1].content\n",
-    "# print(f\"🤖 AI: {ai_response}\\n\")\n",
-    "response=\"\"\n",
-    "for chunk in model.stream(messages):\n",
-    "    print(chunk.content,end=\"\",flush=True)\n",
-    "    response+=chunk.content\n",
-    "messages.append(AIMessage(content=response))\n",
-    "     "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "langchain",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/langchain-study/06.ipynb b/langchain-study/06.ipynb
deleted file mode 100644
index 047a585..0000000
--- a/langchain-study/06.ipynb
+++ /dev/null
@@ -1,118 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4c092599",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dotenv import load_dotenv\n",
-    "import os\n",
-    "load_dotenv()\n",
-    "API_KEY=os.getenv(\"ARK_API_KEY\")\n",
-    "MODEL=os.getenv(\"MODEL\")\n",
-    "BASE_URL=os.getenv(\"BASE_URL\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ce33e0e4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import create_agent\n",
-    "from langchain.agents.middleware import before_model, after_model, AgentState\n",
-    "from langchain.chat_models import init_chat_model\n",
-    "from langgraph.runtime import Runtime\n",
-    "from typing import Any"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "818b80af",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before model call:\n",
-      "Input to model: 1\n",
-      "After model call:\n",
-      "Output from model: 2\n",
-      "Final response from agent:\n",
-      "{'messages': [HumanMessage(content='Tell me a joke about cats.', additional_kwargs={}, response_metadata={}, id='5cf5df45-1b37-48fc-92da-f6140e362e80'), AIMessage(content='Why did the cat sit on the computer?  \\n\\nTo keep an eye on the mouse!  \\n\\n(They’re just really into watching both real mice *and* computer mice.) 😊', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 833, 'prompt_tokens': 91, 'total_tokens': 924, 'completion_tokens_details': {'accepted_prediction_tokens': None, 'audio_tokens': None, 'reasoning_tokens': 796, 'rejected_prediction_tokens': None}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'doubao-seed-1-6-flash-250828', 'system_fingerprint': None, 'id': '02176709935989350db425a2216b77129731d94be5ad438c43cbf', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='lc_run--019b6f54-61f5-77a3-834b-5ae5ebc6fd4b-0', usage_metadata={'input_tokens': 91, 'output_tokens': 833, 'total_tokens': 924, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 796}})]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "@before_model\n",
-    "def log_before_model(state: AgentState,runtime: Runtime) -> None:\n",
-    "    print(\"Before model call:\")\n",
-    "    print(f\"Input to model: {len(state['messages'])}\")\n",
-    "    return None\n",
-    "\n",
-    "@after_model\n",
-    "def log_after_model(state: AgentState,runtime: Runtime) -> None:\n",
-    "    print(\"After model call:\")\n",
-    "    print(f\"Output from model: {len(state['messages'])}\")\n",
-    "    return None\n",
-    "\n",
-    "model=init_chat_model(\n",
-    "    model=MODEL,\n",
-    "    model_provider=\"openai\",\n",
-    "    api_key=API_KEY,\n",
-    "    base_url=BASE_URL,\n",
-    ")\n",
-    "\n",
-    "agent=create_agent(\n",
-    "    model=model,\n",
-    "    middleware=[log_before_model,log_after_model],\n",
-    ")\n",
-    "\n",
-    "response=agent.invoke(\n",
-    "    {\n",
-    "        \"messages\":[{\n",
-    "            \"role\":\"user\",\n",
-    "            \"content\":\"Tell me a joke about cats.\"\n",
-    "        }]\n",
-    "    }\n",
-    ")\n",
-    "print(\"Final response from agent:\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f75d6878",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "langchain",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/langchain-study/07SmartWriter_wokflow.py b/langchain-study/07SmartWriter_wokflow.py
deleted file mode 100644
index acfb14f..0000000
--- a/langchain-study/07SmartWriter_wokflow.py
+++ /dev/null
@@ -1,275 +0,0 @@
-import os
-from dotenv import load_dotenv
-from langchain.chat_models import init_chat_model
-from langgraph.graph import StateGraph, END
-from typing import TypedDict, List, Optional
-from pydantic import BaseModel, Field
-from datetime import datetime
-import json
-
-load_dotenv()
-
-API_KEY = os.getenv("ARK_API_KEY")
-MODEL = os.getenv("MODEL")
-BASE_URL = os.getenv("BASE_URL")
-
-class SectionOutline(BaseModel):
-    title: str=Field(description="章节标题")
-    key_points: List[str]=Field(description="关键要点")
-
-class ArticleOutline(BaseModel):
-    title: str=Field(description="文章主题")
-    introduction: str=Field(description="引言")
-    sections: List[SectionOutline]=Field(description="文章大纲章节列表")
-    conclusion: str=Field(description="结论")
-
-class QualityScore(BaseModel):
-    coherence: float=Field(description="连贯性评分，范围0-10",ge=0,le=10)
-    relevance: float=Field(description="相关性评分，范围0-10",ge=0,le=10)
-    grammar: float=Field(description="语法正确性评分，范围0-10",ge=0,le=10)
-    overall: float=Field(description="整体质量评分，范围0-10",ge=0,le=10)
-    feedback: Optional[str]=Field(description="质量反馈意见")
-
-class WritingState(TypedDict):
-    topic: str                          # 主题
-    outline: Optional[ArticleOutline]   # 大纲
-    sections_content: List[str]         # 各章节内容
-    full_article: str                   # 完整文章
-    quality_score: Optional[QualityScore] # 质量评分
-    revision_count: int                 # 修订次数
-    approved: bool                      # 是否批准
-    human_feedback: str                 # 人工反馈
-
-def create_model():
-    return init_chat_model(
-        model=MODEL,
-        model_provider="openai",
-        api_key=API_KEY,
-        base_url=BASE_URL,
-        temperature=0.7,
-    )
-
-def plan_outline(state: WritingState) -> WritingState:
-    print(f"\n📋 规划大纲: {state['topic']}")
-    model= create_model()
-    structured_model=model.with_structured_output(ArticleOutline)
-    prompt = f"""请为以下主题创建详细的文章大纲：
-
-      主题：{state['topic']}
-
-      要求：
-      1. 创建吸引人的标题
-      2. 撰写引言（2-3句）
-      3. 设计 3-5 个章节，每个章节列出 2-3 个要点
-      4. 撰写结论（2-3句）
-
-      请确保逻辑清晰、结构完整。
-      """
-    outline=structured_model.invoke(prompt)
-    state['outline'] = outline
-
-    print(f"✅ 大纲创建完成")
-    print(f"   标题: {outline.title}")
-    print(f"   章节数: {len(outline.sections)}")
-    return state
-
-def write_sections(state: WritingState) -> WritingState:
-    model= create_model()
-    for idx, section in enumerate(state['outline'].sections):
-        print(f"\n✍️  撰写章节 {idx+1}: {section.title}")
-        prompt = f"""请根据以下大纲要点撰写文章章节：
-
-      章节标题：{section.title}
-      关键要点：
-      {chr(10).join(['- ' + kp for kp in section.key_points])}
-
-      要求：
-      1. 每个要点扩展为完整段落
-      2. 保持连贯和逻辑性
-      3. 使用正式且易懂的语言
-
-      请开始撰写该章节内容。
-      """
-        section_content = model.invoke(prompt)
-        state['sections_content'].append(section_content)
-        print(f"✅ 章节 {idx+1} 撰写完成")
-    return state
-
-def assemble_article(state: WritingState) -> WritingState:
-    print(f"\n📝  组装完整文章")
-    model= create_model()
-    prompt = f"""请将以下章节内容整合为一篇完整的文章：
-
-      标题：{state['outline'].title}
-      引言：{state['outline'].introduction}
-
-      章节内容：
-      {chr(10).join([f'章节 {i+1}:\n{content}' for i, content in enumerate(state['sections_content'])])}
-
-      结论：{state['outline'].conclusion}
-
-      要求：
-      1. 保持逻辑连贯
-      2. 使用过渡句连接各部分
-      3. 确保语言流畅且无语法错误
-
-      请开始组装文章。
-      """
-    full_article = model.invoke(prompt)
-    state['full_article'] = full_article
-    print(f"✅ 文章组装完成")
-    return state
-
-def evaluate_quality(state: WritingState) -> WritingState:
-    print(f"\n🔍  评估文章质量")
-    model= create_model()
-    structured_model=model.with_structured_output(QualityScore)
-    prompt = f"""请根据以下标准评估文章质量：
-
-      文章内容：
-      {state['full_article']}
-
-      评估标准：
-      1. 连贯性（0-10）
-      2. 相关性（0-10）
-      3. 语法正确性（0-10）
-      4. 整体质量（0-10）
-
-      请提供评分和改进建议（如有）。
-      """
-    quality_score=structured_model.invoke(prompt)
-    state['quality_score'] = quality_score
-
-    print(f"✅ 质量评估完成")
-    print(f"   整体评分: {quality_score.overall}/10")
-    return state
-
-def human_review(state: WritingState) -> WritingState:
-    print(f"\n🧑‍💼  等待人工审核...")
-    print(f"请审核以下文章内容，并提供反馈意见：\n")
-    print(f"标题: {state['outline'].title}\n")
-    print(state['full_article'])
-    feedback = input("\n请输入您的反馈意见（或按回车跳过）: ")
-    state['human_feedback'] = feedback
-    state['approved'] = feedback.strip() == ""
-    if state['approved']:
-        print("✅ 文章已批准，无需修改。")
-    else:
-        print("❗ 文章未批准，需根据反馈进行修改。")
-    return state
-
-def revise_article(state: WritingState) -> WritingState:
-    print(f"\n🔄  根据反馈修改文章")
-    model= create_model()
-    prompt = f"""请根据以下反馈意见修改文章内容：
-
-      原文章内容：
-      {state['full_article']}
-
-      反馈意见：
-      {state['human_feedback']}
-
-      请对文章进行相应修改。
-      """
-    revised_article = model.invoke(prompt)
-    state['full_article'] = revised_article
-    state['revision_count'] += 1
-    print(f"✅ 文章修改完成")
-    return state
-
-def save_article(state: WritingState):
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"article_{timestamp}.txt"
-    with open(filename, "w", encoding="utf-8") as f:
-        f.write(f"标题: {state['outline'].title}\n\n")
-        f.write(state['full_article'])
-    print(f"\n💾  文章已保存为 {filename}")
-
-def check_quality(state: WritingState) -> str:
-    score = state['quality_score'].overall
-    if score>=8:
-        print("✅ 文章质量符合要求。")
-        return "review"
-    else:
-        print("❗ 文章质量不符合要求，需要修改。")
-        return "revise"
-    
-def check_approval(state: WritingState) -> str:
-    if state['approved']:
-        print("✅ 文章已获得批准。")
-        return "save"
-    else:
-        print("❗ 文章未获批准，需要修改。")
-        return "revise"
-
-def create_writing_workflow():
-    workflow=StateGraph(WritingState)
-    workflow.add_node("plan", plan_outline)
-    workflow.add_node("write", write_sections)
-    workflow.add_node("assemble", assemble_article)
-    workflow.add_node("evaluate", evaluate_quality)
-    workflow.add_node("review", human_review)
-    workflow.add_node("revise", revise_article)
-    workflow.add_node("save", save_article)
-
-    workflow.set_entry_point("plan")
-    workflow.add_edge("plan", "write")
-    workflow.add_edge("write", "assemble")
-    workflow.add_edge("assemble", "evaluate")
-
-    workflow.add_conditional_edges(
-        "evaluate",
-        check_quality,
-        {
-            "review": "review",
-            "revise": "revise"
-        }
-    )
-    workflow.add_conditional_edges(
-        "review",
-        check_approval,
-        {
-            "save": "save",
-            "revise": "revise"
-        }
-    )
-
-    workflow.add_edge("revise","assemble")
-    workflow.add_edge("save", END)
-    return workflow.compile()
-
-def main():
-    app=create_writing_workflow()
-    topics=[
-        "人工智能在医疗领域的应用",
-        "气候变化对全球生态系统的影响",
-        "远程办公的利与弊分析"
-    ]
-    for topic in topics:
-        print(f"\n================ 开始撰写新文章: {topic} ================\n")
-        initial_state: WritingState={
-            "topic": topic,
-            "outline": None,
-            "sections_content": [],
-            "full_article": "",
-            "quality_score": None,
-            "revision_count": 0,
-            "approved": False,
-            "human_feedback": ""
-        }
-        result=app.invoke(initial_state)
-
-        print(f"\n\n" + "="*70)
-        print("✅ 写作完成！")
-        print("="*70)
-        print(f"主题: {result['topic']}")
-        print(f"标题: {result['outline'].title}")
-        print(f"质量评分: {result['quality_score'].overall}/10")
-        print(f"修订次数: {result['revision_count']}")
-        print(f"状态: {'已发布' if result['approved'] else '待处理'}")
-
-        input("\n按 Enter 继续下一篇...")
-        print(f"\n================ 文章撰写完成: {topic} ================\n")
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/langchain-study/08.ipynb b/langchain-study/08.ipynb
deleted file mode 100644
index a6219e1..0000000
--- a/langchain-study/08.ipynb
+++ /dev/null
@@ -1,129 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e5006f3a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dotenv import load_dotenv\n",
-    "import os\n",
-    "load_dotenv()\n",
-    "API_KEY=os.getenv(\"ARK_API_KEY\")\n",
-    "MODEL=os.getenv(\"MODEL\")\n",
-    "BASE_URL=os.getenv(\"BASE_URL\")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4ef5635f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import create_agent\n",
-    "from langchain.agents.middleware import before_model, after_model, AgentState\n",
-    "from langchain.chat_models import init_chat_model\n",
-    "from langgraph.runtime import Runtime\n",
-    "model=init_chat_model(\n",
-    "    model=MODEL,\n",
-    "    model_provider=\"openai\",\n",
-    "    api_key=API_KEY,\n",
-    "    base_url=BASE_URL,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "6b76b61b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import asyncio\n",
-    "from langsmith import traceable\n",
-    "# @traceable(run_type=\"llm\")\n",
-    "# def process_sequentially(queries:list):\n",
-    "#     res=[]\n",
-    "#     for query in queries:\n",
-    "#         response=model.invoke(query)\n",
-    "#         res.append(response)\n",
-    "#     return res\n",
-    "# @traceable(run_type=\"llm\")\n",
-    "# def process_batch(queries:list):\n",
-    "#     return model.batch(queries)\n",
-    "@traceable(run_type=\"llm\")\n",
-    "async def process_async(queries:list):\n",
-    "    tasks=[model.ainvoke(query) for query in queries]\n",
-    "    results=await asyncio.gather(*tasks)\n",
-    "    return [r.content for r in results]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bc8c6dfa",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "异步处理:\n",
-      "耗时: 5.06秒\n"
-     ]
-    }
-   ],
-   "source": [
-    "import time\n",
-    "queries = [f\"问题{i}\" for i in range(10)]\n",
-    "\n",
-    "# print(\"串行处理:\")\n",
-    "# start = time.time()\n",
-    "# process_sequentially(queries)\n",
-    "# print(f\"耗时: {time.time() - start:.2f}秒\\n\")\n",
-    "\n",
-    "# print(\"批处理:\")\n",
-    "# start = time.time()\n",
-    "# process_batch(queries)\n",
-    "# print(f\"耗时: {time.time() - start:.2f}秒\\n\")\n",
-    "print(\"异步处理:\")\n",
-    "start = time.time()\n",
-    "# In Colab, an event loop is usually running already. Direct `await` is preferred.\n",
-    "await process_async(queries)\n",
-    "print(f\"耗时: {time.time() - start:.2f}秒\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c25f4943",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "langchain",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/langchain-study/08.py b/langchain-study/08.py
deleted file mode 100644
index 104b57f..0000000
--- a/langchain-study/08.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-from langchain.agents import create_agent
-from langchain.chat_models import init_chat_model
-from dotenv import load_dotenv
-
-load_dotenv()
-API_KEY=os.getenv("ARK_API_KEY")
-MODEL=os.getenv("MODEL")
-BASE_URL=os.getenv("BASE_URL")
-
-os.environ["LANGCHAIN_TRACING_V2"] = "true"
-os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
-os.environ["LANGCHAIN_PROJECT"] = "superagent"
-os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
-
-def get_weather(city: str) -> str:
-    """Get weather for a given city."""
-    return f"It's always sunny in {city}!"
-
-model=init_chat_model(
-    model=MODEL,
-    model_provider="openai",
-    api_key=API_KEY,
-    base_url=BASE_URL,
-)
-agent = create_agent(
-    model=model,
-    tools=[get_weather],
-    system_prompt="You are a helpful assistant",
-)
-
-# Run the agent
-agent.invoke(
-    {"messages": [{"role": "user", "content": "What is the weather in San Francisco?"}]}
-)
\ No newline at end of file
diff --git a/langchain-study/09.py b/langchain-study/09.py
deleted file mode 100644
index 7e7f275..0000000
--- a/langchain-study/09.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import os
-import requests
-from langchain_community.document_loaders import BiliBiliLoader
-from langchain_classic.chains.query_constructor.schema import AttributeInfo
-from langchain_classic.retrievers import SelfQueryRetriever
-from langchain_community.vectorstores import Chroma
-from langchain.chat_models import init_chat_model
-import logging
-from dotenv import load_dotenv  
-from rich import traceback
-# from embedding import EmbeddingService
-
-load_dotenv()
-logging.basicConfig(level=logging.INFO)
-traceback.install()
-
-
-class SimpleEmbeddings:
-    def __init__(self):
-        self.base_url = os.getenv("BASE_URL")
-        self.embedder = os.getenv("EMBEDDER")
-        self.api_key = os.getenv("ARK_API_KEY")
-
-    def embed_documents(self, texts):
-        clean = [str(t) for t in texts]
-        payload = {"model": self.embedder, "input": clean, "encoding_format": "float"}
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json",
-        }
-        resp = requests.post(f"{self.base_url}/embeddings", json=payload, headers=headers, timeout=30)
-        resp.raise_for_status()
-        data = resp.json()
-        if "data" not in data:
-            raise RuntimeError(f"embedder 响应缺少 data 字段: {data}")
-        return [item["embedding"] for item in data["data"]]
-
-    def embed_query(self, text):
-        return self.embed_documents([text])[0]
-
-
-
-# 1. 初始化视频数据
-video_urls = [
-    "https://www.bilibili.com/video/BV1Bo4y1A7FU", 
-    "https://www.bilibili.com/video/BV1ug4y157xA",
-    "https://www.bilibili.com/video/BV1yh411V7ge",
-]
-
-bili = []
-texts = []
-metas = []
-try:
-    loader = BiliBiliLoader(video_urls=video_urls)
-    docs = loader.load()
-    
-    for doc in docs:
-        original = doc.metadata
-        
-        # 提取基本元数据字段
-        metadata = {
-            'title': original.get('title', '未知标题'),
-            'author': original.get('owner', {}).get('name', '未知作者'),
-            'source': original.get('bvid', '未知ID'),
-            'view_count': original.get('stat', {}).get('view', 0),
-            'length': original.get('duration', 0),
-        }
-        
-        doc.metadata = metadata
-        doc.page_content = ""  # 不使用正文
-        bili.append(doc)
-
-        text = f"标题:{metadata['title']} 作者:{metadata['author']} 时长:{metadata['length']}秒 观看:{metadata['view_count']}"
-        texts.append(text)
-        metas.append(metadata)
-        
-except Exception as e:
-    print(f"加载BiliBili视频失败: {str(e)}")
-
-if not bili:
-    print("没有成功加载任何视频，程序退出")
-    exit()
-
-# 2. 创建向量存储
-embed_model = SimpleEmbeddings()
-vectorstore = Chroma.from_texts(texts=texts, embedding=embed_model, metadatas=metas)
-
-# 3. 配置元数据字段信息
-metadata_field_info = [
-    AttributeInfo(
-        name="title",
-        description="视频标题（字符串）",
-        type="string", 
-    ),
-    AttributeInfo(
-        name="author",
-        description="视频作者（字符串）",
-        type="string",
-    ),
-    AttributeInfo(
-        name="view_count",
-        description="视频观看次数（整数）",
-        type="integer",
-    ),
-    AttributeInfo(
-        name="length",
-        description="视频长度，以秒为单位的整数",
-        type="integer"
-    )
-]
-
-print(bili)
-# 4. 创建自查询检索器
-llm = init_chat_model(
-    model=os.getenv("MODEL"),
-    model_provider="openai",
-    api_key=os.getenv("ARK_API_KEY"),
-    base_url=os.getenv("BASE_URL"),
-)
-
-retriever = SelfQueryRetriever.from_llm(
-    llm=llm,
-    vectorstore=vectorstore,
-    document_contents="记录视频标题、作者、观看次数等信息的视频元数据",
-    metadata_field_info=metadata_field_info,
-    enable_limit=True,
-    verbose=True
-)
-
-# 5. 执行查询示例
-queries = [
-    "时间最短的视频",
-    "时长大于600秒的视频"
-]
-
-for query in queries:
-    print(f"\n--- 查询: '{query}' ---")
-    results = retriever.invoke(query)
-    if results:
-        for doc in results:
-            title = doc.metadata.get('title', '未知标题')
-            author = doc.metadata.get('author', '未知作者')
-            view_count = doc.metadata.get('view_count', '未知')
-            length = doc.metadata.get('length', '未知')
-            print(f"标题: {title}")
-            print(f"作者: {author}")
-            print(f"观看次数: {view_count}")
-            print(f"时长: {length}秒")
-            print("="*50)
-    else:
-        print("未找到匹配的视频")
-
diff --git a/langchain-study/test.py b/langchain-study/test.py
deleted file mode 100644
index 893fa35..0000000
--- a/langchain-study/test.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from langchain_community.document_loaders import BiliBiliLoader
-
-video_urls = [
-    "https://www.bilibili.com/video/BV1Bo4y1A7FU", 
-    "https://www.bilibili.com/video/BV1ug4y157xA",
-    "https://www.bilibili.com/video/BV1yh411V7ge",
-]
-
-bili = []
-try:
-    loader = BiliBiliLoader(video_urls=video_urls)
-    docs = loader.load()
-    
-    for doc in docs:
-        original = doc.metadata
-        
-        # 提取基本元数据字段
-        metadata = {
-            'title': original.get('title', '未知标题'),
-            'author': original.get('owner', {}).get('name', '未知作者'),
-            'source': original.get('bvid', '未知ID'),
-            'view_count': original.get('stat', {}).get('view', 0),
-            'length': original.get('duration', 0),
-        }
-        
-        doc.metadata = metadata
-        if doc.page_content and doc.page_content.strip():
-            doc.page_content = doc.page_content.strip()
-            bili.append(doc)
-        print(doc)  # For debugging purposes
-    # print(bili)
-except Exception as e:
-    print(f"加载BiliBili视频失败: {str(e)}")
\ No newline at end of file