feat(im): mirror WeChat bot chats into AI chat as WeXin Bot conversation; add conversations.source; migrate history; remove redundant history page

backend/python/app/api/chat.py

@@ -691,6 +691,7 @@ def list_conversations(
             "status": conv.status,
             "pinned": conv.pinned,
             "knowledge_graph_count": conv.knowledge_graph_count or 0,
+            "source": getattr(conv, "source", None),
             "created_at": str(conv.created_at) if conv.created_at else None,
             "updated_at": str(conv.updated_at) if conv.updated_at else None,
         })
@@ -1140,69 +1141,8 @@ def clear_conversation(
     return {"code": 0, "message": "对话上下文已清空"}
 
 
-@router.post("/conversations/{conv_id}/to-knowledge-graph")
+def _build_knowledge_graph(db: Session, kb: KnowledgeBase) -> dict:
-def convert_to_knowledge_graph(
+    """从知识库文档构建知识图谱节点和边，返回 graph_data。"""
-    conv_id: str,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-):
-    """将对话内容转换为知识图谱"""
-    conv = db.query(Conversation).filter(
-        and_(Conversation.id == conv_id, Conversation.user_id == current_user.id)
-    ).first()
-    if not conv:
-        raise HTTPException(status_code=404, detail="对话不存在")
-
-    messages = (
-        db.query(Message)
-        .filter(Message.conversation_id == conv_id)
-        .order_by(asc(Message.created_at))
-        .all()
-    )
-
-    if not messages:
-        raise HTTPException(status_code=400, detail="对话中没有消息, 无法生成知识图谱")
-
-    kb_name = f"对话图谱-{conv.title}"
-    kb = KnowledgeBase(
-        id=gen_uuid(),
-        user_id=current_user.id,
-        name=kb_name,
-        description=f"从对话「{conv.title}」自动生成的知识图谱",
-        directory_path="/",
-        document_count=0,
-        created_at=cst_now(),
-        updated_at=cst_now(),
-    )
-    db.add(kb)
-    db.flush()
-
-    doc_count = 0
-    for idx, msg in enumerate(messages):
-        role_label = "用户" if msg.role == "user" else "AI"
-        preview = re.sub(r'[\\n\\r\\t]+', ' ', msg.content[:40]).strip()
-        filename = f"msg_{idx+1:03d}_{role_label}_{preview}.txt"
-
-        doc = KnowledgeDocument(
-            id=gen_uuid(),
-            knowledge_base_id=kb.id,
-            name=filename,
-            filename=filename,
-            file_type="txt",
-            file_size=len(msg.content.encode('utf-8')),
-            content=msg.content,
-            status="completed",
-            chunk_count=max(1, len(msg.content) // 500 + 1),
-            created_at=cst_now(),
-            updated_at=cst_now(),
-        )
-        db.add(doc)
-        doc_count += 1
-
-    kb.document_count = doc_count
-    db.flush()
-
-    # 构建知识图谱节点和边
     documents = db.query(KnowledgeDocument).filter(
         and_(
             KnowledgeDocument.knowledge_base_id == kb.id,
@@ -1268,7 +1208,96 @@ def convert_to_knowledge_graph(
 
     kb.knowledge_graph_count = len(graph_nodes)
     kb.updated_at = cst_now()
-    conv.knowledge_graph_count = (conv.knowledge_graph_count or 0) + 1
+    return graph_data
+
+
+@router.post("/conversations/{conv_id}/to-knowledge-graph")
+def convert_to_knowledge_graph(
+    conv_id: str,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user),
+):
+    """将对话内容转换为知识图谱（幂等：同一对话仅生成一次知识库）"""
+    conv = db.query(Conversation).filter(
+        and_(Conversation.id == conv_id, Conversation.user_id == current_user.id)
+    ).first()
+    if not conv:
+        raise HTTPException(status_code=404, detail="对话不存在")
+
+    messages = (
+        db.query(Message)
+        .filter(Message.conversation_id == conv_id)
+        .order_by(asc(Message.created_at))
+        .all()
+    )
+
+    if not messages:
+        raise HTTPException(status_code=400, detail="对话中没有消息, 无法生成知识图谱")
+
+    # 幂等：若该对话已生成过知识图谱且知识库仍存在，则直接刷新返回
+    if conv.knowledge_graph_base_id:
+        existing_kb = db.query(KnowledgeBase).filter(
+            KnowledgeBase.id == conv.knowledge_graph_base_id
+        ).first()
+        if existing_kb:
+            graph_data = _build_knowledge_graph(db, existing_kb)
+            conv.knowledge_graph_count = 1
+            conv.updated_at = cst_now()
+            db.commit()
+            return {
+                "code": 0,
+                "data": {
+                    "knowledge_base_id": existing_kb.id,
+                    "knowledge_base_name": existing_kb.name,
+                    "graph": graph_data,
+                    "knowledge_graph_count": 1,
+                    "already_exists": True,
+                },
+                "message": "知识图谱已存在, 已刷新展示",
+            }
+
+    kb_name = f"对话图谱-{conv.title}"
+    kb = KnowledgeBase(
+        id=gen_uuid(),
+        user_id=current_user.id,
+        name=kb_name,
+        description=f"从对话「{conv.title}」自动生成的知识图谱",
+        directory_path="/",
+        document_count=0,
+        created_at=cst_now(),
+        updated_at=cst_now(),
+    )
+    db.add(kb)
+    db.flush()
+
+    doc_count = 0
+    for idx, msg in enumerate(messages):
+        role_label = "用户" if msg.role == "user" else "AI"
+        preview = re.sub(r'[\\n\\r\\t]+', ' ', msg.content[:40]).strip()
+        filename = f"msg_{idx+1:03d}_{role_label}_{preview}.txt"
+
+        doc = KnowledgeDocument(
+            id=gen_uuid(),
+            knowledge_base_id=kb.id,
+            filename=filename,
+            file_type="txt",
+            file_size=len(msg.content.encode('utf-8')),
+            content=msg.content,
+            status="completed",
+            chunk_count=max(1, len(msg.content) // 500 + 1),
+            created_at=cst_now(),
+            updated_at=cst_now(),
+        )
+        db.add(doc)
+        doc_count += 1
+
+    kb.document_count = doc_count
+    db.flush()
+
+    # 构建知识图谱节点和边
+    graph_data = _build_knowledge_graph(db, kb)
+    conv.knowledge_graph_base_id = kb.id
+    conv.knowledge_graph_count = 1
     conv.updated_at = cst_now()
 
     db.commit()
@@ -1285,6 +1314,8 @@ def convert_to_knowledge_graph(
             "knowledge_base_id": kb.id,
             "knowledge_base_name": kb.name,
             "graph": graph_data,
+            "knowledge_graph_count": 1,
+            "already_exists": False,
         },
         "message": f"知识图谱构建完成",
     }
@@ -1299,6 +1330,7 @@ def get_models(db: Session = Depends(get_db)):
         result.append({
             "id": m.id,
             "name": m.name,
+            "display_name": getattr(m, "display_name", None) or m.name,
             "provider_id": m.provider_id,
             "provider_name": provider.name if provider else "",
             "status": m.status,

backend/python/app/api/im_bot_user.py

@@ -264,6 +264,63 @@ def _log_chat(user_id, platform, from_user, context_token, role, content):
             db.close()
 
 
+# ============================================================
+# 镜像到 AI 聊天（标准 conversations/messages）
+# 个人微信对话作为一个独立会话窗（默认名 "WeXin Bot"）出现在 AI 聊天中，
+# 从而复用 AI 聊天模块的全部功能（滚动翻阅、重命名、置顶、删除等）。
+# ============================================================
+WECHAT_CONV_TITLE = "WeXin Bot"
+
+
+def _get_or_create_wechat_conv(db: Session, user_id: str) -> str:
+    """按 user_id 找/建 source='wechat_bot' 的会话，返回会话 id。"""
+    rows = _execute_raw(db, text(
+        "SELECT id FROM conversations WHERE user_id=:uid AND source='wechat_bot' "
+        "AND status<>'archived' ORDER BY updated_at DESC LIMIT 1"
+    ), {"uid": user_id})
+    if rows:
+        return _row_to_dict(rows[0])["id"]
+    import uuid as _uuid
+    cid = str(_uuid.uuid4())
+    now = datetime.now()
+    _execute_raw(db, text(
+        "INSERT INTO conversations "
+        "(id, user_id, title, status, pinned, knowledge_graph_count, source, created_at, updated_at) "
+        "VALUES (:id, :uid, :title, 'active', 0, 0, 'wechat_bot', :c, :u)"
+    ), {"id": cid, "uid": user_id, "title": WECHAT_CONV_TITLE, "c": now, "u": now})
+    return cid
+
+
+def _append_ai_message(db: Session, conv_id: str, role: str, content: str, created=None):
+    import uuid as _uuid
+    _execute_raw(db, text(
+        "INSERT INTO messages (id, conversation_id, role, content, token_used, created_at) "
+        "VALUES (:id, :cid, :role, :content, 0, :c)"
+    ), {
+        "id": str(_uuid.uuid4()), "cid": conv_id, "role": role,
+        "content": content, "c": created or datetime.now(),
+    })
+
+
+def _mirror_to_ai_chat(user_id: str, user_content: str, assistant_content: str):
+    """把一轮微信问答写入用户的 WeXin Bot AI 聊天会话。"""
+    if not user_id:
+        return
+    db = None
+    try:
+        db = SessionLocal()
+        cid = _get_or_create_wechat_conv(db, user_id)
+        _append_ai_message(db, cid, "user", user_content)
+        _append_ai_message(db, cid, "assistant", assistant_content)
+        _execute_raw(db, text("UPDATE conversations SET updated_at=:u WHERE id=:id"),
+                     {"u": datetime.now(), "id": cid})
+    except Exception:
+        pass
+    finally:
+        if db:
+            db.close()
+
+
 # ============================================================
 # 后台接收线程（每用户独立）
 # ============================================================
@@ -326,6 +383,8 @@ def _wechat_receiver_loop(account_id: int):
                     if owner_uid:
                         _log_chat(owner_uid, "personal_wechat", sender, ctx, "user", content)
                         _log_chat(owner_uid, "personal_wechat", sender, ctx, "assistant", reply)
+                        # 同步镜像到 AI 聊天的 WeXin Bot 会话窗
+                        _mirror_to_ai_chat(owner_uid, content, reply)
                 else:
                     try:
                         with open("/tmp/wechat_send.log", "a", encoding="utf-8") as _f:

backend/python/app/models.py

@@ -85,6 +85,7 @@ class Conversation(Base):
     pinned = Column(Boolean, default=False)
     knowledge_graph_count = Column(Integer, default=0)
     knowledge_graph_base_id = Column(String(64), default=None, comment="已生成的知识图谱知识库ID")
+    source = Column(String(32), default=None, comment="对话来源，如 wechat_bot")
     created_at = Column(DateTime, default=datetime.now)
     updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
 

frontend/src/App.tsx

@@ -23,7 +23,6 @@ import AssetsPage from '@/pages/front/AssetsPage';
 import NovelPreviewPage from '@/pages/front/NovelPreviewPage';
 import EvolutionDashboard from '@/pages/front/EvolutionDashboard';
 import WechatBot from '@/pages/front/WechatBot';
-import WechatHistory from '@/pages/front/WechatHistory';
 
 // Admin Pages
 import AdminDashboard from '@/pages/admin/Dashboard';
@@ -120,7 +119,6 @@ const App: React.FC = () => {
           <Route path="assets/novel/:id" element={<NovelPreviewPage />} />
           <Route path="evolution" element={<EvolutionDashboard />} />
           <Route path="wechat-bot" element={<WechatBot />} />
-          <Route path="wechat-history" element={<WechatHistory />} />
         </Route>
 
         {/* Admin Routes */}

frontend/src/components/layout/FrontLayout.tsx

@@ -18,7 +18,6 @@ const navGroups = [
     items: [
       { path: '/channels', icon: 'channel', label: '频道管理' },
       { path: '/wechat-bot', icon: 'wechat', label: '微信机器人' },
-      { path: '/wechat-history', icon: 'chat', label: '对话记录' },
     ],
   },
   {