Merge branch 'ji' into master

.gitignore

@@ -88,10 +88,14 @@ skills/
 hermes-agent-main/
 .gitignore
 offline-backend-20260512/backend/tests/*
+# Keep the openGauss URL/dialect/schema compatibility regression coverage versioned.
+!offline-backend-20260512/backend/tests/test_gauss_database.py
 !offline-backend-20260512/backend/tests/test_llmwiki_weknora.py
 !offline-backend-20260512/backend/tests/test_health_auth_bypass.py
 # Keep formal Word OOXML and embedded-font regression coverage versioned.
 !offline-backend-20260512/backend/tests/test_word_export.py
+# Keep generated-file library aggregation and filtering regression coverage versioned.
+!offline-backend-20260512/backend/tests/test_artifact_library.py
 # Keep the model configuration regression coverage versioned even though
 # this deployment repository otherwise ignores newly added backend tests.
 !offline-backend-20260512/backend/tests/test_model_management.py

frontend-web/docs/skill-knowledge-and-assistant-knowledge.md

@@ -0,0 +1,7 @@
+# 技能知识归纳与“知识梳理”助手知识库
+
+管理员在「技能管理 → 技能知识归纳」中选择一个或多个技能、一个或多个目标，系统按“技能 × 目标”创建后台任务。界面提供总体进度、单项失败重试、实体/关系审核、绑定重归纳、自动同步和两种解绑动作。技能列表通过一次 bindings 查询展示归纳目标，避免逐技能请求。
+
+扫描仅处理知识资料。代码、脚本、锁文件、密钥文件以及 Markdown 代码块不会进入上传、摘要、提示词或知识指纹；ZIP 只读取逐项验证通过的知识成员。自动监听只比较该知识指纹，所以纯代码改动不会产生 stale 状态。
+
+知识库页面新增「普通知识库 / 助手知识库」并行入口。管理员可将系统默认入口设为 v1（普通 WeKnora）或 v2（全局“知识梳理”）。助手库无需 WeKnora 即可初始化，所有用户可只读查看和搜索 Wiki；管理员可上传本地文件、从普通库导入、重新导入或删除来源、查看版本并回滚。来源删除只移除对应贡献，共享实体与关系继续保留。

frontend-web/src/components/workspace/chats/chat-box.tsx

@@ -1,7 +1,7 @@
 import type { Message } from "@langchain/langgraph-sdk";
 import { useQuery } from "@tanstack/react-query";
 import { FilesIcon, XIcon } from "lucide-react";
-import { usePathname } from "next/navigation";
+import { usePathname, useSearchParams } from "next/navigation";
 import { useEffect, useMemo, useRef, useState } from "react";
 import type { GroupImperativeHandle } from "react-resizable-panels";
 
@@ -15,6 +15,7 @@ import {
 import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs";
 import { apiFetch } from "@/core/api";
 import {
+  artifactPathname,
   listThreadArtifacts,
   mergeArtifactPaths,
 } from "@/core/artifacts/listing";
@@ -94,7 +95,13 @@ const ChatBox: React.FC<{
     knowledgeReferencesEnabled = false,
   } = useThread();
   const pathname = usePathname();
+  const searchParams = useSearchParams();
+  const requestedArtifact = useMemo(() => {
+    const value = searchParams.get("artifact")?.trim();
+    return value ? artifactPathname(value) : null;
+  }, [searchParams]);
   const threadIdRef = useRef(threadId);
+  const openedRequestedArtifactRef = useRef<string | null>(null);
   const artifactsCountRef = useRef(0);
   const layoutRef = useRef<GroupImperativeHandle>(null);
   const latestReferenceBatchIdRef = useRef<string | null>(null);
@@ -135,7 +142,7 @@ const ChatBox: React.FC<{
   const { skills } = useSkills();
 
   const canScanArtifactDirectory =
-    artifactDirectoryModeEnabled &&
+    (artifactDirectoryModeEnabled || !!requestedArtifact) &&
     !disableArtifacts &&
     !isMock &&
     !!threadId &&
@@ -153,17 +160,21 @@ const ChatBox: React.FC<{
   );
   const checkpointArtifactSignature = checkpointArtifacts.join("\n");
   const availableArtifacts = useMemo(
-    () =>
-      artifactDirectoryModeEnabled
-        ? mergeArtifactPaths(
-            checkpointArtifacts,
-            (artifactListingQuery.data?.files ?? []).map((file) => file.path),
-          )
-        : checkpointArtifacts,
+    () => {
+      if (!artifactDirectoryModeEnabled && !requestedArtifact) {
+        return checkpointArtifacts;
+      }
+      const discoveredArtifacts = (artifactListingQuery.data?.files ?? []).map(
+        (file) => file.path,
+      );
+      if (requestedArtifact) discoveredArtifacts.push(requestedArtifact);
+      return mergeArtifactPaths(checkpointArtifacts, discoveredArtifacts);
+    },
     [
       artifactDirectoryModeEnabled,
       artifactListingQuery.data?.files,
       checkpointArtifacts,
+      requestedArtifact,
     ],
   );
 
@@ -207,6 +218,29 @@ const ChatBox: React.FC<{
       thread.isLoading,
     );
   }, [referencesEnabled, skillDisplayMap, thread.isLoading, thread.values.messages]);
+  useEffect(() => {
+    if (
+      disableArtifacts ||
+      !requestedArtifact ||
+      !threadId ||
+      threadId === "new"
+    ) {
+      return;
+    }
+    const requestKey = `${threadId}:${artifactPathname(requestedArtifact)}`;
+    if (openedRequestedArtifactRef.current === requestKey) return;
+    openedRequestedArtifactRef.current = requestKey;
+    selectArtifact(requestedArtifact);
+    setArtifactsOpen(true);
+    setSideTab("document");
+  }, [
+    disableArtifacts,
+    requestedArtifact,
+    selectArtifact,
+    setArtifactsOpen,
+    threadId,
+  ]);
+
   useEffect(() => {
     if (!shouldFetchBackendReferenceBatches) {
       setBackendReferenceBatches([]);

frontend-web/src/components/workspace/knowledge-product-nav.tsx

@@ -0,0 +1,57 @@
+import { useMutation, useQuery, useQueryClient } from "@tanstack/react-query";
+import { BookOpen, Database } from "lucide-react";
+import { useLocation, useNavigate } from "react-router-dom";
+import { toast } from "sonner";
+
+import { Button } from "@/components/ui/button";
+import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select";
+import { getStoredAuth } from "@/core/auth";
+import {
+  loadKnowledgeBaseProductVersion,
+  type KnowledgeBaseProductVersion,
+  updateKnowledgeBaseProductVersion,
+} from "@/core/system-settings/api";
+
+export const KNOWLEDGE_PRODUCT_KEY = ["system-settings", "knowledge-base-product-version"] as const;
+export const KNOWLEDGE_NORMAL_VIEW_VALUE = "normal";
+export const KNOWLEDGE_NORMAL_VIEW_SEARCH = `?view=${KNOWLEDGE_NORMAL_VIEW_VALUE}`;
+
+export function useKnowledgeBaseProductVersion() {
+  return useQuery({ queryKey: KNOWLEDGE_PRODUCT_KEY, queryFn: loadKnowledgeBaseProductVersion, staleTime: 60_000 });
+}
+
+export function KnowledgeProductNav() {
+  const location = useLocation();
+  const navigate = useNavigate();
+  const client = useQueryClient();
+  const version = useKnowledgeBaseProductVersion();
+  const isAdmin = getStoredAuth()?.system_role === "admin";
+  const save = useMutation({
+    mutationFn: updateKnowledgeBaseProductVersion,
+    onSuccess: (value) => {
+      client.setQueryData(KNOWLEDGE_PRODUCT_KEY, value);
+      toast.success(`默认知识库体验已切换为 ${value}`);
+    },
+  });
+  const assistant = location.pathname.includes("/knowledge/assistant");
+
+  return (
+    <div className="flex items-center gap-2 border-b bg-card px-6 py-2">
+      <Button size="sm" variant={assistant ? "ghost" : "secondary"} onClick={() => navigate(`/page/workspace/knowledge${KNOWLEDGE_NORMAL_VIEW_SEARCH}`)}>
+        <BookOpen />普通知识库
+      </Button>
+      <Button size="sm" variant={assistant ? "secondary" : "ghost"} onClick={() => navigate("/page/workspace/knowledge/assistant")}>
+        <Database />助手知识库
+      </Button>
+      {isAdmin && (
+        <div className="ml-auto flex items-center gap-2 text-xs text-muted-foreground">
+          默认产品版本
+          <Select value={version.data ?? "v1"} onValueChange={(value) => save.mutate(value as KnowledgeBaseProductVersion)}>
+            <SelectTrigger className="h-8 w-40"><SelectValue /></SelectTrigger>
+            <SelectContent><SelectItem value="v1">v1 普通知识库</SelectItem><SelectItem value="v2">v2 助手知识库</SelectItem></SelectContent>
+          </Select>
+        </div>
+      )}
+    </div>
+  );
+}

frontend-web/src/components/workspace/llmwiki-knowledge-selector.tsx

@@ -22,6 +22,10 @@ import {
 } from "@/components/ui/dropdown-menu";
 import { Input } from "@/components/ui/input";
 import { cn } from "@/lib/utils";
+import {
+  type AssistantKnowledgeBase,
+  useAssistantKnowledgeBases,
+} from "@/strategy-components/api/assistant-knowledge";
 import {
   type LlmWikiKnowledgeBase,
   useAgentLlmWikiKnowledgeBases,
@@ -66,10 +70,26 @@ function SpaceIcon({ item }: { item: LlmWikiKnowledgeBase }) {
   );
 }
 
+function AssistantSpaceIcon({ item }: { item: AssistantKnowledgeBase }) {
+  return (
+    <span
+      className={cn(
+        "flex size-7 shrink-0 items-center justify-center rounded-md",
+        item.is_global ? "bg-amber-500/10 text-amber-600 dark:text-amber-400" : "bg-sky-500/10 text-sky-600 dark:text-sky-400",
+      )}
+    >
+      <Database className="size-3.5" />
+    </span>
+  );
+}
+
 function SelectionContent({
   rows,
+  assistantRows,
   value,
+  assistantValue,
   onChange,
+  onAssistantChange,
   search,
   onSearchChange,
   loading,
@@ -79,8 +99,11 @@ function SelectionContent({
   readOnly,
 }: {
   rows: LlmWikiKnowledgeBase[];
+  assistantRows: AssistantKnowledgeBase[];
   value: string[];
+  assistantValue: string[];
   onChange: (value: string[]) => void;
+  onAssistantChange: (value: string[]) => void;
   search: string;
   onSearchChange: (value: string) => void;
   loading: boolean;
@@ -90,6 +113,7 @@ function SelectionContent({
   readOnly: boolean;
 }) {
   const selected = useMemo(() => new Set(value), [value]);
+  const selectedAssistant = useMemo(() => new Set(assistantValue), [assistantValue]);
   const filtered = useMemo(() => {
     const query = search.trim().toLocaleLowerCase();
     if (!query) return rows;
@@ -97,11 +121,28 @@ function SelectionContent({
       `${item.name} ${item.description}`.toLocaleLowerCase().includes(query),
     );
   }, [rows, search]);
+  const filteredAssistant = useMemo(() => {
+    const query = search.trim().toLocaleLowerCase();
+    if (!query) return assistantRows;
+    return assistantRows.filter((item) =>
+      `${item.name} ${item.description ?? ""}`.toLocaleLowerCase().includes(query),
+    );
+  }, [assistantRows, search]);
   const groups = useMemo(() => groupRows(filtered), [filtered]);
   const toggle = (id: string) => {
     if (readOnly) return;
     onChange(selected.has(id) ? value.filter((item) => item !== id) : [...value, id]);
   };
+  const toggleAssistant = (id: string) => {
+    if (readOnly) return;
+    onAssistantChange(
+      selectedAssistant.has(id)
+        ? assistantValue.filter((item) => item !== id)
+        : [...assistantValue, id],
+    );
+  };
+  const selectedCount = value.length + assistantValue.length;
+  const hasRows = groups.length > 0 || filteredAssistant.length > 0;
 
   return (
     <>
@@ -127,7 +168,7 @@ function SelectionContent({
           <div className="m-2 rounded-lg border border-destructive/30 bg-destructive/5 p-3 text-xs text-destructive">
             {error instanceof Error ? error.message : "知识空间读取失败"}
           </div>
-        ) : groups.length === 0 ? (
+        ) : !hasRows ? (
           <div className="flex h-28 items-center justify-center px-4 text-center text-xs text-muted-foreground">
             {search
               ? "没有匹配的知识空间"
@@ -136,91 +177,181 @@ function SelectionContent({
                 : "当前账号暂无可用知识空间"}
           </div>
         ) : (
-          groups.map((group, groupIndex) => (
-            <div key={group.key}>
-              {groupIndex > 0 && (
-                variant === "popover"
-                  ? <DropdownMenuSeparator className="my-1.5" />
-                  : <div className="my-1.5 border-t" />
-              )}
-              {variant === "popover" ? (
-                <DropdownMenuLabel className="flex items-center gap-1.5 px-2 py-1 text-[11px] font-semibold text-muted-foreground">
-                  {group.key === "personal" ? <Folder className="size-3" /> : <Globe2 className="size-3" />}{group.label}
-                </DropdownMenuLabel>
-              ) : (
-                <div className="flex items-center gap-1.5 px-2 py-1 text-[11px] font-semibold text-muted-foreground">
-                  {group.key === "personal" ? <Folder className="size-3" /> : <Globe2 className="size-3" />}{group.label}
-                </div>
-              )}
-              {group.rows.map((item) => {
-                const rowContent = (
-                  <>
-                    <SpaceIcon item={item} />
-                    <div className="min-w-0 flex-1">
-                      <div className="truncate text-xs font-medium">{item.name}</div>
-                      <div className="mt-0.5 truncate text-[10px] text-muted-foreground">
-                        {item.type === "faq" ? "FAQ" : "文档库"} · {item.knowledge_count ?? 0} 项知识
-                      </div>
-                    </div>
-                    {item.publication_status === "published" && (
-                      <Badge variant="outline" className="h-4 px-1 text-[9px]">公共</Badge>
-                    )}
-                  </>
-                );
-                return variant === "popover" ? (
-                  <DropdownMenuCheckboxItem
-                    key={item.id}
-                    checked={selected.has(item.id)}
-                    disabled={readOnly}
-                    onCheckedChange={() => toggle(item.id)}
-                    onSelect={(event) => event.preventDefault()}
-                    className={cn(
-                      "my-0.5 min-h-10 rounded-md py-1.5 pr-2 pl-7",
-                      selected.has(item.id) && "bg-primary/8 focus:bg-primary/12",
-                    )}
-                  >
-                    {rowContent}
-                  </DropdownMenuCheckboxItem>
+          <>
+            {groups.map((group, groupIndex) => (
+              <div key={group.key}>
+                {groupIndex > 0 && (
+                  variant === "popover"
+                    ? <DropdownMenuSeparator className="my-1.5" />
+                    : <div className="my-1.5 border-t" />
+                )}
+                {variant === "popover" ? (
+                  <DropdownMenuLabel className="flex items-center gap-1.5 px-2 py-1 text-[11px] font-semibold text-muted-foreground">
+                    {group.key === "personal" ? <Folder className="size-3" /> : <Globe2 className="size-3" />}{group.label}
+                  </DropdownMenuLabel>
                 ) : (
-                  <button
-                    key={item.id}
-                    type="button"
-                    role="checkbox"
-                    aria-checked={selected.has(item.id)}
-                    disabled={readOnly}
-                    onClick={() => toggle(item.id)}
-                    className={cn(
-                      "my-0.5 flex min-h-11 w-full items-center gap-2 rounded-md px-2 py-1.5 text-left outline-none transition-colors hover:bg-muted/60 focus-visible:ring-2 focus-visible:ring-primary",
-                      selected.has(item.id) && "bg-primary/8",
-                    )}
-                  >
-                    <span className={cn(
-                      "flex size-4 shrink-0 items-center justify-center rounded border",
-                      selected.has(item.id) && "border-primary bg-primary text-primary-foreground",
-                    )}>
-                      {selected.has(item.id) && <Check className="size-3" />}
-                    </span>
-                    {rowContent}
-                  </button>
-                );
-              })}
-            </div>
-          ))
+                  <div className="flex items-center gap-1.5 px-2 py-1 text-[11px] font-semibold text-muted-foreground">
+                    {group.key === "personal" ? <Folder className="size-3" /> : <Globe2 className="size-3" />}{group.label}
+                  </div>
+                )}
+                {group.rows.map((item) => {
+                  const rowContent = (
+                    <>
+                      <SpaceIcon item={item} />
+                      <div className="min-w-0 flex-1">
+                        <div className="truncate text-xs font-medium">{item.name}</div>
+                        <div className="mt-0.5 truncate text-[10px] text-muted-foreground">
+                          {item.type === "faq" ? "FAQ" : "文档库"} · {item.knowledge_count ?? 0} 项知识
+                        </div>
+                      </div>
+                      {item.publication_status === "published" && (
+                        <Badge variant="outline" className="h-4 px-1 text-[9px]">公共</Badge>
+                      )}
+                    </>
+                  );
+                  return variant === "popover" ? (
+                    <DropdownMenuCheckboxItem
+                      key={item.id}
+                      checked={selected.has(item.id)}
+                      disabled={readOnly}
+                      onCheckedChange={() => toggle(item.id)}
+                      onSelect={(event) => event.preventDefault()}
+                      className={cn(
+                        "my-0.5 min-h-10 rounded-md py-1.5 pr-2 pl-7",
+                        selected.has(item.id) && "bg-primary/8 focus:bg-primary/12",
+                      )}
+                    >
+                      {rowContent}
+                    </DropdownMenuCheckboxItem>
+                  ) : (
+                    <button
+                      key={item.id}
+                      type="button"
+                      role="checkbox"
+                      aria-checked={selected.has(item.id)}
+                      disabled={readOnly}
+                      onClick={() => toggle(item.id)}
+                      className={cn(
+                        "my-0.5 flex min-h-11 w-full items-center gap-2 rounded-md px-2 py-1.5 text-left outline-none transition-colors hover:bg-muted/60 focus-visible:ring-2 focus-visible:ring-primary",
+                        selected.has(item.id) && "bg-primary/8",
+                      )}
+                    >
+                      <span className={cn(
+                        "flex size-4 shrink-0 items-center justify-center rounded border",
+                        selected.has(item.id) && "border-primary bg-primary text-primary-foreground",
+                      )}>
+                        {selected.has(item.id) && <Check className="size-3" />}
+                      </span>
+                      {rowContent}
+                    </button>
+                  );
+                })}
+              </div>
+            ))}
+            {filteredAssistant.length > 0 && (
+              <div>
+                {(groups.length > 0) && (
+                  variant === "popover"
+                    ? <DropdownMenuSeparator className="my-1.5" />
+                    : <div className="my-1.5 border-t" />
+                )}
+                {variant === "popover" ? (
+                  <DropdownMenuLabel className="flex items-center gap-1.5 px-2 py-1 text-[11px] font-semibold text-muted-foreground">
+                    <Database className="size-3" />助手知识库
+                  </DropdownMenuLabel>
+                ) : (
+                  <div className="flex items-center gap-1.5 px-2 py-1 text-[11px] font-semibold text-muted-foreground">
+                    <Database className="size-3" />助手知识库
+                  </div>
+                )}
+                {filteredAssistant.map((item) => {
+                  const rowContent = (
+                    <>
+                      <AssistantSpaceIcon item={item} />
+                      <div className="min-w-0 flex-1">
+                        <div className="truncate text-xs font-medium">{item.name}</div>
+                        <div className="mt-0.5 truncate text-[10px] text-muted-foreground">
+                          Wiki · {item.page_count ?? 0} 页 · {item.entity_count ?? 0} 实体
+                        </div>
+                      </div>
+                      <Badge variant="outline" className="h-4 px-1 text-[9px]">
+                        {item.is_global ? "总库" : "助手"}
+                      </Badge>
+                    </>
+                  );
+                  return variant === "popover" ? (
+                    <DropdownMenuCheckboxItem
+                      key={`assistant:${item.id}`}
+                      checked={selectedAssistant.has(item.id)}
+                      disabled={readOnly}
+                      onCheckedChange={() => toggleAssistant(item.id)}
+                      onSelect={(event) => event.preventDefault()}
+                      className={cn(
+                        "my-0.5 min-h-10 rounded-md py-1.5 pr-2 pl-7",
+                        selectedAssistant.has(item.id) && "bg-primary/8 focus:bg-primary/12",
+                      )}
+                    >
+                      {rowContent}
+                    </DropdownMenuCheckboxItem>
+                  ) : (
+                    <button
+                      key={`assistant:${item.id}`}
+                      type="button"
+                      role="checkbox"
+                      aria-checked={selectedAssistant.has(item.id)}
+                      disabled={readOnly}
+                      onClick={() => toggleAssistant(item.id)}
+                      className={cn(
+                        "my-0.5 flex min-h-11 w-full items-center gap-2 rounded-md px-2 py-1.5 text-left outline-none transition-colors hover:bg-muted/60 focus-visible:ring-2 focus-visible:ring-primary",
+                        selectedAssistant.has(item.id) && "bg-primary/8",
+                      )}
+                    >
+                      <span className={cn(
+                        "flex size-4 shrink-0 items-center justify-center rounded border",
+                        selectedAssistant.has(item.id) && "border-primary bg-primary text-primary-foreground",
+                      )}>
+                        {selectedAssistant.has(item.id) && <Check className="size-3" />}
+                      </span>
+                      {rowContent}
+                    </button>
+                  );
+                })}
+              </div>
+            )}
+          </>
         )}
       </div>
 
       <div className="flex items-center justify-between gap-2 border-t bg-muted/20 px-2.5 py-2">
         <span className="text-[11px] text-muted-foreground">
-          {readOnly ? `本对话已选 ${value.length} 个` : `已选择 ${value.length} 个`}
+          {readOnly ? `本对话已选 ${selectedCount} 个` : `已选择 ${selectedCount} 个`}
         </span>
         {readOnly ? (
           <span className="text-[11px] text-muted-foreground">对话开始后不可更改</span>
         ) : (
           <div className="flex items-center gap-1">
-            <Button type="button" variant="ghost" size="sm" className="h-7 px-2 text-xs" onClick={() => onChange(filtered.map((item) => item.id))}>
+            <Button
+              type="button"
+              variant="ghost"
+              size="sm"
+              className="h-7 px-2 text-xs"
+              onClick={() => {
+                onChange(filtered.map((item) => item.id));
+                onAssistantChange(filteredAssistant.map((item) => item.id));
+              }}
+            >
               全选
             </Button>
-            <Button type="button" variant="ghost" size="sm" className="h-7 px-2 text-xs" onClick={() => onChange([])}>
+            <Button
+              type="button"
+              variant="ghost"
+              size="sm"
+              className="h-7 px-2 text-xs"
+              onClick={() => {
+                onChange([]);
+                onAssistantChange([]);
+              }}
+            >
               清空
             </Button>
           </div>
@@ -232,7 +363,9 @@ function SelectionContent({
 
 export function LlmWikiKnowledgeSelector({
   value,
+  assistantValue = [],
   onChange,
+  onAssistantChange = () => undefined,
   source = "all",
   agentId,
   autoSelectAll = false,
@@ -243,7 +376,9 @@ export function LlmWikiKnowledgeSelector({
   variant = "popover",
 }: {
   value: string[];
+  assistantValue?: string[];
   onChange: (value: string[]) => void;
+  onAssistantChange?: (value: string[]) => void;
   source?: SelectorSource;
   agentId?: string | null;
   autoSelectAll?: boolean;
@@ -257,28 +392,39 @@ export function LlmWikiKnowledgeSelector({
   const [open, setOpen] = useState(false);
   const [search, setSearch] = useState("");
   const runtime = useLlmWikiRuntime();
+  const weknoraEnabled = runtime.data?.provider === "weknora";
   const all = useLlmWikiKnowledgeBases(
     "all",
-    runtime.data?.provider === "weknora",
+    weknoraEnabled,
   );
   const agent = useAgentLlmWikiKnowledgeBases(
     agentId,
-    runtime.data?.provider === "weknora" && source === "agent" && !!agentId,
+    weknoraEnabled && source === "agent" && !!agentId,
   );
-  const rows = all.data ?? [];
+  const assistantBases = useAssistantKnowledgeBases();
+  const rows = weknoraEnabled ? (all.data ?? []) : [];
+  const assistantRows = assistantBases.data ?? [];
   const defaultIds = useMemo(() => {
     if (source !== "agent") {
       return rows.map((item) => item.id);
     }
     return (agent.data ?? []).map((item) => item.id);
   }, [agent.data, rows, source]);
-  const loading = all.isLoading || (source === "agent" && !!agentId && agent.isLoading);
-  const error = all.error ?? (source === "agent" ? agent.error : null);
+  const loading = runtime.isLoading || (weknoraEnabled && all.isLoading) || assistantBases.isLoading || (source === "agent" && !!agentId && agent.isLoading);
+  const error = (weknoraEnabled ? all.error ?? (source === "agent" ? agent.error : null) : null) ?? assistantBases.error;
   const availableIds = useMemo(() => new Set(rows.map((item) => item.id)), [rows]);
+  const assistantAvailableIds = useMemo(
+    () => new Set(assistantRows.map((item) => item.id)),
+    [assistantRows],
+  );
   const initializationKey = `${source}:${agentId ?? "all"}:${defaultIds.join(",")}`;
   const initialized = useRef<string | null>(null);
 
   useEffect(() => {
+    if (!runtime.isLoading && !weknoraEnabled) {
+      if (value.length > 0) onChange([]);
+      return;
+    }
     if (!all.data || (source === "agent" && agentId && !agent.data) || initialized.current === initializationKey) return;
     initialized.current = initializationKey;
     const valid = value.filter((id) => availableIds.has(id));
@@ -288,19 +434,30 @@ export function LlmWikiKnowledgeSelector({
     if (next.length !== value.length || next.some((id, index) => id !== value[index])) {
       onChange(next);
     }
-  }, [agent.data, agentId, all.data, autoSelectAll, availableIds, defaultIds, initializationKey, onChange, source, value]);
+  }, [agent.data, agentId, all.data, autoSelectAll, availableIds, defaultIds, initializationKey, onChange, runtime.isLoading, source, value, weknoraEnabled]);
+
+  useEffect(() => {
+    if (!assistantBases.data) return;
+    const valid = assistantValue.filter((id) => assistantAvailableIds.has(id));
+    if (valid.length !== assistantValue.length || valid.some((id, index) => id !== assistantValue[index])) {
+      onAssistantChange(valid);
+    }
+  }, [assistantAvailableIds, assistantBases.data, assistantValue, onAssistantChange]);
 
   useEffect(() => {
     if (!open) setSearch("");
   }, [open]);
 
-  if (runtime.isError || runtime.data?.provider !== "weknora") return null;
+  if ((runtime.isError || !weknoraEnabled) && !assistantBases.isLoading && assistantRows.length === 0) return null;
 
   const content = (
     <SelectionContent
       rows={rows}
+      assistantRows={assistantRows}
       value={value}
+      assistantValue={assistantValue}
       onChange={onChange}
+      onAssistantChange={onAssistantChange}
       search={search}
       onSearchChange={setSearch}
       loading={loading}
@@ -323,7 +480,7 @@ export function LlmWikiKnowledgeSelector({
               新建该智能体对话时，只能从已绑定空间中选择本次检索范围。
             </p>
           </div>
-          <Badge variant="secondary" className="shrink-0">{value.length} 个</Badge>
+          <Badge variant="secondary" className="shrink-0">{value.length + assistantValue.length} 个</Badge>
         </div>
         {content}
       </div>
@@ -333,10 +490,15 @@ export function LlmWikiKnowledgeSelector({
   const selectedNames = value
     .map((id) => rows.find((item) => item.id === id)?.name)
     .filter((name): name is string => !!name);
-  const selectedLabel = selectedNames.length > 0
-    ? selectedNames.join("、")
-    : value.length > 0
-      ? `已选 ${value.length} 个知识空间`
+  const selectedAssistantNames = assistantValue
+    .map((id) => assistantRows.find((item) => item.id === id)?.name)
+    .filter((name): name is string => !!name);
+  const allSelectedNames = [...selectedNames, ...selectedAssistantNames];
+  const selectedCount = value.length + assistantValue.length;
+  const selectedLabel = allSelectedNames.length > 0
+    ? allSelectedNames.join("、")
+    : selectedCount > 0
+      ? `已选 ${selectedCount} 个知识空间`
       : label;
 
   return (
@@ -348,20 +510,20 @@ export function LlmWikiKnowledgeSelector({
           size="sm"
           className={cn(
             "h-8 max-w-52 gap-1.5 rounded-full border-0 bg-transparent px-2.5 text-muted-foreground shadow-none hover:bg-muted/70 hover:text-foreground",
-            value.length > 0 && "bg-primary/8 text-primary hover:bg-primary/12 hover:text-primary",
+            selectedCount > 0 && "bg-primary/8 text-primary hover:bg-primary/12 hover:text-primary",
             className,
           )}
           disabled={disabled || loading}
-          title={value.length > 0 ? selectedLabel : undefined}
+          title={selectedCount > 0 ? selectedLabel : undefined}
         >
           {loading ? <Loader2 className="size-3.5 animate-spin" /> : <Database className="size-3.5" />}
           <span className="truncate text-xs font-normal">{selectedLabel}</span>
-          {value.length > 0 && (
+          {selectedCount > 0 && (
             <span className="flex size-4 items-center justify-center rounded-full bg-primary text-[9px] font-semibold text-primary-foreground">
-              {value.length}
+              {selectedCount}
             </span>
           )}
-          {value.length > 0 && value.length === rows.length && <Check className="size-3" />}
+          {selectedCount > 0 && selectedCount === rows.length + assistantRows.length && <Check className="size-3" />}
         </Button>
       </DropdownMenuTrigger>
       <DropdownMenuContent align="start" sideOffset={8} className="w-80 overflow-hidden rounded-xl p-0 shadow-xl">

frontend-web/src/components/workspace/settings/skill-settings-page.tsx

@@ -93,6 +93,7 @@ import {
   useViewMode,
 } from "@/core/system-settings/hooks";
 import type { Skill } from "@/core/skills/type";
+import type { SkillKnowledgeBinding } from "@/strategy-components/api/skill-knowledge";
 import { formatDateTime } from "@/core/utils/datetime";
 import { env } from "@/env";
 import { desensitize } from "@/lib/desensitize";
@@ -601,6 +602,7 @@ export function SkillRow({
   isAdmin,
   layout = "row",
   onPublisherClick,
+  knowledgeBindings = [],
 }: {
   skill: Skill;
   currentUserId: string | null;
@@ -610,6 +612,8 @@ export function SkillRow({
   /** Click the 发布者 line to filter the list to that publisher. Receives the
    * owner_user_id + its desensitized display label. */
   onPublisherClick?: (publisher: { id: string; label: string }) => void;
+  /** Admin list can inject its single batched binding query here (never N+1). */
+  knowledgeBindings?: SkillKnowledgeBinding[];
 }) {
   const isCard = layout === "card";
   const { t } = useI18n();
@@ -858,6 +862,20 @@ export function SkillRow({
           常驻
         </Badge>
       )}
+      {knowledgeBindings.slice(0, 3).map((binding) => (
+        <Badge
+          key={binding.id}
+          variant="outline"
+          className="border-indigo-500/30 text-indigo-700 dark:text-indigo-300 text-xs"
+          title={`${binding.target_name} · ${binding.status}${binding.remote_business_key ? ` · ${binding.remote_business_key}` : ""}`}
+        >
+          知识库：{binding.target_name || binding.target_id}
+          {binding.status === "stale" ? "（待更新）" : ""}
+        </Badge>
+      ))}
+      {knowledgeBindings.length > 3 && (
+        <Badge variant="secondary" className="text-xs">+{knowledgeBindings.length - 3}</Badge>
+      )}
     </div>
   );
 

frontend-web/src/components/workspace/skills/SkillKnowledgePanel.tsx

@@ -0,0 +1,261 @@
+import { Database, Loader2, RefreshCw, Search, Sparkles } from "lucide-react";
+import { useEffect, useMemo, useState } from "react";
+import { toast } from "sonner";
+
+import { Badge } from "@/components/ui/badge";
+import { Button } from "@/components/ui/button";
+import { Checkbox } from "@/components/ui/checkbox";
+import { Input } from "@/components/ui/input";
+import { Progress } from "@/components/ui/progress";
+import { ScrollArea } from "@/components/ui/scroll-area";
+import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select";
+import { useAllSkillsAdmin } from "@/core/skills/hooks";
+import { useLlmWikiKnowledgeBases, useLlmWikiRuntime } from "@/strategy-components/api/llmwiki";
+import {
+  type SkillKnowledgeTarget,
+  useSkillKnowledgeActions,
+  useSkillKnowledgeBindings,
+  useSkillKnowledgeJobs,
+} from "@/strategy-components/api/skill-knowledge";
+
+const PHASE_LABELS: Record<string, string> = {
+  scanning: "扫描知识文件",
+  parsing: "解析资料",
+  extracting: "抽取实体关系",
+  awaiting_review: "等待审核",
+  merging: "合并实体关系",
+  writing_wiki: "写入 Wiki",
+  activating: "激活新版本",
+  completed: "完成",
+};
+
+export function SkillKnowledgePanel() {
+  const { skills, isLoading } = useAllSkillsAdmin(true);
+  const liveSkills = useMemo(() => skills.filter((skill) => (skill.state ?? "active") === "active"), [skills]);
+  const [selectedSkills, setSelectedSkills] = useState<string[]>([]);
+  const [skillQuery, setSkillQuery] = useState("");
+  const [selectedTargetKey, setSelectedTargetKey] = useState<string | null>(null);
+  const [reviewMode, setReviewMode] = useState<"auto_high_confidence" | "off">("auto_high_confidence");
+  const runtime = useLlmWikiRuntime();
+  const knowledgeBases = useLlmWikiKnowledgeBases("all", runtime.data?.provider === "weknora");
+  const bindings = useSkillKnowledgeBindings();
+  const jobs = useSkillKnowledgeJobs();
+  const actions = useSkillKnowledgeActions();
+
+  const targets = useMemo<SkillKnowledgeTarget[]>(() => {
+    return (knowledgeBases.data ?? [])
+      .filter((kb) => {
+        const strategy = kb.indexing_strategy ?? {};
+        const capabilities = kb.capabilities ?? {};
+        const wikiEnabled =
+          kb.type === "wiki" ||
+          kb.wiki_index_enabled === true ||
+          strategy.wiki_enabled === true ||
+          capabilities.wiki_enabled === true ||
+          (kb.wiki_config && Object.keys(kb.wiki_config).length > 0);
+        return kb.can_write && kb.remote_status !== "missing" && !kb.is_conversation_deposit && kb.type !== "faq" && wikiEnabled;
+      })
+      .map((kb) => ({
+        target_type: "weknora",
+        target_mode: "wiki",
+        target_id: kb.id,
+        target_name: kb.name,
+        remote_business_key: `weknora:kb:${kb.id}`,
+      }));
+  }, [knowledgeBases.data]);
+
+  const filteredSkills = useMemo(() => {
+    const needle = skillQuery.trim().toLowerCase();
+    if (!needle) return liveSkills;
+    return liveSkills.filter((skill) => {
+      const tagText = (skill.tags ?? []).map((tag) => tag.name).join(" ");
+      const haystack = [
+        skill.name,
+        skill.name_zh,
+        skill.description,
+        skill.category,
+        tagText,
+      ]
+        .filter(Boolean)
+        .join("\n")
+        .toLowerCase();
+      return haystack.includes(needle);
+    });
+  }, [liveSkills, skillQuery]);
+
+  const filteredSkillNames = useMemo(() => filteredSkills.map((skill) => skill.name), [filteredSkills]);
+  const allFilteredSelected = filteredSkillNames.length > 0 && filteredSkillNames.every((name) => selectedSkills.includes(name));
+
+  const toggleFilteredSkills = () => {
+    if (!filteredSkillNames.length) return;
+    setSelectedSkills((current) => {
+      if (allFilteredSelected) {
+        const filtered = new Set(filteredSkillNames);
+        return current.filter((name) => !filtered.has(name));
+      }
+      return [...current, ...filteredSkillNames.filter((name) => !current.includes(name))];
+    });
+  };
+
+  useEffect(() => {
+    if (selectedTargetKey && !targets.some((target) => `${target.target_type}:${target.target_id}` === selectedTargetKey)) {
+      setSelectedTargetKey(null);
+    }
+  }, [selectedTargetKey, targets]);
+
+  const toggle = (values: string[], value: string, setter: (next: string[]) => void) =>
+    setter(values.includes(value) ? values.filter((item) => item !== value) : [...values, value]);
+
+  const submit = async () => {
+    const selectedTargets = targets.filter((target) =>
+      selectedTargetKey === `${target.target_type}:${target.target_id}`,
+    );
+    if (!selectedSkills.length || !selectedTargets.length) {
+      toast.error("请至少选择一个技能和一个目标知识库");
+      return;
+    }
+    try {
+      const job = await actions.createJob.mutateAsync({
+        skill_names: selectedSkills,
+        targets: selectedTargets,
+        review_mode: reviewMode,
+      });
+      toast.success(`已创建 ${job.total_count} 个归纳项`);
+      setSelectedSkills([]);
+    } catch (error) {
+      toast.error(error instanceof Error ? error.message : String(error));
+    }
+  };
+
+  return (
+    <div className="grid size-full min-h-0 grid-cols-[minmax(320px,0.9fr)_minmax(420px,1.1fr)] gap-4 overflow-hidden p-6">
+      <section className="flex min-h-0 flex-col rounded-lg border bg-card">
+        <div className="border-b p-4">
+          <h2 className="font-semibold">技能到知识库管理</h2>
+          <p className="mt-1 text-xs text-muted-foreground">一次选择多个技能归纳到一个 WeKnora Wiki 知识库；代码与脚本不会被读取。</p>
+        </div>
+        <ScrollArea className="min-h-0 flex-1">
+          <div className="space-y-5 p-4">
+            <div>
+              <div className="mb-2 flex items-center justify-between">
+                <span className="text-sm font-medium">1. 选择技能（{selectedSkills.length}）</span>
+                <Button variant="ghost" size="sm" onClick={toggleFilteredSkills}>
+                  {allFilteredSelected ? "清空当前结果" : "选择当前结果"}
+                </Button>
+              </div>
+              <div className="relative mb-2">
+                <Search className="absolute left-2.5 top-1/2 size-4 -translate-y-1/2 text-muted-foreground" />
+                <Input
+                  value={skillQuery}
+                  onChange={(event) => setSkillQuery(event.target.value)}
+                  placeholder="按技能中文名、英文名、描述、分类或标签搜索"
+                  className="pl-8"
+                />
+              </div>
+              <div className="max-h-60 space-y-1 overflow-y-auto rounded-md border p-2">
+                {isLoading ? <Loader2 className="m-4 size-5 animate-spin" /> : filteredSkills.map((skill) => (
+                  <label key={skill.name} className="flex cursor-pointer items-center gap-2 rounded px-2 py-1.5 hover:bg-muted">
+                    <Checkbox checked={selectedSkills.includes(skill.name)} onCheckedChange={() => toggle(selectedSkills, skill.name, setSelectedSkills)} />
+                    <span className="truncate text-sm">{skill.name_zh || skill.name}</span>
+                    <span className="ml-auto truncate text-xs text-muted-foreground">{skill.name}</span>
+                  </label>
+                ))}
+                {!isLoading && !filteredSkills.length && (
+                  <p className="p-3 text-xs text-muted-foreground">没有匹配的技能，请换个关键词。</p>
+                )}
+              </div>
+              <p className="mt-1 text-xs text-muted-foreground">
+                当前显示 {filteredSkills.length}/{liveSkills.length} 个技能。
+              </p>
+            </div>
+            <div>
+              <div className="mb-2 flex items-center justify-between">
+                <span className="text-sm font-medium">2. 选择目标知识库（{selectedTargetKey ? 1 : 0}）</span>
+              </div>
+              <div className="space-y-1 rounded-md border p-2">
+                {targets.map((target) => {
+                  const key = `${target.target_type}:${target.target_id}`;
+                  return (
+                    <label key={key} className="flex cursor-pointer items-center gap-2 rounded px-2 py-2 hover:bg-muted">
+                      <Checkbox
+                        checked={selectedTargetKey === key}
+                        onCheckedChange={() => setSelectedTargetKey((current) => (current === key ? null : key))}
+                      />
+                      <Database className="size-4 text-muted-foreground" />
+                      <span className="text-sm">{target.target_name}</span>
+                      <Badge variant="outline" className="ml-auto text-[10px]">WeKnora Wiki</Badge>
+                    </label>
+                  );
+                })}
+                {(runtime.isLoading || knowledgeBases.isLoading) && <Loader2 className="m-4 size-5 animate-spin" />}
+                {!runtime.isLoading && !knowledgeBases.isLoading && knowledgeBases.error && (
+                  <p className="p-3 text-xs text-destructive">
+                    读取 WeKnora 普通知识库失败：
+                    {knowledgeBases.error instanceof Error ? knowledgeBases.error.message : String(knowledgeBases.error)}
+                  </p>
+                )}
+                {!runtime.isLoading && !knowledgeBases.isLoading && !knowledgeBases.error && !targets.length && (
+                  <p className="p-3 text-xs text-muted-foreground">
+                    {runtime.data?.provider !== "weknora"
+                      ? "当前未配置 WeKnora 普通知识库地址；技能需要先归纳到普通知识库，再导入助手知识库。"
+                      : "当前没有可写且已开启 Wiki 的普通知识库；请确认该库不是 FAQ/会话沉淀库，并已开启 Wiki。"}
+                  </p>
+                )}
+              </div>
+            </div>
+            <div className="flex items-center gap-3">
+              <span className="text-sm font-medium">审核策略</span>
+              <Select value={reviewMode} onValueChange={(value) => setReviewMode(value as typeof reviewMode)}>
+                <SelectTrigger className="w-52"><SelectValue /></SelectTrigger>
+                <SelectContent>
+                  <SelectItem value="auto_high_confidence">高置信自动通过</SelectItem>
+                  <SelectItem value="off">不阻塞写入</SelectItem>
+                </SelectContent>
+              </Select>
+            </div>
+            <Button className="w-full" disabled={actions.createJob.isPending} onClick={() => void submit()}>
+              {actions.createJob.isPending ? <Loader2 className="animate-spin" /> : <Sparkles />}
+              开始归纳（{selectedSkills.length * (selectedTargetKey ? 1 : 0)} 项）
+            </Button>
+          </div>
+        </ScrollArea>
+      </section>
+
+      <section className="flex min-h-0 flex-col gap-4 overflow-hidden">
+        <div className="flex min-h-0 flex-1 flex-col rounded-lg border bg-card">
+          <div className="flex items-center justify-between border-b p-4">
+            <div><h2 className="font-semibold">归纳任务</h2><p className="text-xs text-muted-foreground">后台执行、失败可单项重试</p></div>
+            <Button size="icon" variant="ghost" onClick={() => void jobs.refetch()}><RefreshCw className="size-4" /></Button>
+          </div>
+          <ScrollArea className="min-h-0 flex-1">
+            <div className="space-y-3 p-4">
+              {(jobs.data ?? []).map((job) => {
+                const done = job.success_count + job.partial_failed_count + job.failed_count;
+                const progress = job.total_count ? Math.round((done / job.total_count) * 100) : 0;
+                return (
+                  <div key={job.id} className="rounded-md border p-3">
+                    <div className="flex items-center gap-2 text-sm"><Badge variant={job.status === "completed" ? "default" : "secondary"}>{job.status}</Badge><span>{done}/{job.total_count} 项</span><span className="ml-auto text-xs text-muted-foreground">{new Date(job.created_at).toLocaleString()}</span></div>
+                    <Progress className="my-2" value={progress} />
+                    {job.items?.map((item) => (
+                      <div key={item.id} className="mt-2 flex items-center gap-2 border-t pt-2 text-xs">
+                        <span className="max-w-36 truncate font-medium">{item.skill_name}</span>
+                        <span className="text-muted-foreground">{PHASE_LABELS[item.phase] || item.phase}</span>
+                        <span className="ml-auto">{item.progress}%</span>
+                        {item.status === "failed" && <Button size="sm" variant="outline" onClick={() => actions.retry.mutate(item.id)}>重试</Button>}
+                      </div>
+                    ))}
+                  </div>
+                );
+              })}
+              {!jobs.data?.length && <p className="py-10 text-center text-sm text-muted-foreground">暂无归纳任务</p>}
+            </div>
+          </ScrollArea>
+        </div>
+        <div className="max-h-80 overflow-auto rounded-lg border bg-card p-3 text-xs">
+          <div className="mb-2 text-muted-foreground">当前有效绑定：{bindings.data?.length ?? 0}；同名同类型实体按来源贡献合并。</div>
+          <div className="space-y-2">{bindings.data?.map((binding) => <div key={binding.id} className="flex flex-wrap items-center gap-2 rounded border p-2"><span className="max-w-32 truncate font-medium">{binding.skill_name}</span><span className="max-w-40 truncate text-muted-foreground">→ {binding.target_name}</span><Badge variant="outline">{binding.status}</Badge><code className="max-w-40 truncate text-[10px] text-muted-foreground">{binding.remote_business_key}</code><Button size="sm" variant="outline" className="ml-auto" disabled={actions.resync.isPending} onClick={() => actions.resync.mutate(binding.id)}>重归纳</Button><Button size="sm" variant="ghost" onClick={() => { if (window.confirm("解除关联并保留目标知识内容？")) actions.detach.mutate({ id: binding.id, deleteContributions: false }); }}>解绑并保留</Button><Button size="sm" variant="destructive" onClick={() => { if (window.confirm("解除关联并删除此技能贡献？共享知识仍会保留其他来源。")) actions.detach.mutate({ id: binding.id, deleteContributions: true }); }}>解绑并删除贡献</Button></div>)}</div>
+        </div>
+      </section>
+    </div>
+  );
+}

frontend-web/src/components/workspace/workspace-nav-chat-list.tsx

@@ -14,7 +14,7 @@ import {
   SidebarMenuItem,
 } from "@/components/ui/sidebar";
 import { useI18n } from "@/core/i18n/hooks";
-import { BookOpen, BotIcon, BrainIcon, FileSearch, ListChecks, MessageSquarePlus, MessagesSquare, PenLine, Plus, SparklesIcon, Telescope, Users } from "lucide-react";
+import { BookOpen, BotIcon, BrainIcon, FileSearch, Library, ListChecks, MessageSquarePlus, MessagesSquare, PenLine, Plus, SparklesIcon, Telescope, Users } from "lucide-react";
 
 import { KnowledgeRagToggle } from "./knowledge-rag-toggle";
 import { SidebarCollapseButton } from "./workspace-header";
@@ -65,6 +65,13 @@ export const WORKSPACE_COMPACT_NAV_ITEMS: WorkspaceCompactNavItem[] = [
     path: "/page/workspace/chats",
     match: (pathname) => pathname === "/page/workspace/chats",
   },
+  {
+    id: "workspace-library",
+    label: "资料库",
+    icon: Library,
+    path: "/page/workspace/library",
+    match: (pathname) => pathname === "/page/workspace/library",
+  },
   {
     id: "workspace-agents",
     label: "智能体",

frontend-web/src/components/workspace/workspace-nav-menu.tsx

@@ -254,6 +254,14 @@ export function WorkspaceNavMenu() {
                       技能管理
                     </DropdownMenuItem>
                   )}
+                  {isAdmin && (
+                    <DropdownMenuItem
+                      onClick={() => navigate("/page/workspace/admin/skills?tab=knowledge")}
+                    >
+                      <DatabaseZapIcon />
+                      技能到知识库管理
+                    </DropdownMenuItem>
+                  )}
                   {isAdmin && (
                     <DropdownMenuItem
                       onClick={() => navigate("/page/workspace/admin/models")}

frontend-web/src/core/artifacts/download.ts

@@ -1,4 +1,5 @@
 import { apiFetch } from "../api";
+import { downloadMarkdownAsDocx } from "../scheduled-tasks/markdown-docx";
 import { getFileName } from "../utils/files";
 
 import { urlOfArtifact } from "./utils";
@@ -76,6 +77,24 @@ export async function downloadArtifactFile({
   window.setTimeout(() => URL.revokeObjectURL(objectUrl), 30_000);
 }
 
+export async function downloadArtifactMarkdownAsWord({
+  filepath,
+  threadId,
+}: {
+  filepath: string;
+  threadId: string;
+}): Promise<void> {
+  const url = urlOfArtifact({ filepath, threadId, download: false });
+  const res = await apiFetch(url);
+  if (!res.ok) {
+    throw new Error(await errorMessageFromResponse(res));
+  }
+
+  const fileName = getFileName(filepath);
+  const title = fileName.replace(/\.(md|markdown)$/i, "");
+  await downloadMarkdownAsDocx(await res.text(), fileName, title);
+}
+
 export async function openArtifactInNewTab({
   filepath,
   threadId,

frontend-web/src/core/artifacts/library.ts

@@ -0,0 +1,113 @@
+import { apiFetch } from "@/core/api";
+import { getBackendBaseURL } from "@/core/config";
+
+export type ArtifactLibraryKind =
+  | "all"
+  | "document"
+  | "spreadsheet"
+  | "text_data"
+  | "archive"
+  | "other";
+
+export type ArtifactLibrarySource = "all" | "general_qa" | "agent";
+
+export interface ArtifactLibraryItem {
+  threadId: string;
+  threadTitle: string;
+  agentId: string | null;
+  agentName: string | null;
+  threadType: string | null;
+  sourceType: Exclude<ArtifactLibrarySource, "all">;
+  path: string;
+  name: string;
+  sizeBytes: number;
+  mimeType: string | null;
+  modifiedAt: string;
+  fileKind: Exclude<ArtifactLibraryKind, "all">;
+}
+
+export interface ArtifactLibraryResponse {
+  total: number;
+  offset: number;
+  limit: number;
+  items: ArtifactLibraryItem[];
+}
+
+export async function fetchArtifactLibrary({
+  query,
+  fileKind,
+  sourceType,
+  offset,
+  limit,
+  refresh = false,
+  signal,
+}: {
+  query?: string;
+  fileKind: ArtifactLibraryKind;
+  sourceType: ArtifactLibrarySource;
+  offset: number;
+  limit: number;
+  refresh?: boolean;
+  signal?: AbortSignal;
+}): Promise<ArtifactLibraryResponse> {
+  const search = new URLSearchParams({
+    file_kind: fileKind,
+    source_type: sourceType,
+    offset: String(offset),
+    limit: String(limit),
+  });
+  const normalizedQuery = query?.trim();
+  if (normalizedQuery) search.set("query", normalizedQuery);
+  if (refresh) search.set("refresh", "true");
+
+  const response = await apiFetch(
+    `${getBackendBaseURL()}/api/artifact-library?${search.toString()}`,
+    { method: "GET", signal },
+  );
+  if (!response.ok) {
+    const detail = await response.text().catch(() => "");
+    throw new Error(
+      `Failed to load artifact library (${response.status})${detail ? `: ${detail}` : ""}`,
+    );
+  }
+
+  const data = (await response.json()) as {
+    total?: number;
+    offset?: number;
+    limit?: number;
+    items?: Array<{
+      thread_id: string;
+      thread_title: string;
+      agent_id?: string | null;
+      agent_name?: string | null;
+      thread_type?: string | null;
+      source_type?: Exclude<ArtifactLibrarySource, "all">;
+      path: string;
+      name: string;
+      size_bytes: number;
+      mime_type?: string | null;
+      modified_at: string;
+      file_kind: Exclude<ArtifactLibraryKind, "all">;
+    }>;
+  };
+
+  return {
+    total: data.total ?? 0,
+    offset: data.offset ?? offset,
+    limit: data.limit ?? limit,
+    items: (data.items ?? []).map((item) => ({
+      threadId: item.thread_id,
+      threadTitle: item.thread_title,
+      agentId: item.agent_id ?? null,
+      agentName: item.agent_name ?? item.agent_id ?? null,
+      threadType: item.thread_type ?? null,
+      sourceType: item.source_type ?? (item.agent_id ? "agent" : "general_qa"),
+      path: item.path,
+      name: item.name,
+      sizeBytes: item.size_bytes,
+      mimeType: item.mime_type ?? null,
+      modifiedAt: item.modified_at,
+      fileKind: item.file_kind,
+    })),
+  };
+}

frontend-web/src/core/llmwiki/wiki-page-markdown.ts

@@ -149,7 +149,7 @@ export function buildWikiIndexCatalogMarkdown(
   buildPageUrl: WikiPageUrlBuilder = buildWeKnoraWikiPageUrl,
   indexGroups: WikiIndexGroup[] = [],
 ): string {
-  if (indexGroups.length > 0) {
+  if (indexGroups.some((group) => group.items.length > 0)) {
     const sections: string[] = [];
     for (const group of indexGroups) {
       if (group.items.length === 0) continue;

frontend-web/src/core/page-layout/sidebar-menu.ts

@@ -61,6 +61,7 @@ export const sidebarMenuItems: MenuItem[] = [
     icon: HelpCircle,
     children: [
       { id: "general-qa", label: "通用问答", icon: CircleHelp, path: "/page/workspace/chats/new" },
+      { id: "artifact-library", label: "资料库", icon: Library, path: "/page/workspace/library" },
       {
         id: "multi-agent-roundtable",
         label: "多智能体会商",

frontend-web/src/core/system-settings/api.ts

@@ -9,6 +9,31 @@ export interface PromptPrefixSettings {
   ordinary_qa_markdown_format_enabled: boolean;
 }
 
+export type KnowledgeBaseProductVersion = "v1" | "v2";
+
+export async function loadKnowledgeBaseProductVersion(): Promise<KnowledgeBaseProductVersion> {
+  const response = await apiFetch(
+    `${getBackendBaseURL()}/api/system-settings/knowledge-base-product-version`,
+  );
+  if (!response.ok) throw new Error(`Failed to load knowledge base product version: ${response.status}`);
+  return ((await response.json()) as { version: KnowledgeBaseProductVersion }).version;
+}
+
+export async function updateKnowledgeBaseProductVersion(
+  version: KnowledgeBaseProductVersion,
+): Promise<KnowledgeBaseProductVersion> {
+  const response = await apiFetch(
+    `${getBackendBaseURL()}/api/system-settings/knowledge-base-product-version`,
+    {
+      method: "PUT",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ version }),
+    },
+  );
+  if (!response.ok) throw new Error(`Failed to update knowledge base product version: ${response.status}`);
+  return ((await response.json()) as { version: KnowledgeBaseProductVersion }).version;
+}
+
 export interface PluginDomainAgentMappingDto {
   id: string;
   domain: string;

frontend-web/src/core/threads/hooks.ts

@@ -1512,10 +1512,16 @@ export function useThreadStream({
         )
           ? mergedContext.llmwiki_knowledge_base_ids
           : [];
+        const selectedAssistantKnowledgeIds = Array.isArray(
+          mergedContext.assistant_knowledge_base_ids,
+        )
+          ? mergedContext.assistant_knowledge_base_ids
+          : [];
         const ragModeEnabled =
           (referenceModeEntryEnabled && localSettings.display.ragModeEnabled) ||
           isThreadRagModeLocked(threadId) ||
-          selectedLlmWikiIds.length > 0;
+          selectedLlmWikiIds.length > 0 ||
+          selectedAssistantKnowledgeIds.length > 0;
         if (ragModeEnabled) {
           lockThreadRagMode(threadId);
         }

frontend-web/src/core/threads/types.ts

@@ -27,6 +27,8 @@ export interface AgentThreadContext extends Record<string, unknown> {
   knowledge_rag_enabled?: boolean;
   /** DeerFlow LLMWiki mapping ids selected for this conversation. */
   llmwiki_knowledge_base_ids?: string[];
+  /** Assistant knowledge-base ids selected for this conversation. */
+  assistant_knowledge_base_ids?: string[];
   /** Direct RAG modes handled in the frontend; these requests never enter Lead Agent. */
   execution_profile?: "single_round_rag" | "multi_round_rag";
   agent_id?: string;

frontend-web/src/pages/AdminSkillsPage.tsx

@@ -3,11 +3,13 @@
 import {
   ChevronLeftIcon,
   ChevronRightIcon,
+  DatabaseZap,
   Replace,
   SearchIcon,
   SparklesIcon,
 } from "lucide-react";
 import { useEffect, useMemo, useState } from "react";
+import { useSearchParams } from "react-router-dom";
 
 import { Button } from "@/components/ui/button";
 import { Input } from "@/components/ui/input";
@@ -18,17 +20,32 @@ import {
   WorkspaceHeader,
 } from "@/components/workspace/workspace-container";
 import { SkillRow } from "@/components/workspace/settings/skill-settings-page";
+import { SkillKnowledgePanel } from "@/components/workspace/skills/SkillKnowledgePanel";
 import { getStoredAuth } from "@/core/auth";
 import { useAllSkillsAdmin } from "@/core/skills/hooks";
 import { desensitize } from "@/lib/desensitize";
+import { useSkillKnowledgeBindings } from "@/strategy-components/api/skill-knowledge";
 import { SkillAddressesPanel } from "./SkillAddressesPage";
 
 const PAGE_SIZE = 10;
 
 export default function AdminSkillsPage() {
+  const [searchParams, setSearchParams] = useSearchParams();
   const [search, setSearch] = useState("");
   const [page, setPage] = useState(1);
   const { skills, isLoading, error } = useAllSkillsAdmin(true);
+  const knowledgeBindings = useSkillKnowledgeBindings();
+  const activeTab = useMemo(() => {
+    const tab = searchParams.get("tab");
+    return tab === "addresses" || tab === "knowledge" ? tab : "skills";
+  }, [searchParams]);
+
+  const changeTab = (value: string) => {
+    const next = new URLSearchParams(searchParams);
+    if (value === "skills") next.delete("tab");
+    else next.set("tab", value);
+    setSearchParams(next, { replace: true });
+  };
 
   const storedAuth = useMemo(() => getStoredAuth(), []);
   const currentUserId = storedAuth?.user_id ?? null;
@@ -56,7 +73,8 @@ export default function AdminSkillsPage() {
       <WorkspaceHeader />
       <WorkspaceBody>
         <Tabs
-          defaultValue="skills"
+          value={activeTab}
+          onValueChange={changeTab}
           className="flex size-full min-h-0 flex-col gap-0"
         >
           <div className="flex items-center justify-between gap-4 border-b px-6 pt-4">
@@ -71,6 +89,10 @@ export default function AdminSkillsPage() {
                   <Replace />
                   技能地址管理
                 </TabsTrigger>
+                <TabsTrigger value="knowledge">
+                  <DatabaseZap />
+                  技能到知识库管理
+                </TabsTrigger>
               </TabsList>
             </div>
           </div>
@@ -126,6 +148,9 @@ export default function AdminSkillsPage() {
                     skill={skill}
                     currentUserId={currentUserId}
                     isAdmin
+                    knowledgeBindings={(knowledgeBindings.data ?? []).filter(
+                      (binding) => binding.skill_name === skill.name,
+                    )}
                   />
                 ))}
               </>
@@ -164,6 +189,12 @@ export default function AdminSkillsPage() {
           >
             <SkillAddressesPanel />
           </TabsContent>
+          <TabsContent
+            value="knowledge"
+            className="mt-0 min-h-0 flex-1 overflow-hidden"
+          >
+            <SkillKnowledgePanel />
+          </TabsContent>
         </Tabs>
       </WorkspaceBody>
     </WorkspaceContainer>

frontend-web/src/pages/AgentChatPage.tsx

@@ -118,6 +118,26 @@ function readPendingKnowledgeSelection(agentId: string | undefined): string[] |
   }
 }
 
+function readPendingAssistantKnowledgeSelection(agentId: string | undefined): string[] | null {
+  if (typeof window === "undefined" || !agentId) return null;
+  try {
+    const raw = sessionStorage.getItem("pendingAgentMessage");
+    if (!raw) return null;
+    const parsed = JSON.parse(raw) as {
+      agentId?: string;
+      assistantKnowledgeBaseIds?: unknown;
+    };
+    if (parsed.agentId !== agentId || !Array.isArray(parsed.assistantKnowledgeBaseIds)) {
+      return null;
+    }
+    return parsed.assistantKnowledgeBaseIds
+      .map((item) => String(item).trim())
+      .filter(Boolean);
+  } catch {
+    return null;
+  }
+}
+
 function stringifyForHiddenContext(value: unknown): string {
   if (typeof value === "string") return value;
   try {
@@ -334,11 +354,17 @@ export default function AgentChatPage({
   const pendingKnowledgeSelection = useRef(
     readPendingKnowledgeSelection(agent_id),
   );
+  const pendingAssistantKnowledgeSelection = useRef(
+    readPendingAssistantKnowledgeSelection(agent_id),
+  );
   const [selectedKnowledgeBaseIds, setSelectedKnowledgeBaseIds] = useState<string[]>(
     () => pendingKnowledgeSelection.current ?? [],
   );
+  const [selectedAssistantKnowledgeBaseIds, setSelectedAssistantKnowledgeBaseIds] = useState<string[]>(
+    () => pendingAssistantKnowledgeSelection.current ?? [],
+  );
   const [knowledgeSelectionReady, setKnowledgeSelectionReady] = useState(
-    () => pendingKnowledgeSelection.current !== null,
+    () => pendingKnowledgeSelection.current !== null || pendingAssistantKnowledgeSelection.current !== null,
   );
   const [knowledgeSelectionLoading, setKnowledgeSelectionLoading] = useState(false);
   const knowledgeSelectionThread = useRef<string | null>(null);
@@ -349,6 +375,10 @@ export default function AgentChatPage({
     setSelectedKnowledgeBaseIds(ids);
     setKnowledgeSelectionReady(true);
   }, []);
+  const changeAssistantKnowledgeSelection = useCallback((ids: string[]) => {
+    setSelectedAssistantKnowledgeBaseIds(ids);
+    setKnowledgeSelectionReady(true);
+  }, []);
 
   useEffect(() => {
     activeThreadIdRef.current = threadId;
@@ -358,17 +388,23 @@ export default function AgentChatPage({
     if (knowledgeSelectionAgent.current === agent_id) return;
     knowledgeSelectionAgent.current = agent_id;
     const pending = readPendingKnowledgeSelection(agent_id);
+    const pendingAssistant = readPendingAssistantKnowledgeSelection(agent_id);
     pendingKnowledgeSelection.current = pending;
+    pendingAssistantKnowledgeSelection.current = pendingAssistant;
     knowledgeSelectionThread.current = null;
     setSelectedKnowledgeBaseIds(pending ?? []);
-    setKnowledgeSelectionReady(pending !== null);
+    setSelectedAssistantKnowledgeBaseIds(pendingAssistant ?? []);
+    setKnowledgeSelectionReady(pending !== null || pendingAssistant !== null);
   }, [agent_id]);
 
   useEffect(() => {
     if (isNewThread) {
       if (knowledgeSelectionThread.current !== "__new__") {
         setSelectedKnowledgeBaseIds(pendingKnowledgeSelection.current ?? []);
-        setKnowledgeSelectionReady(pendingKnowledgeSelection.current !== null);
+        setSelectedAssistantKnowledgeBaseIds(pendingAssistantKnowledgeSelection.current ?? []);
+        setKnowledgeSelectionReady(
+          pendingKnowledgeSelection.current !== null || pendingAssistantKnowledgeSelection.current !== null,
+        );
       }
       knowledgeSelectionThread.current = "__new__";
       setKnowledgeSelectionLoading(false);
@@ -383,18 +419,29 @@ export default function AgentChatPage({
         if (knowledgeSelectionThread.current !== threadId) return;
         const metadata = record.metadata as Record<string, unknown> | undefined;
         const raw = metadata?.llmwiki_knowledge_base_ids;
+        const assistantRaw = metadata?.assistant_knowledge_base_ids;
+        const llmwikiIds = Array.isArray(raw)
+          ? raw.map((item) => String(item).trim()).filter(Boolean)
+          : [];
+        const assistantIds = Array.isArray(assistantRaw)
+          ? assistantRaw.map((item) => String(item).trim()).filter(Boolean)
+          : [];
         if (Array.isArray(raw)) {
-          setSelectedKnowledgeBaseIds(
-            raw.map((item) => String(item).trim()).filter(Boolean),
-          );
-          setKnowledgeSelectionReady(true);
+          setSelectedKnowledgeBaseIds(llmwikiIds);
         } else {
           setSelectedKnowledgeBaseIds([]);
-          setKnowledgeSelectionReady(false);
         }
+        if (Array.isArray(assistantRaw)) {
+          setSelectedAssistantKnowledgeBaseIds(assistantIds);
+        } else {
+          setSelectedAssistantKnowledgeBaseIds([]);
+        }
+        setKnowledgeSelectionReady(Array.isArray(raw) || Array.isArray(assistantRaw));
       })
       .catch(() => {
         if (knowledgeSelectionThread.current === threadId) {
+          setSelectedKnowledgeBaseIds([]);
+          setSelectedAssistantKnowledgeBaseIds([]);
           setKnowledgeSelectionReady(false);
         }
       })
@@ -417,10 +464,13 @@ export default function AgentChatPage({
     () => ({
       ...(taskId ? taskThreadMetadata(taskContext) : {}),
       ...(knowledgeSelectionReady
-        ? { llmwiki_knowledge_base_ids: selectedKnowledgeBaseIds }
+        ? {
+            llmwiki_knowledge_base_ids: selectedKnowledgeBaseIds,
+            assistant_knowledge_base_ids: selectedAssistantKnowledgeBaseIds,
+          }
         : {}),
     }),
-    [knowledgeSelectionReady, selectedKnowledgeBaseIds, taskContext, taskId],
+    [knowledgeSelectionReady, selectedAssistantKnowledgeBaseIds, selectedKnowledgeBaseIds, taskContext, taskId],
   );
   // 任务工作区（taskBind）：每次运行都带隐藏任务上下文（任务id/名称/内容），
   // 新建对话的消息历史里没有开场信息时模型也能拿到任务id（见 taskBindRunContext）。
@@ -436,7 +486,10 @@ export default function AgentChatPage({
       ...settings.context,
       agent_id,
       ...(knowledgeSelectionReady
-        ? { llmwiki_knowledge_base_ids: selectedKnowledgeBaseIds }
+        ? {
+            llmwiki_knowledge_base_ids: selectedKnowledgeBaseIds,
+            assistant_knowledge_base_ids: selectedAssistantKnowledgeBaseIds,
+          }
         : {}),
       ...(taskBindRunContextValue ?? {}),
     },
@@ -1021,9 +1074,13 @@ export default function AgentChatPage({
                               source="agent"
                               agentId={agent_id}
                               value={selectedKnowledgeBaseIds}
+                              assistantValue={selectedAssistantKnowledgeBaseIds}
                               onChange={changeKnowledgeSelection}
+                              onAssistantChange={changeAssistantKnowledgeSelection}
                               autoSelectAll={
-                                isNewThread && pendingKnowledgeSelection.current === null
+                                isNewThread &&
+                                pendingKnowledgeSelection.current === null &&
+                                pendingAssistantKnowledgeSelection.current === null
                               }
                               disabled={isDemoMode || knowledgeSelectionLoading}
                               label="知识空间"
@@ -1108,7 +1165,9 @@ export default function AgentChatPage({
                               source="agent"
                               agentId={agent_id}
                               value={selectedKnowledgeBaseIds}
+                              assistantValue={selectedAssistantKnowledgeBaseIds}
                               onChange={changeKnowledgeSelection}
+                              onAssistantChange={changeAssistantKnowledgeSelection}
                               disabled={isDemoMode || knowledgeSelectionLoading}
                               readOnly
                               label="知识空间"

frontend-web/src/pages/ArtifactLibraryPage.tsx

@@ -0,0 +1,559 @@
+"use client";
+
+import { useQuery } from "@tanstack/react-query";
+import {
+  ArrowDownWideNarrow,
+  Bot,
+  ChevronLeft,
+  ChevronRight,
+  Download,
+  FileText,
+  FileX2,
+  Library,
+  Loader2,
+  MessagesSquare,
+  MessageSquareShare,
+  RefreshCw,
+  Search,
+} from "lucide-react";
+import Link from "next/link";
+import { useEffect, useMemo, useRef, useState } from "react";
+import { toast } from "sonner";
+
+import { Button } from "@/components/ui/button";
+import { Input } from "@/components/ui/input";
+import { ScrollArea } from "@/components/ui/scroll-area";
+import {
+  Select,
+  SelectContent,
+  SelectItem,
+  SelectTrigger,
+  SelectValue,
+} from "@/components/ui/select";
+import { Skeleton } from "@/components/ui/skeleton";
+import {
+  WorkspaceBody,
+  WorkspaceContainer,
+  WorkspaceHeader,
+} from "@/components/workspace/workspace-container";
+import {
+  downloadArtifactFile,
+  downloadArtifactMarkdownAsWord,
+} from "@/core/artifacts/download";
+import {
+  fetchArtifactLibrary,
+  type ArtifactLibraryItem,
+  type ArtifactLibraryKind,
+  type ArtifactLibrarySource,
+} from "@/core/artifacts/library";
+import { useI18n } from "@/core/i18n/hooks";
+import { bootstrapResumePath, pathOfThread } from "@/core/threads/utils";
+import { formatDateTime } from "@/core/utils/datetime";
+import {
+  getFileExtensionDisplayName,
+  getFileIcon,
+} from "@/core/utils/files";
+import { desensitize } from "@/lib/desensitize";
+import { cn } from "@/lib/utils";
+
+const PAGE_SIZE = 50;
+
+const FILTERS: Array<{ value: ArtifactLibraryKind; label: string }> = [
+  { value: "all", label: "全部" },
+  { value: "document", label: "文档" },
+  { value: "spreadsheet", label: "表格" },
+  { value: "text_data", label: "文本与数据" },
+  { value: "archive", label: "压缩包" },
+  { value: "other", label: "其他" },
+];
+
+const SOURCE_FILTERS: Array<{
+  value: ArtifactLibrarySource;
+  label: string;
+}> = [
+  { value: "all", label: "全部来源" },
+  { value: "general_qa", label: "通用问答" },
+  { value: "agent", label: "智能体" },
+];
+
+type DownloadFormat = "original" | "word";
+
+function isMarkdownArtifact(name: string): boolean {
+  return /\.(md|markdown)$/i.test(name);
+}
+
+function artifactDownloadKey(
+  item: ArtifactLibraryItem,
+  format: DownloadFormat,
+): string {
+  return `${item.threadId}:${item.path}:${format}`;
+}
+
+function formatBytes(bytes: number): string {
+  if (!Number.isFinite(bytes) || bytes <= 0) return "0 B";
+  const units = ["B", "KB", "MB", "GB"];
+  const index = Math.min(
+    Math.floor(Math.log(bytes) / Math.log(1024)),
+    units.length - 1,
+  );
+  const value = bytes / 1024 ** index;
+  const digits = index === 0 || value >= 100 ? 0 : value >= 10 ? 1 : 2;
+  return `${value.toFixed(digits)} ${units[index]}`;
+}
+
+function sourceConversationPath(item: ArtifactLibraryItem): string {
+  const basePath =
+    item.threadType === "bootstrap"
+      ? bootstrapResumePath(item.threadId)
+      : pathOfThread({
+          thread_id: item.threadId,
+          metadata: item.agentId ? { agent_id: item.agentId } : undefined,
+        });
+  const search = new URLSearchParams({ artifact: item.path });
+  return `${basePath}?${search.toString()}`;
+}
+
+function FileRow({
+  item,
+  downloading,
+  onDownload,
+}: {
+  item: ArtifactLibraryItem;
+  downloading: DownloadFormat | null;
+  onDownload: (item: ArtifactLibraryItem, format: DownloadFormat) => void;
+}) {
+  const sourcePath = sourceConversationPath(item);
+  const displayName = desensitize(item.name);
+  const sourceTitle = desensitize(item.threadTitle || "新对话");
+  const sourceLabel = item.sourceType === "agent" ? "智能体" : "通用问答";
+  const sourceName =
+    item.sourceType === "agent"
+      ? desensitize(item.agentName || item.agentId || "未知智能体")
+      : sourceLabel;
+  const SourceIcon = item.sourceType === "agent" ? Bot : MessagesSquare;
+  const busy = downloading !== null;
+
+  return (
+    <tr className="group border-b transition-colors hover:bg-muted/35">
+      <td className="min-w-0 py-3 pr-4 pl-1">
+        <Link
+          href={sourcePath}
+          className="flex min-w-0 items-center gap-3 rounded-md outline-none focus-visible:ring-2 focus-visible:ring-ring"
+          title={`打开原对话并查看 ${displayName}`}
+        >
+          <span className="flex size-10 shrink-0 items-center justify-center rounded-lg bg-muted text-foreground/75">
+            {getFileIcon(item.name, "size-5")}
+          </span>
+          <span className="min-w-0">
+            <span className="block truncate text-sm font-medium text-foreground group-hover:text-primary">
+              {displayName}
+            </span>
+            <span className="mt-1 block truncate text-xs text-muted-foreground lg:hidden">
+              {getFileExtensionDisplayName(item.name)} · {sourceName} · 来自「
+              {sourceTitle}」
+            </span>
+            <span className="mt-1 hidden text-xs text-muted-foreground lg:block">
+              {getFileExtensionDisplayName(item.name)} 文件
+            </span>
+          </span>
+        </Link>
+      </td>
+      <td className="hidden min-w-0 px-3 py-3 lg:table-cell">
+        <div className="flex min-w-0 items-center gap-2.5">
+          <span className="flex size-8 shrink-0 items-center justify-center rounded-md bg-muted text-muted-foreground">
+            <SourceIcon className="size-4" />
+          </span>
+          <span className="min-w-0">
+            <span className="block truncate text-sm font-medium text-foreground">
+              {sourceName}
+            </span>
+            <Link
+              href={sourcePath}
+              className="block truncate text-xs text-muted-foreground underline-offset-4 hover:text-foreground hover:underline"
+              title={`打开原对话：${sourceTitle}`}
+            >
+              {item.sourceType === "agent" ? "智能体 · " : ""}
+              {sourceTitle}
+            </Link>
+          </span>
+        </div>
+      </td>
+      <td className="whitespace-nowrap px-3 py-3 text-sm text-muted-foreground">
+        {formatDateTime(item.modifiedAt)}
+      </td>
+      <td className="hidden whitespace-nowrap px-3 py-3 text-sm text-muted-foreground md:table-cell">
+        {formatBytes(item.sizeBytes)}
+      </td>
+      <td className="py-3 pr-1 pl-3 text-right">
+        <div className="flex items-center justify-end gap-1">
+          <Button
+            type="button"
+            size="icon"
+            variant="ghost"
+            className="size-8"
+            disabled={busy}
+            onClick={() => onDownload(item, "original")}
+            title={`下载原文件：${displayName}`}
+            aria-label={`下载原文件：${displayName}`}
+          >
+            {downloading === "original" ? (
+              <Loader2 className="size-4 animate-spin" />
+            ) : (
+              <Download className="size-4" />
+            )}
+          </Button>
+          {isMarkdownArtifact(item.name) && (
+            <Button
+              type="button"
+              size="sm"
+              variant="ghost"
+              className="h-8 gap-1 px-2 text-xs"
+              disabled={busy}
+              onClick={() => onDownload(item, "word")}
+              title={`下载为 Word：${displayName}`}
+              aria-label={`下载为 Word：${displayName}`}
+            >
+              {downloading === "word" ? (
+                <Loader2 className="size-4 animate-spin" />
+              ) : (
+                <FileText className="size-4" />
+              )}
+              Word
+            </Button>
+          )}
+          <Button
+            asChild
+            size="icon"
+            variant="ghost"
+            className="size-8"
+            title={`打开原对话：${sourceTitle}`}
+          >
+            <Link href={sourcePath} aria-label={`打开原对话：${sourceTitle}`}>
+              <MessageSquareShare className="size-4" />
+            </Link>
+          </Button>
+        </div>
+      </td>
+    </tr>
+  );
+}
+
+function LoadingRows() {
+  return Array.from({ length: 5 }, (_, index) => (
+    <tr className="border-b" key={index}>
+      <td className="py-3 pr-4 pl-1">
+        <div className="flex items-center gap-3">
+          <Skeleton className="size-10 shrink-0" />
+          <div className="min-w-0 flex-1 space-y-2">
+            <Skeleton className="h-4 w-2/3" />
+            <Skeleton className="h-3 w-1/3" />
+          </div>
+        </div>
+      </td>
+      <td className="hidden px-3 lg:table-cell"><Skeleton className="h-4 w-3/4" /></td>
+      <td className="px-3"><Skeleton className="h-4 w-24" /></td>
+      <td className="hidden px-3 md:table-cell"><Skeleton className="h-4 w-16" /></td>
+      <td className="pr-1 pl-3"><Skeleton className="ml-auto size-8" /></td>
+    </tr>
+  ));
+}
+
+export default function ArtifactLibraryPage() {
+  const { t } = useI18n();
+  const [search, setSearch] = useState("");
+  const [debouncedSearch, setDebouncedSearch] = useState("");
+  const [fileKind, setFileKind] = useState<ArtifactLibraryKind>("all");
+  const [sourceType, setSourceType] = useState<ArtifactLibrarySource>("all");
+  const [page, setPage] = useState(1);
+  const [downloadingKey, setDownloadingKey] = useState<string | null>(null);
+  const forceRefreshRef = useRef(false);
+
+  useEffect(() => {
+    document.title = `资料库 - ${t.pages.appName}`;
+  }, [t.pages.appName]);
+
+  useEffect(() => {
+    const timer = window.setTimeout(() => setDebouncedSearch(search.trim()), 300);
+    return () => window.clearTimeout(timer);
+  }, [search]);
+
+  useEffect(() => {
+    setPage(1);
+  }, [debouncedSearch, fileKind, sourceType]);
+
+  const offset = (page - 1) * PAGE_SIZE;
+  const libraryQuery = useQuery({
+    queryKey: ["artifact-library", debouncedSearch, fileKind, sourceType, page],
+    queryFn: async ({ signal }) => {
+      const refresh = forceRefreshRef.current;
+      try {
+        return await fetchArtifactLibrary({
+          query: debouncedSearch,
+          fileKind,
+          sourceType,
+          offset,
+          limit: PAGE_SIZE,
+          refresh,
+          signal,
+        });
+      } finally {
+        forceRefreshRef.current = false;
+      }
+    },
+    retry: false,
+  });
+
+  const total = libraryQuery.data?.total ?? 0;
+  const items = libraryQuery.data?.items ?? [];
+  const totalPages = Math.max(1, Math.ceil(total / PAGE_SIZE));
+  const hasPrevious = page > 1;
+  const hasNext = page < totalPages;
+
+  useEffect(() => {
+    if (page > totalPages) setPage(totalPages);
+  }, [page, totalPages]);
+
+  const itemKeys = useMemo(
+    () =>
+      new Set(
+        items.flatMap((item) => [
+          artifactDownloadKey(item, "original"),
+          artifactDownloadKey(item, "word"),
+        ]),
+      ),
+    [items],
+  );
+  useEffect(() => {
+    if (downloadingKey && !itemKeys.has(downloadingKey)) setDownloadingKey(null);
+  }, [downloadingKey, itemKeys]);
+
+  const handleDownload = async (
+    item: ArtifactLibraryItem,
+    format: DownloadFormat,
+  ) => {
+    const key = artifactDownloadKey(item, format);
+    if (downloadingKey) return;
+    setDownloadingKey(key);
+    try {
+      if (format === "word") {
+        await downloadArtifactMarkdownAsWord({
+          filepath: item.path,
+          threadId: item.threadId,
+        });
+        toast.success("Word 文件已生成");
+      } else {
+        await downloadArtifactFile({
+          filepath: item.path,
+          threadId: item.threadId,
+        });
+      }
+    } catch (error) {
+      console.error(error);
+      toast.error(error instanceof Error ? error.message : "下载失败");
+    } finally {
+      setDownloadingKey(null);
+    }
+  };
+
+  const handleRefresh = () => {
+    forceRefreshRef.current = true;
+    void libraryQuery.refetch();
+  };
+
+  return (
+    <WorkspaceContainer>
+      <WorkspaceHeader />
+      <WorkspaceBody className="items-stretch">
+        <div className="flex min-h-0 w-full flex-1 flex-col">
+          <header className="shrink-0 border-b bg-card/40 px-5 py-6 sm:px-8">
+            <div className="mx-auto flex w-full max-w-6xl flex-col gap-5">
+              <div className="flex flex-col justify-between gap-4 sm:flex-row sm:items-start">
+                <div>
+                  <div className="flex items-center gap-2">
+                    <Library className="size-6 text-primary" />
+                    <h1 className="text-2xl font-semibold tracking-tight">资料库</h1>
+                  </div>
+                  <p className="mt-2 text-sm text-muted-foreground">
+                    汇总当前账号在对话中生成的文件，不包含上传文件、图片、音频和视频。
+                  </p>
+                </div>
+                <Button
+                  type="button"
+                  variant="outline"
+                  size="sm"
+                  onClick={handleRefresh}
+                  disabled={libraryQuery.isFetching}
+                >
+                  <RefreshCw className={cn("size-4", libraryQuery.isFetching && "animate-spin")} />
+                  刷新
+                </Button>
+              </div>
+
+              <div className="relative max-w-xl">
+                <Search className="pointer-events-none absolute top-1/2 left-3 size-4 -translate-y-1/2 text-muted-foreground" />
+                <Input
+                  type="search"
+                  className="h-11 pl-9"
+                  placeholder="搜索文件名、来源对话或智能体名称"
+                  value={search}
+                  onChange={(event) => setSearch(event.target.value)}
+                />
+              </div>
+
+              <div className="flex flex-wrap items-center justify-between gap-4">
+                <div className="flex flex-wrap items-center gap-3">
+                  <div className="flex flex-wrap gap-1" aria-label="文件类型筛选">
+                    {FILTERS.map((filter) => (
+                      <Button
+                        key={filter.value}
+                        type="button"
+                        size="sm"
+                        variant={fileKind === filter.value ? "secondary" : "ghost"}
+                        aria-pressed={fileKind === filter.value}
+                        onClick={() => setFileKind(filter.value)}
+                      >
+                        {filter.label}
+                      </Button>
+                    ))}
+                  </div>
+                  <Select
+                    value={sourceType}
+                    onValueChange={(value) =>
+                      setSourceType(value as ArtifactLibrarySource)
+                    }
+                  >
+                    <SelectTrigger
+                      size="sm"
+                      className="w-32"
+                      aria-label="筛选文件来源"
+                    >
+                      <SelectValue />
+                    </SelectTrigger>
+                    <SelectContent>
+                      {SOURCE_FILTERS.map((filter) => (
+                        <SelectItem key={filter.value} value={filter.value}>
+                          {filter.label}
+                        </SelectItem>
+                      ))}
+                    </SelectContent>
+                  </Select>
+                </div>
+                <div className="flex items-center gap-2 text-xs text-muted-foreground">
+                  <ArrowDownWideNarrow className="size-4" />
+                  最近生成
+                </div>
+              </div>
+            </div>
+          </header>
+
+          <ScrollArea className="min-h-0 flex-1">
+            <main className="mx-auto w-full max-w-6xl px-5 py-5 sm:px-8">
+              <div className="mb-2 text-xs text-muted-foreground tabular-nums">
+                {libraryQuery.isLoading ? "正在统计文件…" : `共 ${total} 个文件`}
+              </div>
+
+              <div className="overflow-x-auto">
+                <table className="w-full table-fixed border-collapse">
+                  <thead>
+                    <tr className="border-b text-left text-xs font-normal text-muted-foreground">
+                      <th className="w-[48%] py-3 pr-4 pl-1 font-normal lg:w-[35%]">文件名</th>
+                      <th className="hidden w-[25%] px-3 py-3 font-normal lg:table-cell">来源</th>
+                      <th className="w-[34%] px-3 py-3 font-normal md:w-[22%] lg:w-[16%]">生成时间</th>
+                      <th className="hidden w-[13%] px-3 py-3 font-normal lg:w-[10%] md:table-cell">大小</th>
+                      <th className="w-[18%] py-3 pr-1 pl-3 text-right font-normal md:w-[17%] lg:w-[14%]">
+                        <span className="sr-only">操作</span>
+                      </th>
+                    </tr>
+                  </thead>
+                  <tbody>
+                    {libraryQuery.isLoading ? (
+                      <LoadingRows />
+                    ) : (
+                      items.map((item) => {
+                        const key = `${item.threadId}:${item.path}`;
+                        const activeDownload = downloadingKey?.startsWith(`${key}:`)
+                          ? downloadingKey.endsWith(":word")
+                            ? "word"
+                            : "original"
+                          : null;
+                        return (
+                          <FileRow
+                            key={key}
+                            item={item}
+                            downloading={activeDownload}
+                            onDownload={(target, format) =>
+                              void handleDownload(target, format)
+                            }
+                          />
+                        );
+                      })
+                    )}
+                  </tbody>
+                </table>
+              </div>
+
+              {!libraryQuery.isLoading && libraryQuery.isError && (
+                <div className="flex flex-col items-center gap-3 py-16 text-center">
+                  <FileX2 className="size-9 text-muted-foreground" />
+                  <div>
+                    <p className="text-sm font-medium">资料库加载失败</p>
+                    <p className="mt-1 text-xs text-muted-foreground">
+                      {libraryQuery.error instanceof Error
+                        ? libraryQuery.error.message
+                        : "请稍后重试"}
+                    </p>
+                  </div>
+                  <Button variant="outline" size="sm" onClick={handleRefresh}>
+                    重新加载
+                  </Button>
+                </div>
+              )}
+
+              {!libraryQuery.isLoading && !libraryQuery.isError && items.length === 0 && (
+                <div className="flex flex-col items-center gap-3 py-20 text-center">
+                  <FileX2 className="size-9 text-muted-foreground" />
+                  <div>
+                    <p className="text-sm font-medium">
+                      {debouncedSearch || fileKind !== "all" || sourceType !== "all"
+                        ? "没有找到匹配的文件"
+                        : "还没有生成文件"}
+                    </p>
+                    <p className="mt-1 text-xs text-muted-foreground">
+                      对话中生成的文档、表格和数据文件会自动出现在这里。
+                    </p>
+                  </div>
+                </div>
+              )}
+
+              {!libraryQuery.isLoading && !libraryQuery.isError && totalPages > 1 && (
+                <div className="flex items-center justify-center gap-3 py-7">
+                  <Button
+                    variant="outline"
+                    size="sm"
+                    disabled={!hasPrevious}
+                    onClick={() => setPage((current) => Math.max(1, current - 1))}
+                  >
+                    <ChevronLeft className="size-4" />
+                    上一页
+                  </Button>
+                  <span className="text-sm text-muted-foreground tabular-nums">
+                    {page} / {totalPages}
+                  </span>
+                  <Button
+                    variant="outline"
+                    size="sm"
+                    disabled={!hasNext}
+                    onClick={() => setPage((current) => current + 1)}
+                  >
+                    下一页
+                    <ChevronRight className="size-4" />
+                  </Button>
+                </div>
+              )}
+            </main>
+          </ScrollArea>
+        </div>
+      </WorkspaceBody>
+    </WorkspaceContainer>
+  );
+}

frontend-web/src/pages/AssistantKnowledgePage.tsx

@@ -0,0 +1,448 @@
+import { BookOpen, Database, Download, Loader2, Plus, RefreshCw, Search, ShieldCheck, Trash2, Upload } from "lucide-react";
+import { useEffect, useMemo, useRef, useState } from "react";
+import { toast } from "sonner";
+
+import { Badge } from "@/components/ui/badge";
+import { Button } from "@/components/ui/button";
+import {
+  Dialog,
+  DialogContent,
+  DialogDescription,
+  DialogFooter,
+  DialogHeader,
+  DialogTitle,
+} from "@/components/ui/dialog";
+import { Input } from "@/components/ui/input";
+import { ScrollArea } from "@/components/ui/scroll-area";
+import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs";
+import { Textarea } from "@/components/ui/textarea";
+import { KnowledgeProductNav } from "@/components/workspace/knowledge-product-nav";
+import { MarkdownContent } from "@/components/workspace/messages/markdown-content";
+import { getStoredAuth } from "@/core/auth";
+import {
+  type AssistantKnowledgeBase,
+  type AssistantWikiPage,
+  useAssistantImportJobs,
+  useAssistantKnowledgeActions,
+  useAssistantKnowledgeBases,
+  useAssistantKnowledgeSources,
+  useAssistantWikiPages,
+} from "@/strategy-components/api/assistant-knowledge";
+
+function baseSubtitle(base: AssistantKnowledgeBase) {
+  if (base.is_global) return "全库沉淀总库 · 所有 WeKnora 导入数据都会同步进入这里并做实体对齐";
+  return base.description || "自建助手知识库 · 可上传普通知识库导出的 Wiki 向量包";
+}
+
+function EmptyInitializeCard({
+  isAdmin,
+  onInitialize,
+  pending,
+}: {
+  isAdmin: boolean;
+  onInitialize: () => void;
+  pending: boolean;
+}) {
+  return (
+    <div className="flex flex-1 items-center justify-center p-8">
+      <div className="max-w-lg rounded-xl border bg-card p-8 text-center">
+        <Database className="mx-auto mb-4 size-10 text-primary" />
+        <h1 className="text-xl font-semibold">初始化助手知识库</h1>
+        <p className="mt-2 text-sm text-muted-foreground">
+          未配置 WeKnora 时会默认展示助手知识库。首次使用需要创建“知识梳理总库”，后续从 WeKnora 导入的数据都会沉淀到这里。
+        </p>
+        {isAdmin ? (
+          <Button className="mt-5" disabled={pending} onClick={onInitialize}>
+            {pending ? <Loader2 className="mr-2 size-4 animate-spin" /> : <Database className="mr-2 size-4" />}
+            创建知识梳理总库
+          </Button>
+        ) : (
+          <p className="mt-5 text-sm">请联系管理员初始化。</p>
+        )}
+      </div>
+    </div>
+  );
+}
+
+export default function AssistantKnowledgePage() {
+  const isAdmin = getStoredAuth()?.system_role === "admin";
+  const [query, setQuery] = useState("");
+  const [selectedBaseId, setSelectedBaseId] = useState<string | null>(null);
+  const [selected, setSelected] = useState<AssistantWikiPage | null>(null);
+  const [createOpen, setCreateOpen] = useState(false);
+  const [createDraft, setCreateDraft] = useState({ name: "", description: "" });
+  const packageInputRef = useRef<HTMLInputElement | null>(null);
+  const bases = useAssistantKnowledgeBases();
+  const actions = useAssistantKnowledgeActions();
+
+  useEffect(() => {
+    const values = bases.data ?? [];
+    if (!values.length) {
+      setSelectedBaseId(null);
+      return;
+    }
+    if (selectedBaseId && values.some((base) => base.id === selectedBaseId)) return;
+    setSelectedBaseId((values.find((base) => base.is_global) ?? values[0]).id);
+  }, [bases.data, selectedBaseId]);
+
+  useEffect(() => {
+    setSelected(null);
+  }, [selectedBaseId]);
+
+  const selectedBase = useMemo(
+    () => (bases.data ?? []).find((base) => base.id === selectedBaseId) ?? null,
+    [bases.data, selectedBaseId],
+  );
+  const pages = useAssistantWikiPages(query, selectedBaseId);
+  const sources = useAssistantKnowledgeSources(selectedBaseId);
+  const jobs = useAssistantImportJobs(selectedBaseId);
+  const activePage = selected ?? pages.data?.[0] ?? null;
+  const counts = useMemo(
+    () => ({
+      pages: pages.data?.length ?? selectedBase?.page_count ?? 0,
+      sources: sources.data?.length ?? selectedBase?.source_count ?? 0,
+      jobs: jobs.data?.length ?? 0,
+    }),
+    [jobs.data, pages.data, selectedBase?.page_count, selectedBase?.source_count, sources.data],
+  );
+
+  const initializeGlobal = () => {
+    actions.initialize.mutate("知识梳理总库", {
+      onSuccess: (base) => {
+        setSelectedBaseId(base.id);
+        toast.success("知识梳理总库已创建");
+      },
+      onError: (error) => toast.error(error.message),
+    });
+  };
+
+  const createBase = async () => {
+    const name = createDraft.name.trim();
+    if (!name) {
+      toast.error("请输入助手知识库名称");
+      return;
+    }
+    try {
+      const base = await actions.createBase.mutateAsync({
+        name,
+        description: createDraft.description.trim(),
+      });
+      setSelectedBaseId(base.id);
+      setCreateDraft({ name: "", description: "" });
+      setCreateOpen(false);
+      toast.success("助手知识库已创建");
+    } catch (error) {
+      toast.error(error instanceof Error ? error.message : String(error));
+    }
+  };
+
+  const uploadPackage = async (file: File | null | undefined) => {
+    if (!file) return;
+    try {
+      await actions.uploadWeKnoraPackage.mutateAsync({
+        file,
+        assistantBaseId: selectedBaseId,
+      });
+      toast.success("导出包已上传，后台正在异步导入");
+    } catch (error) {
+      toast.error(error instanceof Error ? error.message : String(error));
+    } finally {
+      if (packageInputRef.current) packageInputRef.current.value = "";
+    }
+  };
+
+  if (bases.isLoading) {
+    return (
+      <div className="flex size-full flex-col">
+        <KnowledgeProductNav />
+        <div className="flex flex-1 items-center justify-center">
+          <Loader2 className="animate-spin" />
+        </div>
+      </div>
+    );
+  }
+
+  if (!bases.data?.length) {
+    return (
+      <div className="flex size-full flex-col">
+        <KnowledgeProductNav />
+        <EmptyInitializeCard
+          isAdmin={isAdmin}
+          pending={actions.initialize.isPending}
+          onInitialize={initializeGlobal}
+        />
+      </div>
+    );
+  }
+
+  return (
+    <div className="flex size-full min-h-0 flex-col bg-background">
+      <KnowledgeProductNav />
+      <div className="grid min-h-0 flex-1 grid-cols-[280px_1fr]">
+        <aside className="flex min-h-0 flex-col border-r bg-muted/20">
+          <div className="border-b p-4">
+            <div className="flex items-center justify-between">
+              <div>
+                <h2 className="text-sm font-semibold">助手知识库</h2>
+                <p className="mt-1 text-xs text-muted-foreground">总库置顶，自建库可上传 WeKnora 导出包</p>
+              </div>
+              {isAdmin && (
+                <Button size="icon" variant="outline" onClick={() => setCreateOpen(true)}>
+                  <Plus className="size-4" />
+                </Button>
+              )}
+            </div>
+          </div>
+          <ScrollArea className="min-h-0 flex-1">
+            <div className="space-y-2 p-3">
+              {(bases.data ?? []).map((base) => (
+                <button
+                  key={base.id}
+                  type="button"
+                  onClick={() => setSelectedBaseId(base.id)}
+                  className={`w-full rounded-lg border px-3 py-3 text-left transition ${
+                    selectedBaseId === base.id ? "border-primary bg-primary/10 text-primary" : "bg-card hover:bg-muted"
+                  }`}
+                >
+                  <div className="flex items-center gap-2">
+                    <Database className="size-4 shrink-0" />
+                    <span className="min-w-0 flex-1 truncate text-sm font-medium">{base.name}</span>
+                    {base.is_global && <Badge variant="outline" className="text-[10px]">总库</Badge>}
+                  </div>
+                  <div className="mt-2 flex gap-3 text-[11px] text-muted-foreground">
+                    <span>{base.page_count ?? 0} 页</span>
+                    <span>{base.source_count ?? 0} 来源</span>
+                    <span>{base.entity_count ?? 0} 实体</span>
+                  </div>
+                </button>
+              ))}
+            </div>
+          </ScrollArea>
+        </aside>
+
+        <main className="flex min-h-0 flex-col">
+          <header className="flex items-center gap-4 border-b px-6 py-4">
+            <div className="min-w-0">
+              <h1 className="truncate text-xl font-semibold">{selectedBase?.name}</h1>
+              <p className="mt-1 truncate text-xs text-muted-foreground">
+                {selectedBase ? baseSubtitle(selectedBase) : "请选择助手知识库"}
+              </p>
+            </div>
+            <Badge variant="outline">
+              <ShieldCheck className="mr-1 size-3" />
+              实体对齐
+            </Badge>
+            {isAdmin && (
+              <div className="ml-auto flex items-center gap-2">
+                <input
+                  ref={packageInputRef}
+                  type="file"
+                  accept=".jsonl,.ndjson,application/x-ndjson,application/json"
+                  className="hidden"
+                  onChange={(event) => void uploadPackage(event.target.files?.[0])}
+                />
+                <Button
+                  variant="outline"
+                  disabled={actions.uploadWeKnoraPackage.isPending || !selectedBaseId}
+                  onClick={() => packageInputRef.current?.click()}
+                >
+                  {actions.uploadWeKnoraPackage.isPending ? (
+                    <Loader2 className="mr-2 size-4 animate-spin" />
+                  ) : (
+                    <Upload className="mr-2 size-4" />
+                  )}
+                  上传 WeKnora 导出包
+                </Button>
+              </div>
+            )}
+          </header>
+
+          <Tabs defaultValue="wiki" className="flex min-h-0 flex-1 flex-col">
+            <div className="flex items-center gap-4 border-b px-6">
+              <TabsList className="my-2">
+                <TabsTrigger value="wiki">Wiki（{counts.pages}）</TabsTrigger>
+                <TabsTrigger value="sources">来源（{counts.sources}）</TabsTrigger>
+                <TabsTrigger value="jobs">导入记录（{counts.jobs}）</TabsTrigger>
+              </TabsList>
+              <div className="relative ml-auto w-72">
+                <Search className="absolute left-2.5 top-1/2 size-4 -translate-y-1/2 text-muted-foreground" />
+                <Input
+                  value={query}
+                  onChange={(event) => setQuery(event.target.value)}
+                  placeholder="搜索标题、实体别名和正文"
+                  className="pl-8"
+                />
+              </div>
+            </div>
+
+            <TabsContent value="wiki" className="mt-0 grid min-h-0 flex-1 grid-cols-[300px_1fr]">
+              <ScrollArea className="border-r">
+                <div className="space-y-1 p-3">
+                  {pages.isLoading && <Loader2 className="m-4 size-5 animate-spin" />}
+                  {pages.data?.map((page) => (
+                    <button
+                      key={page.id}
+                      type="button"
+                      onClick={() => setSelected(page)}
+                      className={`w-full rounded-md px-3 py-2 text-left ${
+                        activePage?.id === page.id ? "bg-primary/10 text-primary" : "hover:bg-muted"
+                      }`}
+                    >
+                      <div className="truncate text-sm font-medium">{page.title}</div>
+                      <div className="mt-1 flex items-center gap-2 text-[11px] text-muted-foreground">
+                        <span>{page.page_type}</span>
+                        <span>{new Date(page.updated_at).toLocaleString()}</span>
+                      </div>
+                    </button>
+                  ))}
+                  {!pages.isLoading && !pages.data?.length && (
+                    <div className="p-4 text-xs text-muted-foreground">
+                      暂无 Wiki 页面。请先到普通知识库详情页下载向量化数据包，再在当前助手知识库上传。
+                    </div>
+                  )}
+                </div>
+              </ScrollArea>
+              <ScrollArea>
+                <article className="mx-auto max-w-5xl p-8">
+                  {activePage ? (
+                    <>
+                      <h1 className="mb-1 flex items-center gap-2 text-2xl font-semibold">
+                        <BookOpen className="size-5 text-primary" />
+                        {activePage.title}
+                      </h1>
+                      <p className="mb-6 text-xs text-muted-foreground">{activePage.slug}</p>
+                      <MarkdownContent content={activePage.content_markdown} isLoading={false} rehypePlugins={[]} />
+                    </>
+                  ) : (
+                    <p className="text-sm text-muted-foreground">暂无 Wiki 页面。</p>
+                  )}
+                </article>
+              </ScrollArea>
+            </TabsContent>
+
+            <TabsContent value="sources" className="mt-0 min-h-0 flex-1 overflow-auto p-6">
+              <div className="grid gap-3 md:grid-cols-2 xl:grid-cols-3">
+                {sources.data?.map((source) => (
+                  <div key={source.id} className="rounded-lg border bg-card p-4">
+                    <div className="flex items-center gap-2">
+                      <Database className="size-4" />
+                      <span className="truncate font-medium">{source.source_name}</span>
+                      <Badge variant="outline" className="ml-auto">{source.source_type}</Badge>
+                    </div>
+                    <p className="mt-3 text-xs text-muted-foreground">业务键：{source.source_key}</p>
+                    {isAdmin && (
+                      <div className="mt-4 flex justify-end gap-2">
+                        <Button
+                          size="sm"
+                          variant="outline"
+                          disabled={source.source_type !== "weknora" || actions.reimportSource.isPending}
+                          onClick={() =>
+                            actions.reimportSource.mutate(source.id, {
+                              onSuccess: () => toast.success("已发起重新导入"),
+                              onError: (error) => toast.error(error.message),
+                            })
+                          }
+                        >
+                          <RefreshCw className="mr-2 size-4" />
+                          重新导入
+                        </Button>
+                        <Button
+                          size="sm"
+                          variant="destructive"
+                          disabled={actions.deleteSource.isPending}
+                          onClick={() => {
+                            if (window.confirm(`确认删除来源“${source.source_name}”的贡献？仍被其他来源引用的知识会保留。`)) {
+                              actions.deleteSource.mutate(source.id, {
+                                onSuccess: () => toast.success("来源贡献已删除"),
+                                onError: (error) => toast.error(error.message),
+                              });
+                            }
+                          }}
+                        >
+                          <Trash2 className="mr-2 size-4" />
+                          删除来源
+                        </Button>
+                      </div>
+                    )}
+                  </div>
+                ))}
+                {!sources.data?.length && <p className="text-sm text-muted-foreground">暂无来源。</p>}
+              </div>
+            </TabsContent>
+
+            <TabsContent value="jobs" className="mt-0 min-h-0 flex-1 overflow-auto p-6">
+              <div className="space-y-3">
+                {jobs.data?.map((job) => (
+                  <div key={job.id} className="rounded-lg border bg-card p-4">
+                    <div className="flex items-center gap-3">
+                      <Badge>{job.status}</Badge>
+                      <span className="text-sm">{job.trigger}</span>
+                      <span className="text-xs text-muted-foreground">阶段：{job.phase}</span>
+                      <span className="ml-auto text-xs">{new Date(job.created_at).toLocaleString()}</span>
+                      {isAdmin && job.package_download_url && (
+                        <Button asChild size="sm" variant="outline">
+                          <a href={job.package_download_url} target="_blank" rel="noreferrer">
+                            <Download className="mr-2 size-4" />
+                            下载导出包
+                          </a>
+                        </Button>
+                      )}
+                    </div>
+                    <div className="mt-2 text-xs text-muted-foreground">
+                      {Object.entries(job.counts || {}).map(([key, value]) => `${key} ${value}`).join(" · ") || "暂无统计"}
+                    </div>
+                    {job.last_error_message && (
+                      <div className="mt-2 rounded-md bg-destructive/10 px-3 py-2 text-xs text-destructive">
+                        {job.last_error_message}
+                      </div>
+                    )}
+                  </div>
+                ))}
+                {!jobs.data?.length && <p className="text-sm text-muted-foreground">暂无导入记录。</p>}
+              </div>
+            </TabsContent>
+          </Tabs>
+        </main>
+      </div>
+
+      <Dialog open={createOpen} onOpenChange={setCreateOpen}>
+        <DialogContent>
+          <DialogHeader>
+            <DialogTitle>新建助手知识库</DialogTitle>
+            <DialogDescription>
+              自建助手知识库用于承接从 WeKnora 普通知识库下载的 Wiki 向量包；导入时同一份数据也会沉淀到“知识梳理总库”。
+            </DialogDescription>
+          </DialogHeader>
+          <div className="space-y-4">
+            <div className="space-y-1.5">
+              <label className="text-sm font-medium">名称</label>
+              <Input
+                value={createDraft.name}
+                onChange={(event) => setCreateDraft((prev) => ({ ...prev, name: event.target.value }))}
+                placeholder="例如：政策资料库"
+              />
+            </div>
+            <div className="space-y-1.5">
+              <label className="text-sm font-medium">描述</label>
+              <Textarea
+                value={createDraft.description}
+                onChange={(event) => setCreateDraft((prev) => ({ ...prev, description: event.target.value }))}
+                placeholder="可选，用于说明这个助手知识库的范围"
+                rows={3}
+              />
+            </div>
+          </div>
+          <DialogFooter>
+            <Button variant="outline" onClick={() => setCreateOpen(false)}>
+              取消
+            </Button>
+            <Button disabled={actions.createBase.isPending} onClick={() => void createBase()}>
+              {actions.createBase.isPending ? <Loader2 className="mr-2 size-4 animate-spin" /> : <Plus className="mr-2 size-4" />}
+              创建
+            </Button>
+          </DialogFooter>
+        </DialogContent>
+      </Dialog>
+    </div>
+  );
+}

frontend-web/src/pages/ChatPage.tsx

@@ -142,6 +142,7 @@ export default function ChatPage() {
   // per-thread header toggle so stale localStorage can't re-enable it.
   const [knowledgeRagEnabled] = useKnowledgeRagEnabled();
   const [selectedKnowledgeBaseIds, setSelectedKnowledgeBaseIds] = useState<string[]>([]);
+  const [selectedAssistantKnowledgeBaseIds, setSelectedAssistantKnowledgeBaseIds] = useState<string[]>([]);
   const [knowledgeSelectionReady, setKnowledgeSelectionReady] = useState(false);
   const [knowledgeSelectionLoading, setKnowledgeSelectionLoading] = useState(false);
   const knowledgeSelectionThread = useRef<string | null>(null);
@@ -150,18 +151,26 @@ export default function ChatPage() {
     setSelectedKnowledgeBaseIds(ids);
     setKnowledgeSelectionReady(true);
   }, []);
+  const changeAssistantKnowledgeSelection = useCallback((ids: string[]) => {
+    setSelectedAssistantKnowledgeBaseIds(ids);
+    setKnowledgeSelectionReady(true);
+  }, []);
   const streamContext = useMemo(
     () => ({
       ...settings.context,
       knowledge_rag_enabled: knowledgeRagEnabled,
       ...(knowledgeSelectionReady
-        ? { llmwiki_knowledge_base_ids: selectedKnowledgeBaseIds }
+        ? {
+            llmwiki_knowledge_base_ids: selectedKnowledgeBaseIds,
+            assistant_knowledge_base_ids: selectedAssistantKnowledgeBaseIds,
+          }
         : {}),
     }),
     [
       settings.context,
       knowledgeRagEnabled,
       knowledgeSelectionReady,
+      selectedAssistantKnowledgeBaseIds,
       selectedKnowledgeBaseIds,
     ],
   );
@@ -200,6 +209,7 @@ export default function ChatPage() {
       setHasStartedConversation(false);
       setPromptPrefixOn(false);
       setSelectedKnowledgeBaseIds([]);
+      setSelectedAssistantKnowledgeBaseIds([]);
       setKnowledgeSelectionReady(false);
       setKnowledgeSelectionLoading(false);
       knowledgeSelectionThread.current = "__new__";
@@ -225,18 +235,29 @@ export default function ChatPage() {
         if (knowledgeSelectionThread.current !== threadId) return;
         const metadata = record.metadata as Record<string, unknown> | undefined;
         const raw = metadata?.llmwiki_knowledge_base_ids;
+        const assistantRaw = metadata?.assistant_knowledge_base_ids;
+        const llmwikiIds = Array.isArray(raw)
+          ? raw.map((item) => String(item).trim()).filter(Boolean)
+          : [];
+        const assistantIds = Array.isArray(assistantRaw)
+          ? assistantRaw.map((item) => String(item).trim()).filter(Boolean)
+          : [];
         if (Array.isArray(raw)) {
-          setSelectedKnowledgeBaseIds(
-            raw.map((item) => String(item).trim()).filter(Boolean),
-          );
-          setKnowledgeSelectionReady(true);
+          setSelectedKnowledgeBaseIds(llmwikiIds);
         } else {
           setSelectedKnowledgeBaseIds([]);
-          setKnowledgeSelectionReady(false);
         }
+        if (Array.isArray(assistantRaw)) {
+          setSelectedAssistantKnowledgeBaseIds(assistantIds);
+        } else {
+          setSelectedAssistantKnowledgeBaseIds([]);
+        }
+        setKnowledgeSelectionReady(Array.isArray(raw) || Array.isArray(assistantRaw));
       })
       .catch(() => {
         if (knowledgeSelectionThread.current === threadId) {
+          setSelectedKnowledgeBaseIds([]);
+          setSelectedAssistantKnowledgeBaseIds([]);
           setKnowledgeSelectionReady(false);
         }
       })
@@ -259,6 +280,7 @@ export default function ChatPage() {
     if (!known.some((a) => a.id === selectedAgentId)) {
       setSelectedAgentId(null);
       setSelectedKnowledgeBaseIds([]);
+      setSelectedAssistantKnowledgeBaseIds([]);
       setKnowledgeSelectionReady(false);
     }
   }, [selectedAgentId, selectorAgents, mineAgents, squareAgents, selectorLoading]);
@@ -269,7 +291,10 @@ export default function ChatPage() {
     threadId: isNewThread ? undefined : threadId,
     context: streamContext,
     threadMetadata: knowledgeSelectionReady
-      ? { llmwiki_knowledge_base_ids: selectedKnowledgeBaseIds }
+      ? {
+          llmwiki_knowledge_base_ids: selectedKnowledgeBaseIds,
+          assistant_knowledge_base_ids: selectedAssistantKnowledgeBaseIds,
+        }
       : undefined,
     isMock,
     onStart: (createdThreadId) => {
@@ -443,7 +468,10 @@ export default function ChatPage() {
               metadata: {
                 agent_id: selectedAgentId,
                 ...(knowledgeSelectionReady
-                  ? { llmwiki_knowledge_base_ids: selectedKnowledgeBaseIds }
+                  ? {
+                      llmwiki_knowledge_base_ids: selectedKnowledgeBaseIds,
+                      assistant_knowledge_base_ids: selectedAssistantKnowledgeBaseIds,
+                    }
                   : {}),
               },
             });
@@ -454,7 +482,10 @@ export default function ChatPage() {
                 threadId: created.thread_id,
                 text,
                 ...(knowledgeSelectionReady
-                  ? { knowledgeBaseIds: selectedKnowledgeBaseIds }
+                  ? {
+                      knowledgeBaseIds: selectedKnowledgeBaseIds,
+                      assistantKnowledgeBaseIds: selectedAssistantKnowledgeBaseIds,
+                    }
                   : {}),
               }),
             );
@@ -516,6 +547,7 @@ export default function ChatPage() {
       hideAdminPrompt,
       preferencesLoading,
       selectedAgentId,
+      selectedAssistantKnowledgeBaseIds,
       selectedKnowledgeBaseIds,
       knowledgeSelectionReady,
       agentHandoffPending,
@@ -619,6 +651,7 @@ export default function ChatPage() {
   const handleAgentChange = useCallback((agentId: string | null) => {
     setSelectedAgentId(agentId);
     setSelectedKnowledgeBaseIds([]);
+    setSelectedAssistantKnowledgeBaseIds([]);
     setKnowledgeSelectionReady(false);
     saveLastAgentId(agentId);
   }, []);
@@ -907,7 +940,9 @@ export default function ChatPage() {
                           source={selectedAgentId ? "agent" : "all"}
                           agentId={selectedAgentId}
                           value={selectedKnowledgeBaseIds}
+                          assistantValue={selectedAssistantKnowledgeBaseIds}
                           onChange={changeKnowledgeSelection}
+                          onAssistantChange={changeAssistantKnowledgeSelection}
                           autoSelectAll={isLandingState && !!selectedAgentId}
                           disabled={
                             env.NEXT_PUBLIC_STATIC_WEBSITE_ONLY === "true" ||

frontend-web/src/pages/LlmWikiKnowledgePage.tsx

@@ -3,6 +3,11 @@ import { useState } from "react";
 import { Navigate, useParams, useSearchParams } from "react-router-dom";
 
 import { Button } from "@/components/ui/button";
+import {
+  KNOWLEDGE_NORMAL_VIEW_VALUE,
+  KnowledgeProductNav,
+  useKnowledgeBaseProductVersion,
+} from "@/components/workspace/knowledge-product-nav";
 import {
   Dialog,
   DialogContent,
@@ -70,7 +75,9 @@ export default function LlmWikiKnowledgePage({
   const { noteId = "" } = useParams();
   const [searchParams] = useSearchParams();
   const runtime = useLlmWikiRuntime();
+  const productVersion = useKnowledgeBaseProductVersion();
   const wikiSlug = searchParams.get("wikiSlug")?.trim() || "";
+  const explicitNormalView = searchParams.get("view") === KNOWLEDGE_NORMAL_VIEW_VALUE;
 
   if (mode === "detail" && noteId && wikiSlug) {
     return <Navigate to={buildWeKnoraWikiPagePath(noteId, wikiSlug)} replace />;
@@ -80,7 +87,14 @@ export default function LlmWikiKnowledgePage({
     return <RuntimeLoading />;
   }
 
+  if (mode === "list" && productVersion.data === "v2" && !explicitNormalView) {
+    return <Navigate to="/page/workspace/knowledge/assistant" replace />;
+  }
+
   if (runtime.isError || runtime.data?.provider !== "weknora") {
+    if (mode === "list" || mode === "detail") {
+      return <Navigate to="/page/workspace/knowledge/assistant" replace />;
+    }
     if (mode === "graph") {
       return <KnowledgeGraphPage />;
     }
@@ -95,5 +109,5 @@ export default function LlmWikiKnowledgePage({
     return <WeKnoraKnowledgeDetailPage />;
   }
 
-  return <WeKnoraKnowledgeWorkspace />;
+  return <div className="flex size-full min-h-0 flex-col"><KnowledgeProductNav /><div className="min-h-0 flex-1"><WeKnoraKnowledgeWorkspace /></div></div>;
 }

frontend-web/src/pages/WorkspaceRoutes.tsx

@@ -30,6 +30,7 @@ import { agentRefOfThread } from '@/core/threads/utils';
 
 import ChatsPage from './ChatsPage';
 import ChatPage from './ChatPage';
+import ArtifactLibraryPage from './ArtifactLibraryPage';
 import IframeChatPage from './IframeChatPage';
 import AgentsPage from './AgentsPage';
 import AgentChatPage from './AgentChatPage';
@@ -41,6 +42,7 @@ import AdminSkillsPage from './AdminSkillsPage';
 import ScheduledTaskRunDetailPage from './ScheduledTaskRunDetailPage';
 import ScheduledTasksPage from './ScheduledTasksPage';
 import LlmWikiKnowledgePage from './LlmWikiKnowledgePage';
+import AssistantKnowledgePage from './AssistantKnowledgePage';
 import WeKnoraWikiManagementPage from '@/strategy-components/components/resource-management/llmwiki/WeKnoraWikiManagementPage';
 import WeKnoraWikiPageView from '@/strategy-components/components/resource-management/llmwiki/WeKnoraWikiPageView';
 import MarkdownDocxEditorPage from './MarkdownDocxEditorPage';
@@ -95,6 +97,7 @@ function shouldShowCompactTopActions(pathname: string): boolean {
     pathname === '/page/workspace/agents' ||
     pathname === '/page/workspace/skills' ||
     pathname === '/page/workspace/memory' ||
+    pathname === '/page/workspace/library' ||
     pathname === '/page/workspace/knowledge' ||
     pathname.startsWith('/page/workspace/knowledge/')
   );
@@ -518,6 +521,20 @@ export default function WorkspaceRoutes({
           path="chats/:thread_id"
           element={<ChatThreadEntryRoute layoutProps={layoutProps} />}
         />
+        <Route
+          path="library"
+          element={
+            <WorkspaceLayout
+              {...layoutProps}
+              workspaceSidebarHidden={
+                layoutProps.workspaceSidebarHidden ||
+                !layoutProps.pageSidebarHidden
+              }
+            >
+              <ArtifactLibraryPage />
+            </WorkspaceLayout>
+          }
+        />
         <Route
           path="chats_iframe/new"
           element={<IframeChatEntryRoute layoutProps={layoutProps} hasThreadParam={false} />}
@@ -753,6 +770,14 @@ export default function WorkspaceRoutes({
             </WorkspaceLayout>
           }
         />
+        <Route
+          path="knowledge/assistant"
+          element={
+            <WorkspaceLayout {...layoutProps}>
+              <AssistantKnowledgePage />
+            </WorkspaceLayout>
+          }
+        />
         <Route
           path="knowledge/:noteId/wiki/view"
           element={

frontend-web/src/strategy-components/api/assistant-knowledge.ts

@@ -0,0 +1,324 @@
+import { useMutation, useQuery, useQueryClient } from "@tanstack/react-query";
+
+import { apiFetch } from "@/core/api/fetch-client";
+import { getBackendBaseURL } from "@/core/config";
+
+const BASE = () => `${getBackendBaseURL()}/api/assistant-knowledge`;
+
+export interface AssistantKnowledgeBase {
+  id: string;
+  name: string;
+  description?: string;
+  base_kind?: "global" | "custom";
+  is_global?: boolean;
+  status: string;
+  source_count?: number;
+  page_count?: number;
+  entity_count?: number;
+  relation_count?: number;
+  created_at: string;
+  updated_at: string;
+}
+
+export interface AssistantWikiPage {
+  id: string;
+  base_id: string;
+  slug: string;
+  title: string;
+  page_type: string;
+  content_markdown: string;
+  summary: string;
+  current_revision_id?: string | null;
+  updated_at: string;
+}
+
+export interface AssistantWikiRevision {
+  id: string;
+  page_id: string;
+  revision_no: number;
+  summary: string;
+  content_markdown: string;
+  change_type: string;
+  created_by?: string | null;
+  created_at: string;
+}
+
+export interface AssistantKnowledgeSource {
+  id: string;
+  base_id: string;
+  source_type: "weknora" | "skill" | "local_file";
+  source_name: string;
+  source_key: string;
+  status: string;
+  updated_at: string;
+}
+
+export interface AssistantImportJob {
+  id: string;
+  base_id: string;
+  source_id: string;
+  mirrored_global_base_id?: string;
+  mirrored_global_job_id?: string;
+  package_download_url?: string;
+  trigger: string;
+  status: string;
+  phase: string;
+  counts: Record<string, number>;
+  last_error_message?: string | null;
+  created_at: string;
+  completed_at?: string | null;
+}
+
+export interface LlmWikiExportPackageJob {
+  id: string;
+  mapping_id: string;
+  status: string;
+  phase: string;
+  counts: Record<string, number>;
+  package_download_url?: string;
+  status_url?: string;
+  error?: string | null;
+  created_at?: string | null;
+  updated_at?: string | null;
+  completed_at?: string | null;
+  file_exists?: boolean;
+  file_size_bytes?: number;
+}
+
+async function readJson<T>(response: Response): Promise<T> {
+  if (!response.ok) {
+    const data = await response.json().catch(() => null);
+    const detail = data && typeof data === "object" && "detail" in data
+      ? (data as { detail: unknown }).detail
+      : `请求失败 (${response.status})`;
+    throw new Error(typeof detail === "string" ? detail : JSON.stringify(detail));
+  }
+  return (await response.json()) as T;
+}
+
+function normalizeImportJob(job: AssistantImportJob): AssistantImportJob {
+  const url = normalizeBackendUrl(job.package_download_url);
+  return url ? { ...job, package_download_url: url } : job;
+}
+
+function normalizeBackendUrl(url: string | undefined | null): string | undefined {
+  if (!url || /^https?:\/\//i.test(url)) return url || undefined;
+  return `${getBackendBaseURL()}${url.startsWith("/") ? url : `/${url}`}`;
+}
+
+function normalizeExportPackageJob(job: LlmWikiExportPackageJob): LlmWikiExportPackageJob {
+  return {
+    ...job,
+    package_download_url: normalizeBackendUrl(job.package_download_url),
+    status_url: normalizeBackendUrl(job.status_url),
+  };
+}
+
+export async function getAssistantKnowledgeBase() {
+  const data = await readJson<{ base: AssistantKnowledgeBase | null }>(await apiFetch(`${BASE()}/base`));
+  return data.base;
+}
+
+export async function getAssistantKnowledgeBaseById(baseId: string) {
+  const data = await readJson<{ base: AssistantKnowledgeBase }>(
+    await apiFetch(`${BASE()}/bases/${encodeURIComponent(baseId)}`),
+  );
+  return data.base;
+}
+
+export async function listAssistantKnowledgeBases() {
+  return (await readJson<{ bases: AssistantKnowledgeBase[] }>(await apiFetch(`${BASE()}/bases`))).bases;
+}
+
+export async function initializeAssistantKnowledgeBase(name = "知识梳理总库") {
+  const data = await readJson<{ base: AssistantKnowledgeBase }>(
+    await apiFetch(`${BASE()}/base/initialize`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ name }),
+    }),
+  );
+  return data.base;
+}
+
+export async function createAssistantKnowledgeBase(input: { name: string; description?: string }) {
+  const data = await readJson<{ base: AssistantKnowledgeBase }>(
+    await apiFetch(`${BASE()}/bases`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(input),
+    }),
+  );
+  return data.base;
+}
+
+function withBaseParam(baseId?: string | null) {
+  const params = new URLSearchParams();
+  if (baseId) params.set("base_id", baseId);
+  return params;
+}
+
+export async function listAssistantWikiPages(q = "", baseId?: string | null) {
+  const params = withBaseParam(baseId);
+  if (q.trim()) params.set("q", q.trim());
+  return (await readJson<{ pages: AssistantWikiPage[] }>(await apiFetch(`${BASE()}/wiki-pages?${params}`))).pages;
+}
+
+export async function listAssistantKnowledgeSources(baseId?: string | null) {
+  const params = withBaseParam(baseId);
+  return (await readJson<{ sources: AssistantKnowledgeSource[] }>(await apiFetch(`${BASE()}/sources?${params}`))).sources;
+}
+
+export async function listAssistantImportJobs(baseId?: string | null) {
+  const params = withBaseParam(baseId);
+  return (
+    await readJson<{ jobs: AssistantImportJob[] }>(await apiFetch(`${BASE()}/import-jobs?${params}`))
+  ).jobs.map(normalizeImportJob);
+}
+
+export async function listAssistantWikiRevisions(slug: string, baseId?: string | null) {
+  const params = withBaseParam(baseId);
+  return (await readJson<{ revisions: AssistantWikiRevision[] }>(
+    await apiFetch(`${BASE()}/wiki-pages/${encodeURIComponent(slug)}/revisions?${params}`),
+  )).revisions;
+}
+
+export async function rollbackAssistantWikiRevision(slug: string, revisionId: string, baseId?: string | null) {
+  const params = withBaseParam(baseId);
+  return readJson<AssistantWikiRevision>(
+    await apiFetch(
+      `${BASE()}/wiki-pages/${encodeURIComponent(slug)}/revisions/${encodeURIComponent(revisionId)}/rollback?${params}`,
+      { method: "POST" },
+    ),
+  );
+}
+
+export async function exportLlmWikiToAssistant(mappingId: string, assistantBaseId?: string | null) {
+  return normalizeImportJob(await readJson<AssistantImportJob>(
+    await apiFetch(
+      `${getBackendBaseURL()}/api/llmwiki/knowledge-bases/${encodeURIComponent(mappingId)}/export-to-assistant`,
+      {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({ assistant_base_id: assistantBaseId || null }),
+      },
+    ),
+  ));
+}
+
+export async function createLlmWikiExportPackage(mappingId: string) {
+  return normalizeExportPackageJob(
+    await readJson<LlmWikiExportPackageJob>(
+      await apiFetch(
+        `${getBackendBaseURL()}/api/llmwiki/knowledge-bases/${encodeURIComponent(mappingId)}/export-package`,
+        { method: "POST" },
+      ),
+    ),
+  );
+}
+
+export async function listLlmWikiExportPackages(mappingId: string) {
+  return (
+    await readJson<{ jobs: LlmWikiExportPackageJob[] }>(
+      await apiFetch(
+        `${getBackendBaseURL()}/api/llmwiki/knowledge-bases/${encodeURIComponent(mappingId)}/export-package`,
+      ),
+    )
+  ).jobs.map(normalizeExportPackageJob);
+}
+
+export async function getLlmWikiExportPackageStatus(mappingId: string, jobId: string) {
+  return normalizeExportPackageJob(
+    await readJson<LlmWikiExportPackageJob>(
+      await apiFetch(
+        `${getBackendBaseURL()}/api/llmwiki/knowledge-bases/${encodeURIComponent(mappingId)}/export-package/${encodeURIComponent(jobId)}`,
+      ),
+    ),
+  );
+}
+
+export async function reimportAssistantKnowledgeSource(sourceId: string) {
+  return normalizeImportJob(await readJson<AssistantImportJob>(
+    await apiFetch(`${BASE()}/sources/${encodeURIComponent(sourceId)}/reimport`, {
+      method: "POST",
+    }),
+  ));
+}
+
+export function getAssistantImportPackageUrl(jobId: string) {
+  return `${BASE()}/import-jobs/${encodeURIComponent(jobId)}/package`;
+}
+
+export async function uploadAssistantWeKnoraPackage(input: { file: File; assistantBaseId?: string | null }) {
+  const body = new FormData();
+  body.set("file", input.file);
+  if (input.assistantBaseId) body.set("assistant_base_id", input.assistantBaseId);
+  return normalizeImportJob(await readJson<AssistantImportJob>(
+    await apiFetch(`${BASE()}/imports/weknora-package`, {
+      method: "POST",
+      body,
+    }),
+  ));
+}
+
+export async function deleteAssistantKnowledgeSource(sourceId: string) {
+  const response = await apiFetch(`${BASE()}/sources/${encodeURIComponent(sourceId)}`, {
+    method: "DELETE",
+  });
+  if (!response.ok) await readJson(response);
+}
+
+const KEY = "assistant-knowledge";
+
+export function useAssistantKnowledgeBase(baseId?: string | null) {
+  return useQuery({
+    queryKey: [KEY, "base", baseId ?? "global"],
+    queryFn: () => (baseId ? getAssistantKnowledgeBaseById(baseId) : getAssistantKnowledgeBase()),
+  });
+}
+
+export function useAssistantKnowledgeBases() {
+  return useQuery({ queryKey: [KEY, "bases"], queryFn: listAssistantKnowledgeBases });
+}
+
+export function useAssistantWikiPages(query = "", baseId?: string | null) {
+  return useQuery({
+    queryKey: [KEY, "pages", baseId ?? "global", query],
+    queryFn: () => listAssistantWikiPages(query, baseId),
+  });
+}
+
+export function useAssistantKnowledgeSources(baseId?: string | null) {
+  return useQuery({
+    queryKey: [KEY, "sources", baseId ?? "global"],
+    queryFn: () => listAssistantKnowledgeSources(baseId),
+  });
+}
+
+export function useAssistantImportJobs(baseId?: string | null) {
+  return useQuery({
+    queryKey: [KEY, "jobs", baseId ?? "global"],
+    queryFn: () => listAssistantImportJobs(baseId),
+    refetchInterval: (query) =>
+      (query.state.data ?? []).some((job) => ["queued", "running"].includes(job.status))
+        ? 3000
+        : false,
+  });
+}
+
+export function useAssistantKnowledgeActions() {
+  const client = useQueryClient();
+  const invalidate = () => client.invalidateQueries({ queryKey: [KEY] });
+  return {
+    initialize: useMutation({ mutationFn: initializeAssistantKnowledgeBase, onSuccess: invalidate }),
+    createBase: useMutation({ mutationFn: createAssistantKnowledgeBase, onSuccess: invalidate }),
+    uploadWeKnoraPackage: useMutation({ mutationFn: uploadAssistantWeKnoraPackage, onSuccess: invalidate }),
+    reimportSource: useMutation({ mutationFn: reimportAssistantKnowledgeSource, onSuccess: invalidate }),
+    deleteSource: useMutation({ mutationFn: deleteAssistantKnowledgeSource, onSuccess: invalidate }),
+    rollback: useMutation({
+      mutationFn: ({ slug, revisionId, baseId }: { slug: string; revisionId: string; baseId?: string | null }) =>
+        rollbackAssistantWikiRevision(slug, revisionId, baseId),
+      onSuccess: invalidate,
+    }),
+  };
+}

frontend-web/src/strategy-components/api/llmwiki.ts

@@ -29,7 +29,7 @@ export interface LlmWikiKnowledgeBase {
   remote_status: "available" | "missing";
   name: string;
   description: string;
-  type: "document" | "faq";
+  type: "document" | "faq" | "wiki";
   publication_status: "private" | "pending" | "published" | "rejected";
   owner_user_id?: string | null;
   is_owner: boolean;
@@ -37,6 +37,12 @@ export interface LlmWikiKnowledgeBase {
   knowledge_count: number;
   chunk_count: number;
   processing_count: number;
+  share_count?: number;
+  is_processing?: boolean;
+  is_temporary?: boolean;
+  wiki_index_enabled?: boolean;
+  external_search_enabled?: boolean;
+  external_search_updated_at?: string | null;
   embedding_model_id?: string | null;
   vector_store_id?: string | null;
   vector_store_name?: string | null;
@@ -44,19 +50,65 @@ export interface LlmWikiKnowledgeBase {
   vector_store_engine_type?: string | null;
   vector_store_status?: string | null;
   chunking_config?: Record<string, unknown>;
+  image_processing_config?: Record<string, unknown>;
   indexing_strategy?: Record<string, boolean>;
   capabilities?: Record<string, boolean>;
   extract_config?: Record<string, unknown>;
+  faq_config?: Record<string, unknown>;
   wiki_config?: Record<string, unknown>;
   question_generation_config?: Record<string, unknown>;
+  auto_tag_config?: Record<string, unknown>;
+  vlm_config?: Record<string, unknown>;
+  asr_config?: Record<string, unknown>;
   summary_model_id?: string | null;
   storage_provider?: string | null;
   created_at?: string | null;
   updated_at?: string | null;
   published_at?: string | null;
+  remote_created_at?: string | null;
+  remote_updated_at?: string | null;
   is_conversation_deposit?: boolean;
 }
 
+export interface PublicLlmWikiKnowledgeBase extends LlmWikiKnowledgeBase {
+  wiki: {
+    status: "ready" | "empty" | "unavailable";
+    page_count: number;
+    loaded_page_count: number;
+    stats_complete: boolean;
+    page_type_counts: Record<string, number>;
+    page_status_counts: Record<string, number>;
+    index_mode: "explicit" | "generated" | "none" | "unknown";
+    has_explicit_index: boolean;
+    index_slug: string | null;
+    default_slug: string | null;
+    created_at: string | null;
+    updated_at: string | null;
+    embed_route: string;
+  };
+  local_index: {
+    enabled: boolean;
+    wiki_index_enabled: boolean;
+    state: LlmWikiWikiIndexStatus["state"];
+    fully_vectorized: boolean;
+    retrieval_mode: "vector" | "wiki_api";
+    remote_page_count: number;
+    local_page_count: number;
+    ready_page_count: number;
+    failed_page_count: number;
+    vector_count: number;
+    last_started_at?: string | null;
+    last_completed_at?: string | null;
+    last_success_at?: string | null;
+    updated_at?: string | null;
+  };
+}
+
+export interface PublicLlmWikiKnowledgeBaseList {
+  knowledge_bases: PublicLlmWikiKnowledgeBase[];
+  total: number;
+}
+
 export interface LlmWikiDocument {
   id: string;
   type?: string;
@@ -742,6 +794,13 @@ export async function getPublicLlmWikiKnowledgeBase(
   );
 }
 
+export async function listPublicLlmWikiKnowledgeBases(): Promise<PublicLlmWikiKnowledgeBaseList> {
+  return readJson(
+    await apiFetch(`${PUBLIC_API()}/knowledge-bases`),
+    "读取公开知识库列表",
+  );
+}
+
 export async function listPublicLlmWikiWikiPages(
   knowledgeBaseId: string,
   page: number,

frontend-web/src/strategy-components/api/skill-knowledge.ts

@@ -0,0 +1,277 @@
+import { useMutation, useQuery, useQueryClient } from "@tanstack/react-query";
+
+import { apiFetch } from "@/core/api/fetch-client";
+import { getBackendBaseURL } from "@/core/config";
+
+const BASE = () => `${getBackendBaseURL()}/api/skill-knowledge`;
+
+export type SkillKnowledgeTargetType = "weknora" | "assistant";
+export type SkillKnowledgeTargetMode =
+  | "wiki"
+  | "document"
+  | "faq"
+  | "conversation_archive"
+  | "readonly_sidecar"
+  | "assistant";
+
+export interface SkillKnowledgeTarget {
+  target_type: SkillKnowledgeTargetType;
+  target_mode: SkillKnowledgeTargetMode;
+  target_id: string;
+  target_name: string;
+  remote_business_key?: string | null;
+  readonly?: boolean;
+}
+
+export interface SkillKnowledgeBinding {
+  id: string;
+  skill_name: string;
+  target_type: SkillKnowledgeTargetType;
+  target_mode: SkillKnowledgeTargetMode;
+  target_id: string;
+  target_name: string;
+  remote_business_key?: string | null;
+  remote_refs?: Array<{
+    provider: string;
+    remote_kind: string;
+    remote_id: string;
+    slug?: string;
+  }>;
+  status: string;
+  active: boolean;
+  auto_sync_enabled: boolean;
+  synced_digest?: string | null;
+  last_synced_at?: string | null;
+  last_error_message?: string | null;
+}
+
+export interface SkillKnowledgeSyncItem {
+  id: string;
+  job_id: string;
+  binding_id: string;
+  skill_name: string;
+  target_type: SkillKnowledgeTargetType;
+  target_mode: SkillKnowledgeTargetMode;
+  target_id: string;
+  status: string;
+  phase: string;
+  progress: number;
+  attempts: number;
+  last_error_message?: string | null;
+}
+
+export interface SkillKnowledgeJob {
+  id: string;
+  status: string;
+  total_count: number;
+  success_count: number;
+  partial_failed_count: number;
+  failed_count: number;
+  review_mode: "required" | "auto_high_confidence" | "off";
+  created_at: string;
+  completed_at?: string | null;
+  items?: SkillKnowledgeSyncItem[];
+}
+
+export interface SkillKnowledgeReview {
+  id: string;
+  job_id: string;
+  item_id: string;
+  binding_id: string;
+  object_type: "entity" | "relation" | "wiki" | "alignment";
+  object_key: string;
+  original: Record<string, unknown>;
+  edited?: Record<string, unknown> | null;
+  status: string;
+  reason?: string | null;
+}
+
+export interface SkillKnowledgeAutoSyncSettings {
+  watch_enabled: boolean;
+  schedule_enabled: boolean;
+  schedule_cron: string;
+  debounce_seconds: number;
+  write_remote_enabled: boolean;
+  review_mode: "required" | "auto_high_confidence" | "off";
+}
+
+async function readJson<T>(response: Response): Promise<T> {
+  if (!response.ok) {
+    const data = await response.json().catch(() => null);
+    const detail =
+      data && typeof data === "object" && "detail" in data
+        ? JSON.stringify((data as { detail: unknown }).detail)
+        : `请求失败 (${response.status})`;
+    throw new Error(detail.replace(/^"|"$/g, ""));
+  }
+  return (await response.json()) as T;
+}
+
+export async function listSkillKnowledgeBindings(
+  skillNames?: string[],
+): Promise<SkillKnowledgeBinding[]> {
+  const params = new URLSearchParams();
+  for (const name of skillNames ?? []) params.append("skill_names", name);
+  const response = await apiFetch(`${BASE()}/bindings?${params.toString()}`);
+  return (await readJson<{ bindings: SkillKnowledgeBinding[] }>(response)).bindings;
+}
+
+export async function createSkillKnowledgeJob(input: {
+  skill_names: string[];
+  targets: SkillKnowledgeTarget[];
+  force?: boolean;
+  review_mode?: "required" | "auto_high_confidence" | "off";
+}): Promise<SkillKnowledgeJob> {
+  return readJson(
+    await apiFetch(`${BASE()}/sync-jobs`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(input),
+    }),
+  );
+}
+
+export async function listSkillKnowledgeJobs(): Promise<SkillKnowledgeJob[]> {
+  const response = await apiFetch(`${BASE()}/sync-jobs`);
+  return (await readJson<{ jobs: SkillKnowledgeJob[] }>(response)).jobs;
+}
+
+export async function getSkillKnowledgeJob(id: string): Promise<SkillKnowledgeJob> {
+  return readJson(await apiFetch(`${BASE()}/sync-jobs/${encodeURIComponent(id)}`));
+}
+
+export async function listSkillKnowledgeReviews(): Promise<SkillKnowledgeReview[]> {
+  const response = await apiFetch(`${BASE()}/reviews?status=pending`);
+  return (await readJson<{ reviews: SkillKnowledgeReview[] }>(response)).reviews;
+}
+
+export async function reviewSkillKnowledge(input: {
+  review_ids: string[];
+  action: "approve" | "reject";
+  edits?: Record<string, Record<string, unknown>>;
+  reason?: string;
+}) {
+  return readJson<{ updated: number; resumed_item_ids: string[] }>(
+    await apiFetch(`${BASE()}/reviews/actions`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(input),
+    }),
+  );
+}
+
+export async function retrySkillKnowledgeItem(itemId: string) {
+  return readJson<SkillKnowledgeSyncItem>(
+    await apiFetch(`${BASE()}/sync-items/${encodeURIComponent(itemId)}/retry`, {
+      method: "POST",
+    }),
+  );
+}
+
+export async function detachSkillKnowledgeBinding(
+  bindingId: string,
+  deleteContributions: boolean,
+) {
+  return readJson<SkillKnowledgeBinding>(
+    await apiFetch(`${BASE()}/bindings/${encodeURIComponent(bindingId)}/detach`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ delete_contributions: deleteContributions }),
+    }),
+  );
+}
+
+export async function resyncSkillKnowledgeBinding(bindingId: string) {
+  return readJson<SkillKnowledgeJob>(
+    await apiFetch(`${BASE()}/bindings/${encodeURIComponent(bindingId)}/resync`, {
+      method: "POST",
+    }),
+  );
+}
+
+export async function setSkillKnowledgeBindingAutoSync(
+  bindingId: string,
+  enabled: boolean,
+) {
+  return readJson<SkillKnowledgeBinding>(
+    await apiFetch(
+      `${BASE()}/bindings/${encodeURIComponent(bindingId)}/${enabled ? "enable" : "disable"}-auto-sync`,
+      { method: "POST" },
+    ),
+  );
+}
+
+export async function getSkillKnowledgeAutoSyncSettings() {
+  return readJson<SkillKnowledgeAutoSyncSettings>(
+    await apiFetch(`${BASE()}/auto-sync-settings`),
+  );
+}
+
+export async function updateSkillKnowledgeAutoSyncSettings(
+  input: Partial<SkillKnowledgeAutoSyncSettings>,
+) {
+  return readJson<SkillKnowledgeAutoSyncSettings>(
+    await apiFetch(`${BASE()}/auto-sync-settings`, {
+      method: "PUT",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(input),
+    }),
+  );
+}
+
+const KEY = "skill-knowledge";
+
+export function useSkillKnowledgeBindings(skillNames?: string[]) {
+  return useQuery({
+    queryKey: [KEY, "bindings", ...(skillNames ?? [])],
+    queryFn: () => listSkillKnowledgeBindings(skillNames),
+    refetchOnWindowFocus: false,
+  });
+}
+
+export function useSkillKnowledgeJobs() {
+  return useQuery({
+    queryKey: [KEY, "jobs"],
+    queryFn: listSkillKnowledgeJobs,
+    refetchInterval: (query) =>
+      (query.state.data ?? []).some((job) => ["queued", "running"].includes(job.status))
+        ? 1500
+        : false,
+  });
+}
+
+export function useSkillKnowledgeReviews() {
+  return useQuery({ queryKey: [KEY, "reviews"], queryFn: listSkillKnowledgeReviews });
+}
+
+export function useSkillKnowledgeAutoSyncSettings() {
+  return useQuery({
+    queryKey: [KEY, "auto-sync-settings"],
+    queryFn: getSkillKnowledgeAutoSyncSettings,
+  });
+}
+
+export function useSkillKnowledgeActions() {
+  const client = useQueryClient();
+  const invalidate = () => client.invalidateQueries({ queryKey: [KEY] });
+  return {
+    createJob: useMutation({ mutationFn: createSkillKnowledgeJob, onSuccess: invalidate }),
+    review: useMutation({ mutationFn: reviewSkillKnowledge, onSuccess: invalidate }),
+    retry: useMutation({ mutationFn: retrySkillKnowledgeItem, onSuccess: invalidate }),
+    resync: useMutation({ mutationFn: resyncSkillKnowledgeBinding, onSuccess: invalidate }),
+    setAutoSync: useMutation({
+      mutationFn: ({ id, enabled }: { id: string; enabled: boolean }) =>
+        setSkillKnowledgeBindingAutoSync(id, enabled),
+      onSuccess: invalidate,
+    }),
+    updateAutoSyncSettings: useMutation({
+      mutationFn: updateSkillKnowledgeAutoSyncSettings,
+      onSuccess: invalidate,
+    }),
+    detach: useMutation({
+      mutationFn: ({ id, deleteContributions }: { id: string; deleteContributions: boolean }) =>
+        detachSkillKnowledgeBinding(id, deleteContributions),
+      onSuccess: invalidate,
+    }),
+  };
+}

frontend-web/src/strategy-components/components/resource-management/llmwiki/WeKnoraKnowledgeDetailPage.tsx

@@ -1,19 +1,16 @@
-import { AlertCircle, Edit3, FileText, Loader2, RefreshCw, Trash2 } from "lucide-react";
-import { useEffect, useRef, useState } from "react";
+import { AlertCircle, Download, Edit3, FileText, Loader2, RefreshCw, Trash2 } from "lucide-react";
+import { useCallback, useEffect, useRef, useState } from "react";
 import { Navigate, useNavigate, useParams, useSearchParams } from "react-router-dom";
 import { toast } from "sonner";
 
+import { Badge } from "@/components/ui/badge";
 import { Button } from "@/components/ui/button";
-import {
-  Dialog,
-  DialogContent,
-  DialogHeader,
-  DialogTitle,
-} from "@/components/ui/dialog";
+import { Dialog, DialogContent, DialogHeader, DialogTitle } from "@/components/ui/dialog";
 import { Input } from "@/components/ui/input";
 import { Textarea } from "@/components/ui/textarea";
 import { MarkdownCodeEditor } from "@/components/wiki/MarkdownCodeEditor";
 import { getBackendBaseURL } from "@/core/config";
+import { getStoredAuth } from "@/core/auth";
 import { buildWeKnoraWikiPagePath } from "@/core/llmwiki/source-links";
 import {
   createLlmWikiWeKnoraEmbedSession,
@@ -23,8 +20,20 @@ import {
   useLlmWikiWikiPage,
   useLlmWikiRuntime,
 } from "@/strategy-components/api/llmwiki";
+import {
+  createLlmWikiExportPackage,
+  listLlmWikiExportPackages,
+  type LlmWikiExportPackageJob,
+} from "@/strategy-components/api/assistant-knowledge";
 
 const LIST_PATH = "/page/workspace/knowledge";
+const RUNNING_EXPORT_STATUSES = new Set(["queued", "running"]);
+const EXPORT_STATUS_LABELS: Record<string, string> = {
+  queued: "排队中",
+  running: "整理中",
+  completed: "已完成",
+  failed: "失败",
+};
 
 type DeerFlowThemePayload = {
   type: "deerflow-weknora-theme";
@@ -92,6 +101,25 @@ function buildFrameUrl(framePath: string) {
   return new URL(`${backendBase || backendOrigin}/${framePath.replace(/^\/+/, "")}`);
 }
 
+function formatBytes(value: number | undefined | null) {
+  const bytes = Number(value || 0);
+  if (!Number.isFinite(bytes) || bytes <= 0) return "—";
+  const units = ["B", "KB", "MB", "GB", "TB"];
+  let current = bytes;
+  let unitIndex = 0;
+  while (current >= 1024 && unitIndex < units.length - 1) {
+    current /= 1024;
+    unitIndex += 1;
+  }
+  return `${current >= 10 || unitIndex === 0 ? current.toFixed(0) : current.toFixed(1)} ${units[unitIndex]}`;
+}
+
+function formatTime(value: string | undefined | null) {
+  if (!value) return "—";
+  const time = new Date(value);
+  return Number.isNaN(time.getTime()) ? "—" : time.toLocaleString();
+}
+
 export function WeKnoraKnowledgeDetailPage() {
   const {
     knowledge_base_id: strategyKnowledgeBaseId,
@@ -104,15 +132,40 @@ export function WeKnoraKnowledgeDetailPage() {
   const [frameSrc, setFrameSrc] = useState("");
   const [frameVersion, setFrameVersion] = useState(0);
   const [frameError, setFrameError] = useState<string | null>(null);
+  const [exportPackageLoading, setExportPackageLoading] = useState(false);
+  const [exportJobsLoading, setExportJobsLoading] = useState(false);
+  const [exportJobs, setExportJobs] = useState<LlmWikiExportPackageJob[]>([]);
+  const isAdmin = getStoredAuth()?.system_role === "admin";
   const runtime = useLlmWikiRuntime();
   const details = useLlmWikiKnowledgeBase(knowledgeBaseId || null, !!knowledgeBaseId);
 
+  const refreshExportJobs = useCallback(
+    async (silent = false) => {
+      if (!knowledgeBaseId || !isAdmin) return;
+      if (!silent) setExportJobsLoading(true);
+      try {
+        setExportJobs(await listLlmWikiExportPackages(knowledgeBaseId));
+      } catch (error) {
+        if (!silent) toast.error(error instanceof Error ? error.message : String(error));
+      } finally {
+        if (!silent) setExportJobsLoading(false);
+      }
+    },
+    [isAdmin, knowledgeBaseId],
+  );
+
   const detailsReady = !!details.data;
   const requestedTab = searchParams.get("tab");
+  const detailsWikiEnabled =
+    details.data?.type === "wiki" ||
+    details.data?.wiki_index_enabled === true ||
+    details.data?.indexing_strategy?.wiki_enabled === true ||
+    details.data?.capabilities?.wiki_enabled === true ||
+    (details.data?.wiki_config && Object.keys(details.data.wiki_config).length > 0);
   const preferredTab =
     requestedTab === "documents" || requestedTab === "wiki" || requestedTab === "graph"
       ? requestedTab
-      : details.data?.indexing_strategy?.wiki_enabled
+      : detailsWikiEnabled
         ? "wiki"
         : "documents";
   const sourceChunkId = searchParams.get("sourceChunkId") || searchParams.get("chunkId") || "";
@@ -179,6 +232,22 @@ export function WeKnoraKnowledgeDetailPage() {
     };
   }, [frameSrc]);
 
+  useEffect(() => {
+    if (!isAdmin || !knowledgeBaseId || runtime.data?.provider !== "weknora") {
+      setExportJobs([]);
+      return;
+    }
+    void refreshExportJobs();
+  }, [isAdmin, knowledgeBaseId, refreshExportJobs, runtime.data?.provider]);
+
+  useEffect(() => {
+    if (!isAdmin || !knowledgeBaseId || !exportJobs.some((job) => RUNNING_EXPORT_STATUSES.has(job.status))) {
+      return;
+    }
+    const timer = window.setInterval(() => void refreshExportJobs(true), 5000);
+    return () => window.clearInterval(timer);
+  }, [exportJobs, isAdmin, knowledgeBaseId, refreshExportJobs]);
+
   if (nativeWikiPageUrl) {
     return <Navigate to={nativeWikiPageUrl} replace />;
   }
@@ -232,8 +301,25 @@ export function WeKnoraKnowledgeDetailPage() {
 
   const canWrite = details.data.can_write && !details.data.is_conversation_deposit;
   const canEditCurrentWikiArticle =
-    canWrite && !!details.data.indexing_strategy?.wiki_enabled && !!wikiSlug;
-  const canManageWiki = canWrite && !!details.data.indexing_strategy?.wiki_enabled;
+    canWrite && !!detailsWikiEnabled && !!wikiSlug;
+  const canManageWiki = canWrite && !!detailsWikiEnabled;
+
+  const createVectorizedPackage = () => {
+    if (exportPackageLoading) return;
+    setExportPackageLoading(true);
+    void (async () => {
+      try {
+        const job = await createLlmWikiExportPackage(knowledgeBaseId);
+        setExportJobs((current) => [job, ...current.filter((item) => item.id !== job.id)]);
+        toast.success("已创建向量化数据包整理记录，完成后可在下方列表下载");
+        void refreshExportJobs(true);
+      } catch (error) {
+        toast.error(error instanceof Error ? error.message : String(error));
+      } finally {
+        setExportPackageLoading(false);
+      }
+    })();
+  };
 
   return (
     <div className="flex h-full min-h-0 flex-col bg-background">
@@ -255,6 +341,17 @@ export function WeKnoraKnowledgeDetailPage() {
             )}
           </div>
           <div className="flex shrink-0 items-center gap-2">
+            {isAdmin && (
+              <Button
+                size="sm"
+                variant="outline"
+                disabled={exportPackageLoading}
+                onClick={createVectorizedPackage}
+              >
+                {exportPackageLoading ? <Loader2 className="mr-2 size-4 animate-spin" /> : <Download className="mr-2 size-4" />}
+                生成向量化数据包
+              </Button>
+            )}
             <Button
               size="sm"
               variant="outline"
@@ -266,6 +363,13 @@ export function WeKnoraKnowledgeDetailPage() {
           </div>
         </div>
       )}
+      {canManageWiki && isAdmin && (
+        <ExportPackageJobsPanel
+          jobs={exportJobs}
+          loading={exportJobsLoading}
+          onRefresh={() => void refreshExportJobs()}
+        />
+      )}
       {frameSrc && (
         <iframe
           key={`${frameSrc}:${frameVersion}`}
@@ -293,6 +397,84 @@ export function WeKnoraKnowledgeDetailPage() {
   );
 }
 
+function ExportPackageJobsPanel({
+  jobs,
+  loading,
+  onRefresh,
+}: {
+  jobs: LlmWikiExportPackageJob[];
+  loading: boolean;
+  onRefresh: () => void;
+}) {
+  return (
+    <div className="shrink-0 border-b bg-muted/20 px-4 py-3">
+      <div className="mb-2 flex items-center justify-between">
+        <div>
+          <div className="text-sm font-medium">向量化数据包记录</div>
+          <p className="text-xs text-muted-foreground">
+            每次点击都会生成一条后台整理记录；文件较大时，请等待状态变为“已完成”后再下载。
+          </p>
+        </div>
+        <Button size="sm" variant="ghost" onClick={onRefresh} disabled={loading}>
+          {loading ? <Loader2 className="mr-2 size-4 animate-spin" /> : <RefreshCw className="mr-2 size-4" />}
+          刷新
+        </Button>
+      </div>
+      <div className="max-h-44 space-y-2 overflow-y-auto pr-1">
+        {jobs.map((job) => {
+          const running = RUNNING_EXPORT_STATUSES.has(job.status);
+          const completed = job.status === "completed";
+          const canDownload = completed && !!job.package_download_url && job.file_exists !== false;
+          const counts = job.counts ?? {};
+          return (
+            <div key={job.id} className="flex items-center gap-3 rounded-md border bg-card px-3 py-2 text-xs">
+              <Badge
+                variant={job.status === "failed" ? "destructive" : completed ? "default" : "secondary"}
+                className="shrink-0"
+              >
+                {running && <Loader2 className="mr-1 inline size-3 animate-spin" />}
+                {EXPORT_STATUS_LABELS[job.status] || job.status}
+              </Badge>
+              <div className="min-w-0 flex-1">
+                <div className="flex flex-wrap items-center gap-x-3 gap-y-1">
+                  <span className="font-medium">任务 {job.id.slice(0, 8)}</span>
+                  <span className="text-muted-foreground">创建：{formatTime(job.created_at)}</span>
+                  <span className="text-muted-foreground">大小：{formatBytes(job.file_size_bytes)}</span>
+                </div>
+                <div className="mt-1 flex flex-wrap gap-x-3 gap-y-1 text-muted-foreground">
+                  <span>Wiki {counts.wiki_page ?? counts.wiki_pages ?? 0}</span>
+                  <span>文档 {counts.document ?? counts.documents ?? 0}</span>
+                  <span>片段 {counts.chunk ?? counts.chunks ?? 0}</span>
+                  <span>向量 {counts.vector ?? counts.vectors ?? 0}</span>
+                  <span>阶段：{job.phase}</span>
+                  {job.error && <span className="text-destructive">{job.error}</span>}
+                </div>
+              </div>
+              {canDownload ? (
+                <Button asChild size="sm" variant="outline" className="shrink-0">
+                  <a href={job.package_download_url} target="_blank" rel="noreferrer">
+                    <Download className="mr-2 size-4" />
+                    下载
+                  </a>
+                </Button>
+              ) : (
+                <Button size="sm" variant="outline" className="shrink-0" disabled>
+                  {completed && job.file_exists === false ? "文件不存在" : "等待完成"}
+                </Button>
+              )}
+            </div>
+          );
+        })}
+        {!jobs.length && (
+          <div className="rounded-md border border-dashed bg-card px-3 py-6 text-center text-xs text-muted-foreground">
+            暂无导出记录。点击上方“生成向量化数据包”后，任务会显示在这里。
+          </div>
+        )}
+      </div>
+    </div>
+  );
+}
+
 function CurrentWikiArticleActions({
   knowledgeBaseId,
   slug,

frontend-web/src/strategy-components/components/resource-management/llmwiki/WeKnoraWikiPageView.tsx

@@ -54,6 +54,7 @@ import {
 import { WikiProtectedImage } from "./WikiProtectedImage";
 
 const LIST_PATH = "/page/workspace/knowledge";
+const GENERATED_PUBLIC_INDEX_SLUG = "__index__";
 
 function cleanText(value?: string | null) {
   return (value || "").replace(/\r\n/g, "\n").replace(/\n{3,}/g, "\n\n").trim();
@@ -144,13 +145,40 @@ export function WeKnoraWikiPageView({
     !!noteId && publicEmbed,
   );
   const pages = publicEmbed ? publicPages : privatePages;
-  const pagesList = useMemo(() => pages.data?.pages || [], [pages.data?.pages]);
-  const indexPage = useMemo(
-    () => (publicEmbed ? pagesList.find(isWikiIndexPage) : undefined),
-    [pagesList, publicEmbed],
+  const remotePagesList = useMemo(() => pages.data?.pages || [], [pages.data?.pages]);
+  const explicitIndexPage = useMemo(
+    () => (publicEmbed ? remotePagesList.find(isWikiIndexPage) : undefined),
+    [publicEmbed, remotePagesList],
+  );
+  const generatedIndexPage = useMemo<LlmWikiWikiPage | undefined>(
+    () => publicEmbed && remotePagesList.length > 0 && !explicitIndexPage
+      ? {
+          id: "__generated_public_index__",
+          slug: GENERATED_PUBLIC_INDEX_SLUG,
+          title: "索引",
+          page_type: "index",
+          status: "generated",
+          content: "# 索引\n\n知识库页面目录",
+          summary: "",
+          aliases: [],
+          source_refs: [],
+          in_links: [],
+          out_links: [],
+          version: 0,
+        }
+      : undefined,
+    [explicitIndexPage, publicEmbed, remotePagesList.length],
+  );
+  const indexPage = explicitIndexPage || generatedIndexPage;
+  const pagesList = useMemo(
+    () => generatedIndexPage ? [generatedIndexPage, ...remotePagesList] : remotePagesList,
+    [generatedIndexPage, remotePagesList],
   );
   const landingPage = useMemo(() => selectDefaultWikiPage(pagesList), [pagesList]);
   const wikiSlug = requestedWikiSlug || (publicEmbed ? landingPage?.slug || "" : "");
+  const generatedIndexActive = generatedIndexPage?.slug === wikiSlug;
+  const generatedIndexRequested =
+    publicEmbed && wikiSlug === GENERATED_PUBLIC_INDEX_SLUG && !explicitIndexPage;
   const privatePage = useLlmWikiWikiPage(
     noteId || null,
     wikiSlug || null,
@@ -159,7 +187,11 @@ export function WeKnoraWikiPageView({
   const publicPage = usePublicLlmWikiWikiPage(
     noteId || null,
     wikiSlug || null,
-    !!noteId && !!wikiSlug && publicEmbed,
+    !!noteId &&
+      !!wikiSlug &&
+      publicEmbed &&
+      !generatedIndexActive &&
+      !generatedIndexRequested,
   );
   const page = publicEmbed ? publicPage : privatePage;
   const publicWikiIndex = usePublicLlmWikiWikiIndex(
@@ -170,7 +202,9 @@ export function WeKnoraWikiPageView({
   const [directoryQuery, setDirectoryQuery] = useState("");
   const [collapsedDirectories, setCollapsedDirectories] = useState<Set<string>>(() => new Set());
   const routedPage = (location.state as { wikiPage?: LlmWikiWikiPage } | null)?.wikiPage;
-  const pageData = page.data || (routedPage?.slug === wikiSlug ? routedPage : undefined);
+  const pageData = generatedIndexActive
+    ? generatedIndexPage
+    : page.data || (routedPage?.slug === wikiSlug ? routedPage : undefined);
   const filteredPages = useMemo(
     () => filterWikiPagesWithAncestors(pagesList, directoryQuery),
     [directoryQuery, pagesList],
@@ -345,6 +379,24 @@ export function WeKnoraWikiPageView({
         </div>
       );
     }
+    if (publicEmbed && pages.error) {
+      return (
+        <div className="flex h-full flex-col items-center justify-center gap-4 px-6 text-center">
+          <div className="flex size-12 items-center justify-center rounded-full bg-destructive/10 text-destructive">
+            <BookOpenText className="size-6" />
+          </div>
+          <div>
+            <div className="font-medium text-foreground">无法读取 Wiki 目录</div>
+            <div className="mt-1 text-sm text-muted-foreground">
+              {pages.error instanceof Error ? pages.error.message : "公开 Wiki 服务暂时不可用"}
+            </div>
+          </div>
+          <Button onClick={() => void pages.refetch()}>
+            <RefreshCw className="mr-2 size-4" />重试
+          </Button>
+        </div>
+      );
+    }
     return (
       <div className="flex h-full flex-col items-center justify-center gap-4 text-sm text-muted-foreground">
         {publicEmbed ? "该知识库暂无可展示的 Wiki 页面" : "当前链接缺少 Wiki 页面标识"}

offline-backend-20260512/backend/CLAUDE.md

@@ -379,7 +379,7 @@ FastAPI application on port 8001 with health checks at `GET|HEAD /health`, `/api
 | **Uploads** (`/api/threads/{id}/uploads`) | `POST /` - upload files (auto-converts PDF/PPT/Excel/Word); `GET /list` - list; `DELETE /{filename}` - delete |
 | **Threads** (`/api/threads/{id}`) | `DELETE /` - remove DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail |
 | **Threads search** (`POST /threads/search`, LangGraph-compat) | Conversation listing. **System threads are excluded by default**: rows whose `metadata.system` is truthy (scheduler runs, roundtable workers) never crowd the user's list; a caller whose `metadata` filter explicitly references `thread_type`/`system` (e.g. studio notebooks) opts out of the exclusion. New `query` field = case-insensitive title (`display_name`) substring search, used by the chats page's server-side pagination. `POST /threads/count` takes the same request body (limit/offset ignored) and returns `{total}` under identical filtering/exclusion semantics ? powers the chats page's ??/??? display without fetching rows (`ThreadMetaStore.count`). `ThreadMetaStore.search` gained `exclude_system`/`query` params; JSON-blob filters batch-scan until the requested `limit`/`offset` window is full (so pagination over a heavily-filtered set never under-fills), with a `thread_id` tie-breaker on the `updated_at DESC` ordering for stable OFFSET paging. Tests: `tests/test_thread_meta_search.py` |
-| **Artifacts** (`/api/threads/{id}/artifacts`) | `GET /{path}` - serve artifacts; active content types (`text/html`, `application/xhtml+xml`, `image/svg+xml`) are always forced as download attachments to reduce XSS risk; `?download=true` still forces download for other file types |
+| **Artifacts** (`/api/threads/{id}/artifacts`, `/api/artifact-library`) | `GET /{path}` - serve artifacts; active content types (`text/html`, `application/xhtml+xml`, `image/svg+xml`) are always forced as download attachments to reduce XSS risk; `?download=true` still forces download for other file types. `GET /api/artifact-library` scans every current-user normal thread's authoritative `outputs/` directory (including files omitted from `present_files`), excludes uploads and image/audio/video media, joins the owning conversation title/agent route metadata, and exposes search/filter/pagination with a bounded 15-second per-user cache plus `refresh=true` bypass. Frontend route `/page/workspace/library` links each item back to the source chat with `?artifact=<path>` so the chat opens the existing artifact panel directly. |
 | **Word export** (`POST /api/writing/export/docx`) | Shared Markdown → DOCX generator used by conversation, artifact, scheduled-task, Deep Research, and AI-writing download surfaces. `app/gateway/word_export.py` builds OOXML with no Python conversion dependency: A4 portrait, 3.7/3.5/2.8/2.6 cm margins, real multilevel heading numbering (`一、` / `(一)` / `1.`), two-character first-line indent on heading levels 1–3, formal body/table styles, page numbers beginning at 1 with odd pages left-aligned and even pages right-aligned, and embedded `app/gateway/assets/fonts/方正小标宋简体.ttf`. Before numbering it removes complete manual heading prefixes (including `2.1` / `2.1.1`) and drops Markdown horizontal rules from Word output. The ordinary-Q&A Lead Agent can emit the matching Markdown heading forms and avoid unordered bullet lists when the admin enables `prompt_prefix.ordinary_qa_markdown_format_enabled` in `system_settings.json`; the switch defaults off, while writing setup, writing mode, notebook, scheduled runs, and SOUL-driven custom agents keep their independent prompts. The font loader validates its internal family, SHA-256, and OpenType embedding permission (`OS/2.fsType`) before export. Tests: `tests/test_word_export.py`, `tests/test_es_query_routing_prompt.py`. |
 | **Deep Research** (`/api/deep-research`) | Durable, user-owned research sessions/jobs with replayable SSE events and hidden-thread artifacts. `basic`/`quick` use the compact runner; `detailed` creates bounded subtopics, searches/writes sections concurrently, and assembles a report; `deep` recursively searches follow-up questions under a deterministic breadth/depth query budget. `multi_agent` is a LangGraph 1.x editor/researcher/writer/reviewer graph: its initial plan emits `awaiting_input`, the recorder stores the interrupt and releases the lease, and `POST /jobs/{id}/resume` rebuilds the graph from the persisted approved/revised plan. A bounded `custom_outline` is frozen into the session; headings/list entries deterministically supply the detailed and multi-agent plan, and the shared writer receives it only as a guarded content constraint. Completed reports support owner-scoped follow-up (`GET /sessions/{id}/messages`, `POST /sessions/{id}/chat`): context is only the current report, selected sources and recent messages; source-id citations are persisted, while `allow_new_research=true` is explicitly rejected. Owners may change that evidence set only after the job stops via `PATCH /sessions/{id}/sources/{source_id}`; this applies to future follow-ups rather than rewriting a completed report. Optional report illustrations are gated by `config.yaml -> deep_research.image_generation`: the Harness `OpenAICompatibleImageProvider` requires base64 output, the shared runner step writes only bounded `research-image-01..04.(png|jpg|webp)` artifacts and Markdown uses private `deep-research://` links. `GET /sessions/{id}/artifacts/{name}` checks session ownership before serving an image; an unavailable provider yields a warning but never fails the text report. Runners live in `packages/harness/deerflow/agents/deep_research/runners/`, may never import `app.*`, and use only the `AdapterBundle`. |
 | **Gateway auth** | Owner-scoped Deep Research routes await the platform's asynchronous `get_current_user` resolver before they read or write persistence, ensuring each session and job operation receives a concrete user ID. Deep Research artifacts are always resolved from a whitelisted filename via `/mnt/user-data/outputs/<name>` before mapping to a host path, preserving the sandbox guard on native Windows and Linux hosts. |
@@ -1611,8 +1611,10 @@ same DOM value, so a fetched-and-revoked object URL is not acceptable.
 Third-party pages embed published Wiki content through the chrome-free route
 `/#/embed/knowledge/{mapping_id}` (optional exact-page `wikiSlug`). It defaults
 to a recognized Wiki index page (title `索引`/`index`, page type `index`, or an
-`index`/`home`/`readme` slug), promotes that page to the first public-directory
-entry, then falls back to an overview/root page when no index exists. It retains
+`index`/`home`/`readme` slug) and promotes that page to the first public-directory
+entry. When upstream pages exist without an explicit index shell, the frontend
+injects a virtual `__index__` page and builds its catalogue from the page list;
+an all-empty dynamic index response must also fall back to that page list. It retains
 directory navigation and supports the shared public-embed `theme` / `bg` /
 `text` appearance inputs. WeKnora dynamically renders index page-type counts,
 nested category headings, page summaries, and `data-slug` links even though the
@@ -1628,9 +1630,14 @@ the loaded page title, slug, or alias and rewrites them to exact embedded
 `wikiSlug` routes. Preserve WeKnora's `wiki-content-link` interaction: primary
 brand color, medium weight, dashed underline becoming solid on hover, focus
 outline, and in-reader SPA navigation rather than a page reload. `GET
-/api/public/llmwiki/knowledge-bases` is the
-anonymous discovery endpoint and returns only each published ordinary mapping's
-`id` and `name` (plus the collection total). All anonymous reader calls use that
+/api/public/llmwiki/knowledge-bases` is the anonymous discovery endpoint. It
+validates published ordinary mappings against WeKnora, drops stale mappings,
+and returns safe comprehensive metadata from the WeKnora list/detail/page APIs:
+document/chunk/processing/share counts, non-secret configuration and timestamps,
+Wiki page type/status counts and explicit/generated index entry, plus aggregate
+local-vector state/counts/timestamps. Never expose remote WeKnora ids,
+tenant/creator identity, storage credentials, API credentials, raw vectors, or
+per-page indexing errors. All anonymous reader calls use that
 public namespace and the `/knowledge-bases/{id}` Wiki-page children. Those
 endpoints must resolve with a synthetic anonymous user and `is_admin=False`,
 explicitly require `publication_status=published`, and hard-deny the system
@@ -1709,3 +1716,44 @@ before MarkItDown extraction, avoiding concurrent profile locks. The image
 build and `OFFLINE_INSTALL.sh --office-check` both run
 `scripts/office_runtime_selfcheck.py`; that smoke test creates and re-parses
 minimal DOCX/XLSX/PPTX files and does not need the application database.
+
+## Skill/assistant knowledge invariants
+
+Skill distillation is implemented in `deerflow.skill_knowledge` and
+`deerflow.persistence.skill_knowledge`; native assistant bases are in
+`deerflow.assistant_knowledge` and
+`deerflow.persistence.assistant_knowledge`. Gateway composition, remote adapters,
+the lease dispatcher, and APIs remain in `app.gateway` so the harness-to-app
+import firewall is preserved.
+
+Never execute a skill or read/upload/summarize its code and script files.
+Knowledge fingerprints exclude those files and Markdown code fences. All
+resyncs must stage and validate the knowledge digest before activation; the old
+snapshot remains authoritative on failure. A binding may have only one active
+item (`active_dedupe_key` plus atomic lease claims). Skill distillation targets
+only writable WeKnora Wiki knowledge bases in this product version; reject direct
+assistant-base targets, non-Wiki/FAQ/conversation/readonly targets, and direct
+Neo4j writes. Detach/delete operates on source contributions, not canonical rows,
+and shared entities/relations are retired only when their final contribution is
+gone.
+
+Assistant knowledge has one global full-library base (`知识梳理总库`) and any
+number of admin-created custom bases. Ordinary users have read/search access and
+administrators own all mutations. Assistant bases only accept WeKnora export
+packages or imports triggered from a WeKnora mapping. Do not add direct skill
+imports or arbitrary local-file imports to assistant knowledge. Importing into a
+custom assistant base must also mirror the package into the global base for
+cross-source entity alignment.
+
+## openGauss / GaussDB persistence invariant
+
+`database.backend=gauss` is an application-ORM backend, parallel to `mysql`.
+It normalizes `database.gauss_url` to `opengauss+asyncpg://` and requires the
+`gauss` optional dependency (`asyncpg` plus `opengauss-sqlalchemy`). Never route
+a Gauss URL through SQLAlchemy's generic PostgreSQL dialect: the wire protocol
+may connect, but dialect initialization and schema DDL can fail against the
+Gauss server/version behavior. Keep the LangGraph checkpointer on the separately
+configured SQLite backend; the PostgreSQL checkpointer schema is not part of the
+Gauss compatibility contract. Treat both `postgresql` and `opengauss` dialect
+names as timezone-aware PostgreSQL-family engines in portable types and
+dialect-specific session settings.

offline-backend-20260512/backend/Dockerfile

@@ -13,8 +13,8 @@ FROM python:3.12-slim-bookworm AS builder
 ARG NODE_MAJOR=22
 ARG APT_MIRROR
 ARG UV_INDEX_URL
-# Optional extras to install (e.g. "postgres" for PostgreSQL support)
-# Usage: docker build --build-arg UV_EXTRAS=postgres ...
+# Optional extras to install (e.g. "postgres" or "gauss")
+# Usage: docker build --build-arg UV_EXTRAS=gauss ...
 ARG UV_EXTRAS
 
 # Optionally override apt mirror for restricted networks (e.g. APT_MIRROR=mirrors.aliyun.com)

offline-backend-20260512/backend/README.md

@@ -158,6 +158,7 @@ FastAPI application providing REST endpoints for frontend integration:
 | `POST /api/threads/{id}/uploads` | Upload files (auto-converts PDF/PPT/Excel/Word to Markdown, rejects directory paths) |
 | `GET /api/threads/{id}/uploads/list` | List uploaded files |
 | `DELETE /api/threads/{id}` | Delete DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail |
+| `GET /api/artifact-library` | List the current user's generated conversation files across normal threads, with filename/conversation search, file-kind filtering, pagination, and an optional cache-bypassing refresh. The filesystem `outputs/` directories remain authoritative; uploads and image/audio/video files are excluded. |
 | `GET /api/threads/{id}/artifacts/{path}` | Serve generated artifacts |
 | `GET/POST /api/deep-research/*` | Durable Deep Research sessions and jobs. `quick`/`basic` run the compact report flow; `detailed` plans subtopics and writes independent sections; `deep` recursively investigates bounded follow-up branches. All modes reuse DeerFlow's configured material provider, LLM factory, hidden-thread artifacts, and SSE event log. Deep Research session/job/event/source/message writes serialise their rows in the writer transaction, rather than doing a post-commit ORM refresh or readback; this prevents MySQL read/write-splitting replica lag from failing “start writing”, dispatcher claim, report persistence, or follow-up history. Whole-document artifact/report rewrites likewise create their reversible version snapshots from writer-local ORM data and never refresh after commit, so replica lag cannot restore an otherwise successful rewrite. A report is written to the session and confirmed before its Job becomes `completed`; an unconfirmed write is retried and never exposed as a false completion. Retrying an already-created queued job re-nudges the dispatcher, recovering an interrupted HTTP response without creating a second report. If progress-event persistence, durable replay, or live fan-out is briefly unavailable, the worker/stream logs the degradation and continues or reconnects rather than failing the whole job. Whole-report rewrite accepts an optional `reportOutline` Markdown template; the durable job persists it and applies it as the chapter structure constraint. Optional report illustrations use a separately configured OpenAI-compatible image endpoint and are served only through the owning session's authenticated artifact route. |
 | `/api/enterprise-research/*` | Temporarily disabled. Its router and persistence initialization are not registered, so these endpoints return 404 and no enterprise-research tables are created or migrated during startup. |
@@ -988,8 +989,10 @@ report-structure column for databases stamped by either old variant.
 Published knowledge bases also have a login-free, chrome-free reader at
 `/#/embed/knowledge/{mapping_id}` for direct third-party iframe use. With no
 `wikiSlug`, it selects the Wiki **索引** page first (recognized by title, page
-type, or `index`/`home`/`readme` slug), pins that page to the top of the public
-directory, and otherwise falls back to an overview/root page;
+type, or `index`/`home`/`readme` slug) and pins that page to the top of the
+public directory. If WeKnora has pages but no explicit index page, the reader
+creates a virtual `__index__` landing page from the public directory; empty
+dynamic-index groups also fall back to those page summaries;
 `?wikiSlug=...` deep-links one page. The reader supports
 `theme=light|dark`, `bg`, and `text` embed appearance parameters. Index-article
 catalogue sections (page-type counts, nested categories, page summaries) are
@@ -1005,8 +1008,14 @@ only from the read-only `/api/public/llmwiki/knowledge-bases/{id}/...` namespace
 which rechecks `publication_status=published` on every request and denies the
 system conversation-deposit knowledge base. Private or unpublished bases are
 never exposed by this route. Anonymous consumers can discover the available
-public Wiki libraries through `GET /api/public/llmwiki/knowledge-bases`; each
-entry deliberately contains only the DeerFlow mapping `id` and display `name`.
+public Wiki libraries through `GET /api/public/llmwiki/knowledge-bases`. The
+endpoint validates each published mapping against WeKnora, omits stale remote
+mappings, and returns safe comprehensive metadata: description/type,
+document/chunk/processing/share counts, non-secret configuration and
+timestamps, Wiki page/type/status counts plus explicit/generated index mode and
+default embed route, and aggregate local-vector state/counts/timestamps. It
+never returns the WeKnora id, tenant/creator identity, storage credentials, API
+credentials, raw vectors, or per-page indexing errors.
 Protected `resource://` and supported object-storage images are fetched through
 the current mapping instead of being exposed as upstream URLs. Authenticated
 readers use `GET /api/llmwiki/knowledge-bases/{id}/files?file_path=...`; public
@@ -1077,3 +1086,58 @@ uploads are parsed directly; legacy DOC/XLS/PPT and ODT/ODS/ODP files are
 normalized through an isolated LibreOffice profile before Markdown extraction.
 Run `./OFFLINE_INSTALL.sh --office-check` from the offline bundle to generate
 and re-parse Word, Excel, and PowerPoint samples without requiring a database.
+
+## Skill knowledge distillation and assistant knowledge
+
+The Gateway now exposes an administrator-only skill distillation pipeline at
+`/api/skill-knowledge`. Each request can send up to 200 skills to one writable
+WeKnora Wiki knowledge base; multi-base distillation is done by repeated
+operations so the local binding remains `skill_name + target_id`. The scanner
+never reads or uploads code/script files, strips Markdown code fences, blocks
+credential-like files and content, safely inspects ZIP members, and converts
+supported Office/PDF/spreadsheet files off the event loop. Jobs use database
+claims and renewable leases, stage snapshots before activation, retain the
+prior active version on failure, support per-item retry, and keep per-binding
+contribution records for safe detach/delete. Direct assistant-base targets,
+non-Wiki/FAQ/conversation/readonly targets, and direct Neo4j writes are rejected
+for this product version.
+
+`/api/assistant-knowledge` manages DeerFlow-native assistant knowledge bases.
+There is one global full-library base named `知识梳理总库`, plus admin-created
+custom assistant bases. Reads and search are available to authenticated users;
+global initialization, custom-base creation, WeKnora export/import, source
+reimport, and source deletion are admin-only. Assistant bases only accept
+WeKnora export packages or imports triggered from a WeKnora mapping; direct
+skill import and arbitrary local-file import return 410. Importing into a custom
+assistant base also mirrors the same package into the global base, where
+canonical entities and relations are aligned across all imported sources.
+The database schema is introduced by Alembic revision `20260901_01`.
+
+## openGauss / GaussDB application database
+
+The application ORM can use a PG-compatible openGauss/GaussDB database through
+the official `opengauss-sqlalchemy` async dialect. Install the optional driver
+set before enabling it:
+
+```bash
+uv sync --extra gauss
+```
+
+Configure the business database independently from the LangGraph checkpointer:
+
+```yaml
+database:
+  backend: gauss
+  gauss_url: $GAUSS_DATABASE_URL
+
+checkpointer:
+  type: sqlite
+  connection_string: .deer-flow/data/deerflow.db
+```
+
+`GAUSS_DATABASE_URL` should normally be
+`opengauss+asyncpg://user:password@host:26000/deerflow`. Plain
+`opengauss://`, `gaussdb://`, and PostgreSQL-style URLs are accepted and
+normalized to the openGauss dialect. Do not configure the LangGraph
+PostgreSQL checkpointer against GaussDB: its migrations are PostgreSQL-specific.
+For a production image, build with `--build-arg UV_EXTRAS=gauss`.

offline-backend-20260512/backend/app/gateway/app.py

@@ -32,6 +32,7 @@ from app.gateway.routers import (
     agents,
     ai_writing,
     artifacts,
+    assistant_knowledge,
     assistants_compat,
     auth,
     browser_context,
@@ -87,6 +88,7 @@ from app.gateway.routers import (
     sensitive_words,
     sentiment_agent,
     skill_addresses,
+    skill_knowledge,
     skills,
     suggestions,
     system_settings,
@@ -1580,6 +1582,10 @@ This gateway provides custom endpoints for models, MCP configuration, skills, an
 
     # Skills API is mounted at /api/skills
     app.include_router(skills.router)
+    # Admin skill distillation matrix + global assistant knowledge base.
+    app.include_router(skill_knowledge.router)
+    app.include_router(assistant_knowledge.router)
+    app.include_router(assistant_knowledge.export_router)
 
     # Artifacts API is mounted at /api/threads/{thread_id}/artifacts
     app.include_router(artifacts.router)

offline-backend-20260512/backend/app/gateway/deps.py

@@ -331,6 +331,33 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
         app.state.llmwiki_store = make_llmwiki_store(sf)
         register_llmwiki_store(app.state.llmwiki_store)
 
+        # Skill distillation and the single global assistant knowledge base.
+        # Both repositories live in ``deerflow.*``; only this composition layer
+        # wires them to Gateway executors and remote adapters.
+        from deerflow.persistence.assistant_knowledge import make_assistant_knowledge_store
+        from deerflow.persistence.skill_knowledge import make_skill_knowledge_store
+
+        app.state.assistant_knowledge_store = make_assistant_knowledge_store(sf)
+        app.state.skill_knowledge_store = make_skill_knowledge_store(sf)
+        if app.state.skill_knowledge_store is not None and app.state.assistant_knowledge_store is not None:
+            from app.gateway.skill_knowledge_job_dispatcher import SkillKnowledgeJobDispatcher
+            from app.gateway.skill_knowledge_job_executor import SkillKnowledgeJobExecutor
+
+            app.state.skill_knowledge_executor = SkillKnowledgeJobExecutor(app)
+            app.state.skill_knowledge_dispatcher = SkillKnowledgeJobDispatcher(
+                app.state.skill_knowledge_store,
+                app.state.skill_knowledge_executor,
+            )
+            app.state.skill_knowledge_dispatcher.start()
+            from app.gateway.skill_knowledge_auto_sync import SkillKnowledgeAutoSyncService
+
+            app.state.skill_knowledge_auto_sync = SkillKnowledgeAutoSyncService(app)
+            app.state.skill_knowledge_auto_sync.start()
+        else:
+            app.state.skill_knowledge_executor = None
+            app.state.skill_knowledge_dispatcher = None
+            app.state.skill_knowledge_auto_sync = None
+
         # DeerFlow-local Wiki mirror/vector index. The store is always wired so
         # migrations and status inspection work while feature flags are off;
         # network/search services only exist when explicitly enabled.
@@ -351,13 +378,21 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
 
         resolved_llmwiki = get_resolved_llmwiki_runtime(config)
         weknora_client = build_weknora_client(resolved_llmwiki) if resolved_llmwiki.weknora_enabled else None
-        if local_index_config.enabled:
+        embedding_config = local_index_config.embedding
+        embedding_configured = bool(
+            embedding_config.enabled
+            and embedding_config.base_url
+            and embedding_config.model.strip()
+        )
+        if embedding_configured:
             from deerflow.integrations.weknora.local_index.embedding import StrictWikiEmbeddingClient
+
+            app.state.llmwiki_embedding = StrictWikiEmbeddingClient(embedding_config)
+        if local_index_config.enabled and app.state.llmwiki_embedding is not None:
             from deerflow.integrations.weknora.local_index.search import WikiVectorSearchService
             from deerflow.integrations.weknora.local_index.sync import WikiSyncService
             from deerflow.integrations.weknora.local_index.vector_cache import WikiVectorCache
 
-            app.state.llmwiki_embedding = StrictWikiEmbeddingClient(local_index_config.embedding)
             app.state.llmwiki_vector_cache = WikiVectorCache(
                 app.state.llmwiki_index_store,
                 max_knowledge_bases=local_index_config.cache_max_knowledge_bases,
@@ -649,6 +684,12 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
             from deerflow.workflows.runtime.sql_runner import dispose_engines
 
             await dispose_engines()
+            skill_knowledge_auto_sync = getattr(app.state, "skill_knowledge_auto_sync", None)
+            if skill_knowledge_auto_sync is not None:
+                await skill_knowledge_auto_sync.close()
+            skill_knowledge_dispatcher = getattr(app.state, "skill_knowledge_dispatcher", None)
+            if skill_knowledge_dispatcher is not None:
+                await skill_knowledge_dispatcher.close()
             queue = getattr(app.state, "knowledge_auto_ingest_queue", None)
             if queue is not None:
                 from deerflow.knowledge.auto_ingest import set_auto_ingest_queue

offline-backend-20260512/backend/app/gateway/llmwiki_rag.py

@@ -40,6 +40,7 @@ _MAX_SOURCE_CHARS = 1400
 @dataclass(frozen=True)
 class LlmWikiRagContext:
     knowledge_base_ids: list[str]
+    assistant_knowledge_base_ids: list[str]
     source_count: int
     prompt: str
     sources: list[dict[str, Any]]
@@ -50,6 +51,10 @@ def _store(request: Request) -> LlmWikiStore | None:
     return getattr(request.app.state, "llmwiki_store", None)
 
 
+def _assistant_store(request: Request) -> Any | None:
+    return getattr(request.app.state, "assistant_knowledge_store", None)
+
+
 async def _actor(request: Request) -> tuple[str, bool]:
     user = await get_optional_user_from_request(request)
     if user is None:
@@ -76,6 +81,13 @@ def _body_selected_ids(body: Any) -> list[str]:
     return []
 
 
+def _body_selected_assistant_ids(body: Any) -> list[str]:
+    for source in (getattr(body, "context", None), getattr(body, "metadata", None)):
+        if isinstance(source, Mapping) and "assistant_knowledge_base_ids" in source:
+            return _as_id_list(source.get("assistant_knowledge_base_ids"))
+    return []
+
+
 def _assistant_agent_id(body: Any) -> str | None:
     assistant_id = validate_agent_id(getattr(body, "assistant_id", None))
     if assistant_id:
@@ -106,8 +118,17 @@ def resolve_requested_llmwiki_knowledge_base_ids(body: Any) -> list[str]:
     return _body_selected_ids(body)
 
 
+def resolve_requested_assistant_knowledge_base_ids(body: Any) -> list[str]:
+    """Return assistant knowledge bases explicitly selected for the run."""
+
+    return _body_selected_assistant_ids(body)
+
+
 def llmwiki_selection_requested(body: Any) -> bool:
-    return bool(resolve_requested_llmwiki_knowledge_base_ids(body))
+    return bool(
+        resolve_requested_llmwiki_knowledge_base_ids(body)
+        or resolve_requested_assistant_knowledge_base_ids(body)
+    )
 
 
 def _latest_human_query(graph_input: dict[str, Any]) -> str:
@@ -191,18 +212,50 @@ def _source_from_result(result: dict[str, Any], mapping: dict[str, Any], index:
     }
 
 
+def _source_from_assistant_result(result: dict[str, Any], index: int) -> dict[str, Any]:
+    title = str(result.get("title") or f"助手知识库片段 {index}")
+    matched_section = result.get("matched_section") if isinstance(result.get("matched_section"), Mapping) else {}
+    content = str(
+        matched_section.get("content")
+        or result.get("content_markdown")
+        or result.get("content")
+        or result.get("summary")
+        or ""
+    )
+    snippet = str(result.get("summary") or content)
+    raw = {
+        **result,
+        "source": "助手知识库",
+        "url": "/page/workspace/knowledge/assistant",
+    }
+    return {
+        "index": index,
+        "id": result.get("id") or result.get("slug") or f"assistant:{index}",
+        "title": title,
+        "type": "assistant_knowledge",
+        "snippet": _truncate(snippet, 360),
+        "content": content,
+        "source": "助手知识库",
+        "score": float(result.get("score") or 0),
+        "url": "/page/workspace/knowledge/assistant",
+        "skillName": "assistant_knowledge_search",
+        "mode": "citation",
+        "raw": raw,
+    }
+
+
 def _build_prompt(query: str, sources: list[dict[str, Any]]) -> str:
     if not sources:
-        return "当前用户选择了 LLMWiki 知识库，但 DeerFlow 未检索到可用片段。请直接说明没有检索到可用背景资料，不要编造知识库内容或引用编号。"
+        return "当前用户选择了知识库，但 DeerFlow 未检索到可用片段。请直接说明没有检索到可用背景资料，不要编造知识库内容或引用编号。"
 
     lines = [
-        "当前用户选择了 LLMWiki 知识库。DeerFlow 已在模型回答前自动检索到以下背景资料。",
+        "当前用户选择了知识库。DeerFlow 已在模型回答前自动检索到以下背景资料。",
         "请优先依据这些资料回答；如果资料不足，请明确说明不足之处，不要编造。",
         "引用规则：正文关键结论必须使用 [1]、[2] 这样的编号引用下方资料；只能引用存在的编号；不要在正文末尾重复输出完整参考文献列表，前端会展示引用卡片。",
         "",
         f"用户问题：{query}",
         "",
-        f'<llmwiki_wiki_reference_batch source_count="{len(sources)}">',
+        f'<knowledge_reference_batch source_count="{len(sources)}">',
     ]
     for source in sources:
         meta = " / ".join(
@@ -213,12 +266,12 @@ def _build_prompt(query: str, sources: list[dict[str, Any]]) -> str:
             )
             if item
         )
-        lines.append(f"[{source['index']}] {source.get('title') or 'LLMWiki 片段'}")
+        lines.append(f"[{source['index']}] {source.get('title') or '知识库片段'}")
         if meta:
             lines.append(f"来源：{meta}")
         lines.append(_truncate(str(source.get("content") or source.get("snippet") or ""), _MAX_SOURCE_CHARS))
         lines.append("")
-    lines.append("</llmwiki_wiki_reference_batch>")
+    lines.append("</knowledge_reference_batch>")
     return "\n".join(lines).strip()
 
 
@@ -229,52 +282,99 @@ async def build_llmwiki_rag_context(
     graph_input: dict[str, Any],
 ) -> LlmWikiRagContext:
     selected_ids = resolve_requested_llmwiki_knowledge_base_ids(body)
-    if not selected_ids:
-        return LlmWikiRagContext([], 0, "", [], "")
-
-    runtime = get_resolved_llmwiki_runtime(getattr(request.app.state, "config", None))
-    local_config = request.app.state.config.llmwiki.local_wiki_index
-    if not (local_config.enabled and local_config.internal_search_enabled) and not runtime.weknora_enabled:
-        return LlmWikiRagContext(selected_ids, 0, "", [], "")
-    store = _store(request)
-    if store is None:
-        return LlmWikiRagContext(selected_ids, 0, "", [], "")
+    assistant_selected_ids = resolve_requested_assistant_knowledge_base_ids(body)
+    if not selected_ids and not assistant_selected_ids:
+        return LlmWikiRagContext([], [], 0, "", [], "")
 
     query = _latest_human_query(graph_input)
     if not query:
-        return LlmWikiRagContext(selected_ids, 0, "", [], "")
+        return LlmWikiRagContext(selected_ids, assistant_selected_ids, 0, "", [], "")
 
-    user_id, is_admin = await _actor(request)
-    rows: list[dict[str, Any]] = []
-    for mapping_id in selected_ids:
-        row = await store.get_authorized(mapping_id, user_id, write=False, is_admin=is_admin)
-        if row is None:
-            raise HTTPException(status_code=404, detail=f"Knowledge base {mapping_id} not found")
-        rows.append(row)
-    search_service = getattr(request.app.state, "llmwiki_retrieval_service", None)
-    if search_service is None:
-        return LlmWikiRagContext(
-            selected_ids,
-            0,
-            "Wiki 检索暂不可用。请明确说明系统检索不可用，不要表述为资料不存在。",
-            [],
-            query,
-        )
-    try:
-        payload = await search_service.search(query, rows, top_k_pages=_MAX_SOURCES)
-    except WikiIndexError as exc:
-        logger.warning("Wiki-only pre-retrieval failed user=%s kb_count=%s code=%s", sanitize_log_param(user_id), len(rows), exc.code)
-        return LlmWikiRagContext(selected_ids, 0, f"Wiki 检索暂不可用（{exc.code}）。请明确说明系统检索状态，不要声称知识库没有资料。", [], query)
-    mapping_by_id = {str(row["id"]): row for row in rows}
     sources: list[dict[str, Any]] = []
-    for result in payload.get("results") or []:
-        mapping = mapping_by_id.get(str(result.get("knowledge_base_id") or ""))
-        if mapping is None:
-            continue
-        sources.append(_source_from_result(result, mapping, len(sources) + 1))
-        if len(sources) >= _MAX_SOURCES:
-            break
-    return LlmWikiRagContext(selected_ids, len(sources), _build_prompt(query, sources), sources, query)
+
+    runtime = get_resolved_llmwiki_runtime(getattr(request.app.state, "config", None))
+    local_config = request.app.state.config.llmwiki.local_wiki_index
+    user_id, is_admin = await _actor(request)
+
+    if selected_ids:
+        llmwiki_available = (local_config.enabled and local_config.internal_search_enabled) or runtime.weknora_enabled
+        store = _store(request)
+        if not llmwiki_available or store is None:
+            if not assistant_selected_ids:
+                return LlmWikiRagContext(selected_ids, assistant_selected_ids, 0, "", [], query)
+        else:
+            rows: list[dict[str, Any]] = []
+            for mapping_id in selected_ids:
+                row = await store.get_authorized(mapping_id, user_id, write=False, is_admin=is_admin)
+                if row is None:
+                    raise HTTPException(status_code=404, detail=f"Knowledge base {mapping_id} not found")
+                rows.append(row)
+            search_service = getattr(request.app.state, "llmwiki_retrieval_service", None)
+            if search_service is None:
+                if not assistant_selected_ids:
+                    return LlmWikiRagContext(
+                        selected_ids,
+                        assistant_selected_ids,
+                        0,
+                        "Wiki 检索暂不可用。请明确说明系统检索不可用，不要表述为资料不存在。",
+                        [],
+                        query,
+                    )
+            try:
+                payload = (
+                    await search_service.search(query, rows, top_k_pages=_MAX_SOURCES)
+                    if search_service is not None
+                    else {"results": []}
+                )
+            except WikiIndexError as exc:
+                logger.warning(
+                    "Wiki-only pre-retrieval failed user=%s kb_count=%s code=%s",
+                    sanitize_log_param(user_id),
+                    len(rows),
+                    exc.code,
+                )
+                if not assistant_selected_ids:
+                    return LlmWikiRagContext(
+                        selected_ids,
+                        assistant_selected_ids,
+                        0,
+                        f"Wiki 检索暂不可用（{exc.code}）。请明确说明系统检索状态，不要声称知识库没有资料。",
+                        [],
+                        query,
+                    )
+                payload = {"results": []}
+            mapping_by_id = {str(row["id"]): row for row in rows}
+            for result in payload.get("results") or []:
+                mapping = mapping_by_id.get(str(result.get("knowledge_base_id") or ""))
+                if mapping is None:
+                    continue
+                sources.append(_source_from_result(result, mapping, len(sources) + 1))
+                if len(sources) >= _MAX_SOURCES:
+                    break
+
+    if assistant_selected_ids and len(sources) < _MAX_SOURCES:
+        assistant_store = _assistant_store(request)
+        if assistant_store is not None:
+            rows = await assistant_store.search_pages(
+                base_ids=assistant_selected_ids,
+                query=query,
+                limit=_MAX_SOURCES - len(sources),
+                include_chunks=True,
+                embedding_client=getattr(request.app.state, "llmwiki_embedding", None),
+            )
+            for row in rows:
+                sources.append(_source_from_assistant_result(row, len(sources) + 1))
+                if len(sources) >= _MAX_SOURCES:
+                    break
+
+    return LlmWikiRagContext(
+        selected_ids,
+        assistant_selected_ids,
+        len(sources),
+        _build_prompt(query, sources),
+        sources,
+        query,
+    )
 
 
 def attach_llmwiki_rag_to_graph_input(
@@ -294,6 +394,7 @@ def attach_llmwiki_rag_to_graph_input(
         existing = str(extra.get("prompt_prefix") or "").strip()
         extra["prompt_prefix"] = f"{existing}\n\n{rag.prompt}".strip() if existing else rag.prompt
         extra["llmwiki_knowledge_base_ids"] = rag.knowledge_base_ids
+        extra["assistant_knowledge_base_ids"] = rag.assistant_knowledge_base_ids
         extra["llmwiki_reference_query"] = rag.query
         if rag.sources:
             extra["llmwiki_reference_sources"] = rag.sources

offline-backend-20260512/backend/app/gateway/routers/admin_users.py

@@ -461,7 +461,7 @@ async def _set_analytics_read_timeout(session) -> None:
       - MariaDB:        ``max_statement_time`` (seconds)
       - MySQL <= 5.6:   no per-statement timeout variable — skip it entirely
                         (5.6 raises "Unknown system variable" otherwise)
-      - PostgreSQL:     ``statement_timeout`` (milliseconds)
+      - PostgreSQL/openGauss: ``statement_timeout`` (milliseconds)
     """
     conn = await session.connection()
     dialect = conn.dialect
@@ -474,7 +474,7 @@ async def _set_analytics_read_timeout(session) -> None:
             await _try_set_session_value(session, f"SET SESSION max_execution_time = {_LEADERBOARD_QUERY_TIMEOUT_MS}")
         # else: MySQL <= 5.6 has no statement-timeout variable — skip silently.
         await _try_set_session_value(session, f"SET SESSION innodb_lock_wait_timeout = {_LEADERBOARD_LOCK_WAIT_SECONDS}")
-    elif name == "postgresql":
+    elif name in {"postgresql", "opengauss"}:
         await _try_set_session_value(session, f"SET LOCAL statement_timeout = {_LEADERBOARD_QUERY_TIMEOUT_MS}")
 
 

offline-backend-20260512/backend/app/gateway/routers/artifacts.py

@@ -1,11 +1,15 @@
+import asyncio
 import logging
 import mimetypes
+import time
 import zipfile
-from datetime import datetime, timezone
+from collections import OrderedDict
+from datetime import UTC, datetime
 from pathlib import Path
+from typing import Literal
 from urllib.parse import quote
 
-from fastapi import APIRouter, HTTPException, Request
+from fastapi import APIRouter, HTTPException, Query, Request
 from fastapi.responses import FileResponse, PlainTextResponse, Response
 from pydantic import BaseModel
 
@@ -13,6 +17,7 @@ from app.gateway.authz import require_permission
 from app.gateway.path_utils import aresolve_thread_virtual_path
 from deerflow.config.paths import get_paths
 from deerflow.runtime.thread_paths import aresolve_path_user_id
+from deerflow.runtime.user_context import get_effective_user_id
 
 logger = logging.getLogger(__name__)
 
@@ -38,6 +43,85 @@ EDITABLE_TEXT_SUFFIXES = {
 
 _VIRTUAL_USER_DATA_PREFIX = "mnt/user-data"
 
+ArtifactLibraryKind = Literal["all", "document", "spreadsheet", "text_data", "archive", "other"]
+ArtifactLibrarySource = Literal["all", "general_qa", "agent"]
+
+_MEDIA_MIME_PREFIXES = ("image/", "audio/", "video/")
+_MEDIA_SUFFIXES = {
+    ".aac",
+    ".aiff",
+    ".ape",
+    ".avi",
+    ".bmp",
+    ".flac",
+    ".gif",
+    ".heic",
+    ".ico",
+    ".jpeg",
+    ".jpg",
+    ".m4a",
+    ".m4v",
+    ".mkv",
+    ".mov",
+    ".mp3",
+    ".mp4",
+    ".ogg",
+    ".png",
+    ".svg",
+    ".tiff",
+    ".wav",
+    ".webm",
+    ".webp",
+    ".wma",
+}
+_DOCUMENT_SUFFIXES = {
+    ".doc",
+    ".docx",
+    ".epub",
+    ".odt",
+    ".pdf",
+    ".ppt",
+    ".pptx",
+    ".rtf",
+}
+_SPREADSHEET_SUFFIXES = {".csv", ".ods", ".tsv", ".xls", ".xlsx"}
+_TEXT_DATA_SUFFIXES = {
+    ".bash",
+    ".c",
+    ".conf",
+    ".cpp",
+    ".css",
+    ".go",
+    ".h",
+    ".html",
+    ".ini",
+    ".java",
+    ".js",
+    ".json",
+    ".jsonl",
+    ".log",
+    ".markdown",
+    ".md",
+    ".mmd",
+    ".py",
+    ".rs",
+    ".sh",
+    ".sql",
+    ".toml",
+    ".ts",
+    ".tsx",
+    ".txt",
+    ".xml",
+    ".yaml",
+    ".yml",
+}
+_ARCHIVE_SUFFIXES = {".7z", ".gz", ".rar", ".skill", ".tar", ".tgz", ".zip"}
+_ARTIFACT_LIBRARY_CACHE_TTL_SECONDS = 15.0
+_ARTIFACT_LIBRARY_CACHE_MAX_USERS = 128
+_ARTIFACT_LIBRARY_SCAN_CONCURRENCY = 8
+_ARTIFACT_LIBRARY_THREAD_BATCH_SIZE = 500
+_artifact_library_cache: OrderedDict[str, tuple[float, list[dict]]] = OrderedDict()
+
 
 def coerce_artifact_virtual_path(path: str) -> str:
     """Normalize an artifacts URL path to a sandbox virtual path.
@@ -62,6 +146,28 @@ class ArtifactUpdateRequest(BaseModel):
     content: str
 
 
+class ArtifactLibraryItem(BaseModel):
+    thread_id: str
+    thread_title: str
+    agent_id: str | None = None
+    agent_name: str | None = None
+    thread_type: str | None = None
+    source_type: Literal["general_qa", "agent"]
+    path: str
+    name: str
+    size_bytes: int
+    mime_type: str | None = None
+    modified_at: str
+    file_kind: str
+
+
+class ArtifactLibraryResponse(BaseModel):
+    total: int
+    offset: int
+    limit: int
+    items: list[ArtifactLibraryItem]
+
+
 def _build_content_disposition(disposition_type: str, filename: str) -> str:
     """Build an RFC 5987 encoded Content-Disposition header value."""
     return f"{disposition_type}; filename*=UTF-8''{quote(filename)}"
@@ -139,16 +245,22 @@ def collect_outputs_listing(outputs_dir: Path) -> list[dict]:
             resolved.relative_to(outputs_root)
         except (OSError, ValueError):
             continue
-        relative = resolved.relative_to(outputs_root).as_posix()
-        mime_type, _ = mimetypes.guess_type(resolved.name)
-        stat = resolved.stat()
+        try:
+            relative = resolved.relative_to(outputs_root).as_posix()
+            mime_type, _ = mimetypes.guess_type(resolved.name)
+            stat = resolved.stat()
+        except OSError:
+            # A run may atomically replace or remove a file while this directory
+            # is being scanned. Treat that transient entry as absent instead of
+            # failing the complete thread/library listing.
+            continue
         files.append(
             {
                 "path": f"mnt/user-data/outputs/{relative}",
                 "name": resolved.name,
                 "size_bytes": stat.st_size,
                 "mime_type": mime_type,
-                "modified_at": datetime.fromtimestamp(stat.st_mtime, tz=timezone.utc).isoformat(),
+                "modified_at": datetime.fromtimestamp(stat.st_mtime, tz=UTC).isoformat(),
             }
         )
 
@@ -156,6 +268,190 @@ def collect_outputs_listing(outputs_dir: Path) -> list[dict]:
     return files
 
 
+def artifact_library_kind(name: str) -> str:
+    """Classify one non-media artifact for the library filters."""
+    suffix = Path(name).suffix.lower()
+    if suffix in _DOCUMENT_SUFFIXES:
+        return "document"
+    if suffix in _SPREADSHEET_SUFFIXES:
+        return "spreadsheet"
+    if suffix in _TEXT_DATA_SUFFIXES:
+        return "text_data"
+    if suffix in _ARCHIVE_SUFFIXES:
+        return "archive"
+    return "other"
+
+
+def is_artifact_library_file(file: dict) -> bool:
+    """Return whether a generated file belongs in the document-only library."""
+    mime_type = str(file.get("mime_type") or "").lower()
+    if mime_type.startswith(_MEDIA_MIME_PREFIXES):
+        return False
+    return Path(str(file.get("name") or file.get("path") or "")).suffix.lower() not in _MEDIA_SUFFIXES
+
+
+async def _search_artifact_library_threads(thread_store) -> list[dict]:
+    """Load every normal conversation owned by the current request user."""
+    rows: list[dict] = []
+    seen: set[str] = set()
+    offset = 0
+    while True:
+        batch = await thread_store.search(
+            limit=_ARTIFACT_LIBRARY_THREAD_BATCH_SIZE,
+            offset=offset,
+            exclude_system=True,
+        )
+        if not batch:
+            break
+        added = 0
+        for row in batch:
+            thread_id = str(row.get("thread_id") or "")
+            if not thread_id or thread_id in seen:
+                continue
+            seen.add(thread_id)
+            rows.append(row)
+            added += 1
+        if len(batch) < _ARTIFACT_LIBRARY_THREAD_BATCH_SIZE or added == 0:
+            break
+        offset += len(batch)
+    return rows
+
+
+async def _scan_artifact_library(thread_store) -> list[dict]:
+    """Scan generated outputs for all current-user conversation threads."""
+    rows = await _search_artifact_library_threads(thread_store)
+    paths = get_paths()
+    semaphore = asyncio.Semaphore(_ARTIFACT_LIBRARY_SCAN_CONCURRENCY)
+
+    async def scan_thread(row: dict) -> list[dict]:
+        thread_id = str(row.get("thread_id") or "")
+        if not thread_id:
+            return []
+        async with semaphore:
+            try:
+                path_user_id = await aresolve_path_user_id(thread_id)
+                outputs_dir = paths.sandbox_outputs_dir(thread_id, user_id=path_user_id)
+                files = await asyncio.to_thread(collect_outputs_listing, outputs_dir)
+            except Exception:
+                logger.warning("Failed to scan artifacts for library thread %s", thread_id, exc_info=True)
+                return []
+
+        metadata = row.get("metadata") if isinstance(row.get("metadata"), dict) else {}
+        agent_id = metadata.get("agent_id")
+        agent_name = metadata.get("agent_name")
+        thread_type = metadata.get("thread_type")
+        normalized_agent_id = agent_id.strip() if isinstance(agent_id, str) else ""
+        normalized_agent_name = agent_name.strip() if isinstance(agent_name, str) else ""
+        title = str(row.get("display_name") or "新对话")
+        items: list[dict] = []
+        for file in files:
+            if not is_artifact_library_file(file):
+                continue
+            items.append(
+                {
+                    "thread_id": thread_id,
+                    "thread_title": title,
+                    "agent_id": normalized_agent_id or None,
+                    "agent_name": normalized_agent_name or None,
+                    "thread_type": thread_type if isinstance(thread_type, str) and thread_type else None,
+                    "source_type": "agent" if normalized_agent_id else "general_qa",
+                    **file,
+                    "file_kind": artifact_library_kind(str(file.get("name") or "")),
+                }
+            )
+        return items
+
+    nested = await asyncio.gather(*(scan_thread(row) for row in rows))
+    items = [item for group in nested for item in group]
+    items.sort(
+        key=lambda item: (
+            str(item.get("modified_at") or ""),
+            str(item.get("thread_id") or ""),
+            str(item.get("path") or ""),
+        ),
+        reverse=True,
+    )
+    return items
+
+
+async def _cached_artifact_library(thread_store, *, refresh: bool) -> list[dict]:
+    user_id = str(get_effective_user_id() or "default")
+    now = time.monotonic()
+    cached = _artifact_library_cache.get(user_id)
+    if not refresh and cached and cached[0] > now:
+        _artifact_library_cache.move_to_end(user_id)
+        return cached[1]
+
+    items = await _scan_artifact_library(thread_store)
+    _artifact_library_cache[user_id] = (now + _ARTIFACT_LIBRARY_CACHE_TTL_SECONDS, items)
+    _artifact_library_cache.move_to_end(user_id)
+    while len(_artifact_library_cache) > _ARTIFACT_LIBRARY_CACHE_MAX_USERS:
+        _artifact_library_cache.popitem(last=False)
+    return items
+
+
+@router.get(
+    "/artifact-library",
+    response_model=ArtifactLibraryResponse,
+    summary="List Generated Files Across Conversations",
+    description="Aggregate non-media files generated in the current user's normal conversations.",
+)
+@require_permission("threads", "read")
+async def list_artifact_library(
+    request: Request,
+    query: str | None = Query(default=None, max_length=200),
+    file_kind: ArtifactLibraryKind = Query(default="all"),
+    source_type: ArtifactLibrarySource = Query(default="all"),
+    offset: int = Query(default=0, ge=0),
+    limit: int = Query(default=50, ge=1, le=200),
+    refresh: bool = Query(default=False),
+) -> ArtifactLibraryResponse:
+    """Return a flat, newest-first library of generated conversation files.
+
+    The filesystem remains the source of truth: every owned, non-system thread's
+    ``outputs`` directory is scanned, including files written without an
+    explicit ``present_files`` call. Uploads and image/audio/video outputs are
+    intentionally outside this collection.
+    """
+    from app.gateway.deps import get_agent_store, get_thread_store
+
+    items = await _cached_artifact_library(get_thread_store(request), refresh=refresh)
+    agent_ids = list(dict.fromkeys(str(item.get("agent_id") or "") for item in items if item.get("agent_id")))
+    agent_names: dict[str, str] = {}
+    if agent_ids:
+        try:
+            agents = await get_agent_store(request).list_by_ids(
+                agent_ids,
+                str(get_effective_user_id() or "default"),
+            )
+            agent_names = {str(agent.get("id") or ""): str(agent.get("name") or "").strip() for agent in agents if agent.get("id") and agent.get("name")}
+        except Exception:
+            logger.warning("Failed to resolve artifact library agent names", exc_info=True)
+
+    items_with_sources = [
+        {
+            **item,
+            "agent_name": (agent_names.get(str(item.get("agent_id") or "")) or item.get("agent_name") or item.get("agent_id")),
+        }
+        for item in items
+    ]
+    normalized_query = (query or "").strip().casefold()
+    filtered = [
+        item
+        for item in items_with_sources
+        if (file_kind == "all" or item["file_kind"] == file_kind)
+        and (source_type == "all" or item["source_type"] == source_type)
+        and (not normalized_query or normalized_query in str(item.get("name") or "").casefold() or normalized_query in str(item.get("thread_title") or "").casefold() or normalized_query in str(item.get("agent_name") or "").casefold())
+    ]
+    page = filtered[offset : offset + limit]
+    return ArtifactLibraryResponse(
+        total=len(filtered),
+        offset=offset,
+        limit=limit,
+        items=[ArtifactLibraryItem.model_validate(item) for item in page],
+    )
+
+
 @router.get(
     "/threads/{thread_id}/artifacts",
     summary="List Artifact Files",

offline-backend-20260512/backend/app/gateway/routers/llmwiki.py

@@ -55,6 +55,7 @@ from deerflow.config.agents_config import load_agent_config
 from deerflow.config.llmwiki_config import LlmWikiRuntimeOverride, normalize_service_url
 from deerflow.config.system_settings import load_system_settings, save_system_settings
 from deerflow.integrations.weknora.client import WeKnoraClient, WeKnoraError
+from deerflow.integrations.weknora.local_index.retrieval import is_fully_vectorized
 from deerflow.integrations.weknora.local_index.search import WikiIndexError
 from deerflow.integrations.weknora.runtime import build_weknora_client, get_resolved_llmwiki_runtime
 from deerflow.persistence.llmwiki import LlmWikiStore
@@ -496,15 +497,7 @@ def _should_follow_weknora_binary_redirect(path: str) -> bool:
     """Keep protected preview/image bytes inside the DeerFlow proxy."""
 
     segments = _path_segments(path)
-    return (
-        len(segments) == 5
-        and segments[:3] == ["api", "v1", "knowledge"]
-        and segments[4] == "preview"
-    ) or (
-        len(segments) == 5
-        and segments[:3] == ["api", "v1", "knowledge-bases"]
-        and segments[4] == "files"
-    )
+    return (len(segments) == 5 and segments[:3] == ["api", "v1", "knowledge"] and segments[4] == "preview") or (len(segments) == 5 and segments[:3] == ["api", "v1", "knowledge-bases"] and segments[4] == "files")
 
 
 def _forward_request_headers(request: Request) -> dict[str, str]:
@@ -1833,6 +1826,9 @@ def _mapping_view(row: dict[str, Any], remote: dict[str, Any] | None, *, actor_u
         "knowledge_count": int(source.get("knowledge_count") or 0),
         "chunk_count": int(source.get("chunk_count") or 0),
         "processing_count": int(source.get("processing_count") or 0),
+        "share_count": int(source.get("share_count") or 0),
+        "is_processing": bool(source.get("is_processing") or int(source.get("processing_count") or 0) > 0),
+        "is_temporary": bool(source.get("is_temporary", False)),
         "embedding_model_id": source.get("embedding_model_id"),
         "vector_store_id": source.get("vector_store_id"),
         "vector_store_name": source.get("vector_store_name"),
@@ -1840,16 +1836,23 @@ def _mapping_view(row: dict[str, Any], remote: dict[str, Any] | None, *, actor_u
         "vector_store_engine_type": source.get("vector_store_engine_type"),
         "vector_store_status": source.get("vector_store_status"),
         "chunking_config": source.get("chunking_config") if isinstance(source.get("chunking_config"), dict) else {},
+        "image_processing_config": (source.get("image_processing_config") if isinstance(source.get("image_processing_config"), dict) else {}),
         "indexing_strategy": source.get("indexing_strategy") if isinstance(source.get("indexing_strategy"), dict) else {},
         "capabilities": source.get("capabilities") if isinstance(source.get("capabilities"), dict) else {},
         "extract_config": source.get("extract_config") if isinstance(source.get("extract_config"), dict) else {},
+        "faq_config": source.get("faq_config") if isinstance(source.get("faq_config"), dict) else {},
         "wiki_config": source.get("wiki_config") if isinstance(source.get("wiki_config"), dict) else {},
         "question_generation_config": (source.get("question_generation_config") if isinstance(source.get("question_generation_config"), dict) else {}),
+        "auto_tag_config": source.get("auto_tag_config") if isinstance(source.get("auto_tag_config"), dict) else {},
+        "vlm_config": source.get("vlm_config") if isinstance(source.get("vlm_config"), dict) else {},
+        "asr_config": source.get("asr_config") if isinstance(source.get("asr_config"), dict) else {},
         "summary_model_id": source.get("summary_model_id"),
         "storage_provider": (source.get("storage_provider_config", {}).get("provider") if isinstance(source.get("storage_provider_config"), dict) else None),
         "created_at": row.get("created_at"),
         "updated_at": row.get("updated_at"),
         "published_at": row.get("published_at"),
+        "remote_created_at": source.get("created_at"),
+        "remote_updated_at": source.get("updated_at"),
     }
 
 
@@ -1877,11 +1880,7 @@ async def _public_mapping(request: Request, mapping_id: str) -> dict[str, Any]:
         write=False,
         is_admin=False,
     )
-    if (
-        row is None
-        or row.get("publication_status") != "published"
-        or _is_conversation_deposit_mapping(row)
-    ):
+    if row is None or row.get("publication_status") != "published" or _is_conversation_deposit_mapping(row):
         raise HTTPException(status_code=404, detail="Published knowledge base not found")
     return row
 
@@ -2537,24 +2536,260 @@ async def test_llmwiki_connection(request: Request, body: LlmWikiConnectionTest)
     return {"success": True, "provider": "weknora", "health": health}
 
 
+_PUBLIC_WIKI_SUMMARY_CONCURRENCY = 8
+_PUBLIC_WIKI_PAGE_SIZE = 500
+_PUBLIC_WIKI_PAGE_LIMIT = 100
+_GENERATED_PUBLIC_WIKI_INDEX_SLUG = "__index__"
+
+
+def _is_public_wiki_index_page(page: dict[str, Any]) -> bool:
+    page_type = str(page.get("page_type") or "").strip().lower()
+    title = str(page.get("title") or "").strip().lower()
+    slug_name = str(page.get("slug") or "").strip("/").rsplit("/", 1)[-1].lower()
+    return page_type == "index" or title in {"索引", "index"} or slug_name in {"index", "home", "readme"}
+
+
+def _public_wiki_default_page(pages: list[dict[str, Any]]) -> dict[str, Any] | None:
+    return (
+        next((page for page in pages if _is_public_wiki_index_page(page)), None)
+        or next((page for page in pages if str(page.get("page_type") or "").lower() == "overview"), None)
+        or next(
+            (page for page in pages if not str(page.get("parent_slug") or "").strip() and int(page.get("depth") or 0) == 0),
+            None,
+        )
+        or (pages[0] if pages else None)
+    )
+
+
+def _count_public_wiki_values(pages: list[dict[str, Any]], key: str, fallback: str) -> dict[str, int]:
+    counts: dict[str, int] = {}
+    for page in pages:
+        value = str(page.get(key) or fallback).strip().lower() or fallback
+        counts[value] = counts.get(value, 0) + 1
+    return counts
+
+
+def _public_wiki_time_bound(pages: list[dict[str, Any]], key: str, *, latest: bool) -> str | None:
+    values = [str(page.get(key) or "").strip() for page in pages]
+    values = [value for value in values if value]
+    if not values:
+        return None
+    return max(values) if latest else min(values)
+
+
+async def _all_public_wiki_page_views(
+    client: WeKnoraClient,
+    remote_id: str,
+) -> tuple[list[dict[str, Any]], int, bool]:
+    pages_by_slug: dict[str, dict[str, Any]] = {}
+    remote_total = 0
+    complete = False
+    for page_number in range(1, _PUBLIC_WIKI_PAGE_LIMIT + 1):
+        result = await client.list_wiki_pages(
+            remote_id,
+            page=page_number,
+            page_size=_PUBLIC_WIKI_PAGE_SIZE,
+            query="",
+        )
+        raw_pages = result.get("pages") if isinstance(result, dict) else []
+        page_items = raw_pages if isinstance(raw_pages, list) else []
+        for page_item in page_items:
+            if not isinstance(page_item, dict):
+                continue
+            page = _wiki_page_view(page_item)
+            if page is None:
+                continue
+            slug = str(page.get("slug") or "").strip()
+            pages_by_slug[slug or f"__page_{len(pages_by_slug)}"] = page
+        reported_total = int(result.get("total") or 0)
+        remote_total = max(remote_total, reported_total, len(pages_by_slug))
+        total_pages = max(1, int(result.get("total_pages") or 1))
+        if not page_items or page_number >= total_pages or (reported_total > 0 and len(pages_by_slug) >= remote_total):
+            complete = True
+            break
+    return list(pages_by_slug.values()), max(remote_total, len(pages_by_slug)), complete
+
+
+def _public_wiki_summary(
+    mapping_id: str,
+    pages: list[dict[str, Any]],
+    *,
+    total: int,
+    complete: bool,
+) -> dict[str, Any]:
+    explicit_index = next((page for page in pages if _is_public_wiki_index_page(page)), None)
+    default_page = _public_wiki_default_page(pages)
+    if explicit_index is not None:
+        index_mode = "explicit"
+        index_slug: str | None = str(explicit_index.get("slug") or "") or None
+    elif pages:
+        index_mode = "generated"
+        index_slug = _GENERATED_PUBLIC_WIKI_INDEX_SLUG
+    else:
+        index_mode = "none"
+        index_slug = None
+    default_slug = index_slug or (str(default_page.get("slug") or "") if default_page else None)
+    route = f"/embed/knowledge/{quote(mapping_id, safe='')}"
+    if default_slug:
+        route = f"{route}?{urlencode({'wikiSlug': default_slug})}"
+    return {
+        "status": "ready" if pages else "empty",
+        "page_count": total,
+        "loaded_page_count": len(pages),
+        "stats_complete": complete,
+        "page_type_counts": _count_public_wiki_values(pages, "page_type", "page"),
+        "page_status_counts": _count_public_wiki_values(pages, "status", "unknown"),
+        "index_mode": index_mode,
+        "has_explicit_index": explicit_index is not None,
+        "index_slug": index_slug,
+        "default_slug": default_slug,
+        "created_at": _public_wiki_time_bound(pages, "created_at", latest=False),
+        "updated_at": _public_wiki_time_bound(pages, "updated_at", latest=True),
+        "embed_route": route,
+    }
+
+
+def _unavailable_public_wiki_summary(mapping_id: str) -> dict[str, Any]:
+    return {
+        "status": "unavailable",
+        "page_count": 0,
+        "loaded_page_count": 0,
+        "stats_complete": False,
+        "page_type_counts": {},
+        "page_status_counts": {},
+        "index_mode": "unknown",
+        "has_explicit_index": False,
+        "index_slug": None,
+        "default_slug": None,
+        "created_at": None,
+        "updated_at": None,
+        "embed_route": f"/embed/knowledge/{quote(mapping_id, safe='')}",
+    }
+
+
+def _public_local_index_summary(
+    request: Request,
+    mapping: dict[str, Any],
+    state: dict[str, Any] | None,
+) -> dict[str, Any]:
+    app_state = request.app.state
+    local_config = getattr(
+        getattr(getattr(app_state, "config", None), "llmwiki", None),
+        "local_wiki_index",
+        None,
+    )
+    enabled = bool(getattr(local_config, "enabled", False))
+    current = dict(state or {})
+    state_name = str(current.get("state") or "not_synced")
+    if state_name == "idle" and int(current.get("index_revision") or 0) == 0 and not current.get("last_completed_at") and not current.get("last_success_at"):
+        state_name = "not_synced"
+    embedding = getattr(app_state, "llmwiki_embedding", None)
+    fingerprint = embedding.fingerprint if embedding is not None else None
+    fully_vectorized = bool(mapping.get("wiki_index_enabled", True)) and is_fully_vectorized(
+        current,
+        fingerprint,
+    )
+    return {
+        "enabled": enabled,
+        "wiki_index_enabled": bool(mapping.get("wiki_index_enabled", True)),
+        "state": state_name,
+        "fully_vectorized": fully_vectorized,
+        "retrieval_mode": "vector" if fully_vectorized else "wiki_api",
+        "remote_page_count": int(current.get("remote_page_count") or 0),
+        "local_page_count": int(current.get("local_page_count") or 0),
+        "ready_page_count": int(current.get("ready_page_count") or 0),
+        "failed_page_count": int(current.get("failed_page_count") or 0),
+        "vector_count": int(current.get("vector_count") or 0),
+        "last_started_at": current.get("last_started_at"),
+        "last_completed_at": current.get("last_completed_at"),
+        "last_success_at": current.get("last_success_at"),
+        "updated_at": current.get("updated_at"),
+    }
+
+
 @router.get("/api/public/llmwiki/knowledge-bases")
 async def list_public_knowledge_bases(request: Request) -> dict[str, Any]:
-    """List the minimal identity of every knowledge base published as a public Wiki."""
+    """List live, published Wiki libraries with safe metadata and index health."""
 
-    rows = await _store(request).list_visible(
-        "__public_wiki__",
-        scope="public",
-        is_admin=False,
-    )
-    knowledge_bases = [
-        {
-            "id": str(row["id"]),
-            "name": str(row.get("name") or ""),
-        }
-        for row in rows
-        if row.get("publication_status") == "published"
-        and not _is_conversation_deposit_mapping(row)
+    rows = [
+        row
+        for row in await _store(request).list_visible(
+            "__public_wiki__",
+            scope="public",
+            is_admin=False,
+        )
+        if row.get("publication_status") == "published" and not _is_conversation_deposit_mapping(row)
     ]
+    client = _client_or_503()
+    try:
+        remote_items = await client.list_knowledge_bases()
+    except WeKnoraError as exc:
+        _raise_upstream(exc)
+    remote_by_id = {str(item.get("id")): item for item in remote_items if isinstance(item, dict) and item.get("id")}
+    # A successful upstream list is authoritative: stale DeerFlow mappings are
+    # omitted so discovery never advertises a Wiki that its detail route cannot open.
+    rows = [row for row in rows if str(row.get("weknora_id") or "") in remote_by_id]
+    mapping_ids = [str(row["id"]) for row in rows]
+    index_states: dict[str, dict[str, Any]] = {}
+    index_store = getattr(request.app.state, "llmwiki_index_store", None)
+    if index_store is not None and mapping_ids:
+        try:
+            index_states = {str(state.get("knowledge_base_mapping_id") or ""): state for state in await index_store.list_index_status(mapping_ids) if isinstance(state, dict)}
+        except Exception:
+            logger.warning("Failed to read public Wiki local-index summaries", exc_info=True)
+
+    semaphore = asyncio.Semaphore(_PUBLIC_WIKI_SUMMARY_CONCURRENCY)
+
+    async def build_summary(row: dict[str, Any]) -> dict[str, Any]:
+        mapping_id = str(row["id"])
+        remote_id = str(row["weknora_id"])
+        remote = remote_by_id[remote_id]
+        async with semaphore:
+            try:
+                remote = {**remote, **await client.get_knowledge_base(remote_id)}
+            except WeKnoraError as exc:
+                if exc.status_code == 404:
+                    return {}
+                logger.warning(
+                    "Failed to load public Wiki detail metadata: mapping_id=%s",
+                    mapping_id,
+                    exc_info=True,
+                )
+            try:
+                pages, page_total, complete = await _all_public_wiki_page_views(client, remote_id)
+                wiki = _public_wiki_summary(
+                    mapping_id,
+                    pages,
+                    total=page_total,
+                    complete=complete,
+                )
+            except WeKnoraError as exc:
+                logger.warning(
+                    "Failed to load public Wiki page summary: mapping_id=%s",
+                    mapping_id,
+                    exc_info=True,
+                )
+                wiki = (
+                    _public_wiki_summary(mapping_id, [], total=0, complete=True)
+                    if exc.status_code == 404
+                    else _unavailable_public_wiki_summary(mapping_id)
+                )
+        return {
+            **_mapping_view(
+                row,
+                remote,
+                actor_user_id="__public_wiki__",
+                is_admin=False,
+            ),
+            "wiki": wiki,
+            "local_index": _public_local_index_summary(
+                request,
+                row,
+                index_states.get(mapping_id),
+            ),
+        }
+
+    knowledge_bases = [summary for summary in await asyncio.gather(*(build_summary(row) for row in rows)) if summary]
     return {"knowledge_bases": knowledge_bases, "total": len(knowledge_bases)}
 
 
@@ -2596,13 +2831,7 @@ async def list_public_wiki_pages(
     except WeKnoraError as exc:
         _raise_upstream(exc)
     raw_pages = result.get("pages") if isinstance(result, dict) else []
-    pages = [
-        view
-        for page_item in (raw_pages if isinstance(raw_pages, list) else [])
-        if isinstance(page_item, dict)
-        for view in [_wiki_page_view(page_item)]
-        if view is not None
-    ]
+    pages = [view for page_item in (raw_pages if isinstance(raw_pages, list) else []) if isinstance(page_item, dict) for view in [_wiki_page_view(page_item)] if view is not None]
     return {**result, "pages": pages}
 
 
@@ -2650,11 +2879,7 @@ def _public_wiki_index_item(value: Any) -> dict[str, Any] | None:
         "slug": slug,
         "title": str(value.get("title") or slug),
         "summary": str(value.get("summary") or ""),
-        "category_path": (
-            [str(item) for item in value.get("category_path", []) if str(item).strip()]
-            if isinstance(value.get("category_path"), list)
-            else []
-        ),
+        "category_path": ([str(item) for item in value.get("category_path", []) if str(item).strip()] if isinstance(value.get("category_path"), list) else []),
         "wiki_path": str(value.get("wiki_path") or ""),
         "depth": int(value.get("depth") or 0),
     }
@@ -2680,20 +2905,12 @@ async def _all_public_wiki_index(client: WeKnoraClient, remote_id: str) -> dict[
             version = max(version, int(payload.get("version") or 0))
             raw_groups = payload.get("groups") if isinstance(payload.get("groups"), list) else []
             group = next(
-                (
-                    value
-                    for value in raw_groups
-                    if isinstance(value, dict) and value.get("type") == page_type
-                ),
+                (value for value in raw_groups if isinstance(value, dict) and value.get("type") == page_type),
                 {},
             )
             total = max(total, int(group.get("total") or 0))
             raw_items = group.get("items") if isinstance(group.get("items"), list) else []
-            items.extend(
-                item
-                for value in raw_items
-                if (item := _public_wiki_index_item(value)) is not None
-            )
+            items.extend(item for value in raw_items if (item := _public_wiki_index_item(value)) is not None)
             next_cursor = str(group.get("next_cursor") or "")
             if not next_cursor or next_cursor in seen_cursors:
                 break
@@ -3201,12 +3418,7 @@ async def search_llmwiki(request: Request, body: SearchRequest) -> dict[str, Any
     except WikiIndexError as exc:
         raise HTTPException(status_code=exc.status_code, detail={"code": exc.code, "message": str(exc)}) from None
     mapping_by_id = {str(row["id"]): row for row in rows}
-    result["results"] = [
-        _sanitize_conversation_deposit_value(item)
-        if _is_conversation_deposit_mapping(mapping_by_id.get(str(item.get("knowledge_base_id") or ""), {}))
-        else item
-        for item in result.get("results") or []
-    ]
+    result["results"] = [_sanitize_conversation_deposit_value(item) if _is_conversation_deposit_mapping(mapping_by_id.get(str(item.get("knowledge_base_id") or ""), {})) else item for item in result.get("results") or []]
     return result
 
 

offline-backend-20260512/backend/app/gateway/routers/skill_knowledge.py

@@ -0,0 +1,328 @@
+"""Administrator APIs for skill knowledge bindings and matrix jobs."""
+
+from __future__ import annotations
+
+import asyncio
+from typing import Any, Literal
+
+from croniter import croniter
+from fastapi import APIRouter, HTTPException, Query, Request
+from pydantic import BaseModel, Field, field_validator
+
+from app.gateway.deps import get_optional_user_from_request
+from app.gateway.skill_knowledge_job_executor import _skill_directory
+from deerflow.config.system_settings import (
+    SkillKnowledgeAutoSyncSettings,
+    load_system_settings,
+    save_system_settings,
+)
+from deerflow.integrations.weknora.runtime import (
+    build_weknora_client,
+    get_resolved_llmwiki_runtime,
+)
+from deerflow.skill_knowledge import scan_skill
+
+router = APIRouter(prefix="/api/skill-knowledge", tags=["skill-knowledge"])
+
+
+async def _require_admin(request: Request) -> str | None:
+    user = await get_optional_user_from_request(request)
+    if user is None:
+        return None
+    if getattr(user, "system_role", None) != "admin":
+        raise HTTPException(status_code=403, detail="技能知识归纳仅管理员可操作")
+    return str(user.id)
+
+
+def _store(request: Request):
+    value = getattr(request.app.state, "skill_knowledge_store", None)
+    if value is None:
+        raise HTTPException(status_code=503, detail="技能知识归纳存储不可用")
+    return value
+
+
+class SyncTarget(BaseModel):
+    target_type: Literal["weknora", "assistant"]
+    target_mode: Literal["wiki", "document", "faq", "conversation_archive", "readonly_sidecar", "assistant"]
+    target_id: str = Field(min_length=1, max_length=128)
+    target_name: str = Field(default="", max_length=255)
+    remote_business_key: str | None = Field(default=None, max_length=512)
+    readonly: bool = False
+
+
+class CreateSyncJobRequest(BaseModel):
+    skill_names: list[str] = Field(min_length=1, max_length=200)
+    targets: list[SyncTarget] = Field(min_length=1, max_length=50)
+    force: bool = False
+    review_mode: Literal["required", "auto_high_confidence", "off"] = "auto_high_confidence"
+
+    @field_validator("skill_names")
+    @classmethod
+    def validate_names(cls, values: list[str]) -> list[str]:
+        result = list(dict.fromkeys(value.strip() for value in values if value.strip()))
+        if not result:
+            raise ValueError("At least one skill is required")
+        return result
+
+
+class ReviewRequest(BaseModel):
+    review_ids: list[str] = Field(min_length=1, max_length=500)
+    action: Literal["approve", "reject"]
+    edits: dict[str, dict[str, Any]] = Field(default_factory=dict)
+    reason: str | None = Field(default=None, max_length=1024)
+
+
+class DetachRequest(BaseModel):
+    delete_contributions: bool
+
+
+class AutoSyncSettingsUpdate(BaseModel):
+    watch_enabled: bool | None = None
+    schedule_enabled: bool | None = None
+    schedule_cron: str | None = Field(default=None, min_length=5, max_length=128)
+    debounce_seconds: int | None = Field(default=None, ge=10, le=86_400)
+    write_remote_enabled: bool | None = None
+    review_mode: Literal["required", "auto_high_confidence", "off"] | None = None
+
+    @field_validator("schedule_cron")
+    @classmethod
+    def validate_cron(cls, value: str | None) -> str | None:
+        if value is not None and not croniter.is_valid(value):
+            raise ValueError("无效的 cron 表达式")
+        return value
+
+
+@router.get("/bindings")
+async def list_bindings(
+    request: Request,
+    skill_names: list[str] | None = Query(default=None),
+    target_id: str | None = Query(default=None),
+    include_detached: bool = Query(default=False),
+) -> dict[str, Any]:
+    await _require_admin(request)
+    rows = await _store(request).list_bindings(skill_names=skill_names, target_id=target_id, include_detached=include_detached)
+    return {"bindings": rows}
+
+
+@router.get("/source-status")
+async def source_status(request: Request, skill_names: list[str] = Query(...)) -> dict[str, Any]:
+    await _require_admin(request)
+    bindings = await _store(request).list_bindings(skill_names=skill_names)
+    by_skill: dict[str, dict[str, Any]] = {}
+    for name in list(dict.fromkeys(skill_names)):
+        try:
+            root = _skill_directory(request.app.state.config, name)
+            result = await asyncio.to_thread(scan_skill, name, root)
+            by_skill[name] = {"source_digest": result.source_digest, "counts": result.counts, "blocked_files": result.blocked_files}
+        except Exception as exc:  # noqa: BLE001
+            by_skill[name] = {"error": str(exc)}
+    for binding in bindings:
+        status = by_skill.get(binding["skill_name"])
+        if status and status.get("source_digest") and status["source_digest"] != binding.get("synced_digest"):
+            binding["computed_status"] = "stale"
+    return {"skills": by_skill, "bindings": bindings}
+
+
+@router.get("/auto-sync-settings", response_model=SkillKnowledgeAutoSyncSettings)
+async def get_auto_sync_settings(request: Request) -> SkillKnowledgeAutoSyncSettings:
+    await _require_admin(request)
+    return load_system_settings().skill_knowledge_auto_sync
+
+
+@router.put("/auto-sync-settings", response_model=SkillKnowledgeAutoSyncSettings)
+async def update_auto_sync_settings(request: Request, body: AutoSyncSettingsUpdate) -> SkillKnowledgeAutoSyncSettings:
+    await _require_admin(request)
+    current = load_system_settings()
+    updated = current.skill_knowledge_auto_sync.model_copy(update=body.model_dump(exclude_unset=True))
+    saved = save_system_settings(current.model_copy(update={"skill_knowledge_auto_sync": updated}))
+    service = getattr(request.app.state, "skill_knowledge_auto_sync", None)
+    if service is not None:
+        service.nudge()
+    return saved.skill_knowledge_auto_sync
+
+
+async def _set_binding_auto_sync(request: Request, binding_id: str, *, enabled: bool) -> dict[str, Any]:
+    await _require_admin(request)
+    row = await _store(request).set_binding_auto_sync(binding_id, enabled=enabled)
+    if row is None:
+        raise HTTPException(status_code=404, detail="绑定不存在")
+    service = getattr(request.app.state, "skill_knowledge_auto_sync", None)
+    if service is not None:
+        service.nudge()
+    return row
+
+
+@router.post("/bindings/{binding_id}/enable-auto-sync")
+async def enable_binding_auto_sync(request: Request, binding_id: str) -> dict[str, Any]:
+    return await _set_binding_auto_sync(request, binding_id, enabled=True)
+
+
+@router.post("/bindings/{binding_id}/disable-auto-sync")
+async def disable_binding_auto_sync(request: Request, binding_id: str) -> dict[str, Any]:
+    return await _set_binding_auto_sync(request, binding_id, enabled=False)
+
+
+@router.post("/sync-jobs", status_code=202)
+async def create_sync_job(request: Request, body: CreateSyncJobRequest) -> dict[str, Any]:
+    actor = await _require_admin(request)
+    skill_store = request.app.state.skill_store
+    missing = [name for name in body.skill_names if await skill_store.get_any(name) is None]
+    if missing:
+        raise HTTPException(status_code=404, detail=f"技能不存在：{', '.join(missing[:10])}")
+    targets: list[dict[str, Any]] = []
+    for target in body.targets:
+        data = target.model_dump()
+        if target.target_type != "weknora":
+            raise HTTPException(status_code=422, detail="不支持技能直接归纳到助手知识库，请先归纳到 WeKnora 普通知识库")
+        if target.target_mode != "wiki":
+            raise HTTPException(status_code=422, detail="技能归纳目标仅支持 WeKnora Wiki 知识库")
+        mapping = await request.app.state.llmwiki_store.get_authorized(target.target_id, actor or "system", write=True, is_admin=True)
+        if mapping is None:
+            raise HTTPException(status_code=404, detail=f"普通知识库不存在：{target.target_id}")
+        data["target_name"] = target.target_name or str(mapping.get("name") or target.target_id)
+        data["remote_business_key"] = target.remote_business_key or f"weknora:kb:{mapping['weknora_id']}"
+        targets.append(data)
+    job = await _store(request).create_job(
+        skill_names=body.skill_names,
+        targets=targets,
+        created_by=actor,
+        force=body.force,
+        review_mode=body.review_mode,
+    )
+    dispatcher = getattr(request.app.state, "skill_knowledge_dispatcher", None)
+    if dispatcher is not None:
+        dispatcher.nudge()
+    return job
+
+
+@router.get("/sync-jobs")
+async def list_sync_jobs(request: Request, limit: int = Query(default=50, ge=1, le=200)) -> dict[str, Any]:
+    await _require_admin(request)
+    return {"jobs": await _store(request).list_jobs(limit=limit)}
+
+
+@router.get("/sync-jobs/{job_id}")
+async def get_sync_job(request: Request, job_id: str) -> dict[str, Any]:
+    await _require_admin(request)
+    row = await _store(request).get_job(job_id)
+    if row is None:
+        raise HTTPException(status_code=404, detail="归纳任务不存在")
+    return row
+
+
+@router.post("/sync-jobs/{job_id}/cancel")
+async def cancel_sync_job(request: Request, job_id: str) -> dict[str, bool]:
+    await _require_admin(request)
+    if not await _store(request).cancel_job(job_id):
+        raise HTTPException(status_code=404, detail="归纳任务不存在")
+    return {"canceled": True}
+
+
+@router.post("/sync-items/{item_id}/retry", status_code=202)
+async def retry_sync_item(request: Request, item_id: str) -> dict[str, Any]:
+    await _require_admin(request)
+    row = await _store(request).requeue_item(item_id)
+    if row is None:
+        raise HTTPException(status_code=404, detail="归纳任务项不存在")
+    request.app.state.skill_knowledge_dispatcher.nudge()
+    return row
+
+
+@router.post("/bindings/{binding_id}/resync", status_code=202)
+async def resync_binding(request: Request, binding_id: str) -> dict[str, Any]:
+    actor = await _require_admin(request)
+    binding = await _store(request).get_binding(binding_id)
+    if binding is None or not binding.get("active"):
+        raise HTTPException(status_code=404, detail="绑定不存在")
+    target = {
+        "target_type": binding["target_type"],
+        "target_mode": binding["target_mode"],
+        "target_id": binding["target_id"],
+        "target_name": binding["target_name"],
+        "remote_business_key": binding.get("remote_business_key"),
+    }
+    job = await _store(request).create_job(
+        skill_names=[binding["skill_name"]],
+        targets=[target],
+        created_by=actor,
+        force=True,
+        review_mode="auto_high_confidence",
+    )
+    request.app.state.skill_knowledge_dispatcher.nudge()
+    return job
+
+
+@router.post("/bindings/{binding_id}/detach")
+async def detach_binding(request: Request, binding_id: str, body: DetachRequest) -> dict[str, Any]:
+    await _require_admin(request)
+    store = _store(request)
+    binding = await store.get_binding(binding_id)
+    if binding is None:
+        raise HTTPException(status_code=404, detail="绑定不存在")
+    if await store.has_active_item(binding_id):
+        raise HTTPException(status_code=409, detail="该绑定仍有运行中的归纳项，请先取消任务")
+    cleanup_failed = False
+    if body.delete_contributions:
+        try:
+            if binding["target_type"] == "assistant":
+                sources = await request.app.state.assistant_knowledge_store.list_sources()
+                for source in sources:
+                    if source.get("source_type") == "skill" and source.get("source_key") == binding["skill_name"]:
+                        await request.app.state.assistant_knowledge_store.delete_source(source["id"])
+            elif binding["target_type"] == "weknora":
+                mapping = await request.app.state.llmwiki_store.get_authorized(
+                    str(binding["target_id"]),
+                    "system",
+                    write=True,
+                    is_admin=True,
+                )
+                runtime = get_resolved_llmwiki_runtime(request.app.state.config)
+                if mapping is None or not runtime.weknora_enabled:
+                    raise RuntimeError("远端普通知识库当前不可写")
+                client = build_weknora_client(runtime)
+                remote_kb_id = str(mapping["weknora_id"])
+                for artifact in await store.list_artifacts(binding_id):
+                    if artifact["kind"] == "wiki_page" and artifact.get("slug"):
+                        await client.delete_wiki_page(remote_kb_id, artifact["slug"])
+                    elif artifact.get("remote_id") and artifact["kind"] in {
+                        "raw_source",
+                        "summary_document",
+                        "faq_projection",
+                        "conversation_archive",
+                        "graph_projection",
+                    }:
+                        await client.delete_document(str(artifact["remote_id"]))
+        except Exception:  # noqa: BLE001
+            cleanup_failed = True
+    row = await store.detach_binding(
+        binding_id,
+        delete_contributions=body.delete_contributions,
+        cleanup_failed=cleanup_failed,
+    )
+    if row is None:
+        raise HTTPException(status_code=404, detail="绑定不存在")
+    return row
+
+
+@router.get("/reviews")
+async def list_reviews(
+    request: Request,
+    job_id: str | None = Query(default=None),
+    item_id: str | None = Query(default=None),
+    status: str | None = Query(default="pending"),
+) -> dict[str, Any]:
+    await _require_admin(request)
+    return {"reviews": await _store(request).list_reviews(job_id=job_id, item_id=item_id, status=status)}
+
+
+@router.post("/reviews/actions")
+async def review_actions(request: Request, body: ReviewRequest) -> dict[str, Any]:
+    actor = await _require_admin(request)
+    item_ids = await _store(request).review(body.review_ids, action=body.action, actor=actor, edits=body.edits, reason=body.reason)
+    resumed: list[str] = []
+    executor = request.app.state.skill_knowledge_executor
+    for item_id in item_ids:
+        if await _store(request).pending_review_count(item_id) == 0:
+            asyncio.create_task(executor.resume_reviewed_item(item_id))
+            resumed.append(item_id)
+    return {"updated": len(body.review_ids), "resumed_item_ids": resumed}

offline-backend-20260512/backend/app/gateway/routers/system_settings.py

@@ -48,6 +48,14 @@ class PromptPrefixResponse(BaseModel):
     )
 
 
+class KnowledgeBaseProductVersionResponse(BaseModel):
+    version: Literal["v1", "v2"] = "v1"
+
+
+class KnowledgeBaseProductVersionUpdateRequest(BaseModel):
+    version: Literal["v1", "v2"]
+
+
 class PromptPrefixUpdateRequest(BaseModel):
     enabled: bool | None = None
     switch_label: str | None = None
@@ -175,6 +183,21 @@ async def update_prompt_prefix(body: PromptPrefixUpdateRequest, request: Request
     return _to_response(saved.prompt_prefix)
 
 
+@router.get("/knowledge-base-product-version", response_model=KnowledgeBaseProductVersionResponse)
+async def get_knowledge_base_product_version() -> KnowledgeBaseProductVersionResponse:
+    return KnowledgeBaseProductVersionResponse(version=load_system_settings().knowledge_base_product_version)
+
+
+@router.put("/knowledge-base-product-version", response_model=KnowledgeBaseProductVersionResponse)
+async def update_knowledge_base_product_version(
+    body: KnowledgeBaseProductVersionUpdateRequest,
+    request: Request,
+) -> KnowledgeBaseProductVersionResponse:
+    await _require_admin(request)
+    saved = save_system_settings(load_system_settings().model_copy(update={"knowledge_base_product_version": body.version}))
+    return KnowledgeBaseProductVersionResponse(version=saved.knowledge_base_product_version)
+
+
 @router.get("/plugin-management", response_model=PluginManagementResponse)
 async def get_plugin_management() -> PluginManagementResponse:
     """Return the iframe plugin domain -> agent mappings."""

offline-backend-20260512/backend/app/gateway/services.py

@@ -405,6 +405,7 @@ _CONTEXT_CONFIGURABLE_KEYS: frozenset[str] = frozenset(
         "reasoning_effort",
         "rag_mode_enabled",
         "llmwiki_knowledge_base_ids",
+        "assistant_knowledge_base_ids",
         "writing_mode",
         "writing_artifact_path",
         "canvas_artifact_title",
@@ -1116,7 +1117,7 @@ async def start_run(
         if prefix_settings.enabled and prefix_settings.prompt_prefix.strip():
             graph_input = _prepend_prompt_prefix(graph_input, prefix_settings.prompt_prefix.strip())
     llmwiki_rag = (
-        LlmWikiRagContext([], 0, "", [], "")
+        LlmWikiRagContext([], [], 0, "", [], "")
         if fixed_answer is not None
         else await build_llmwiki_rag_context(
             request=request,
@@ -1128,12 +1129,14 @@ async def start_run(
         graph_input = attach_llmwiki_rag_to_graph_input(graph_input, llmwiki_rag)
         effective_rag_mode = True
         run_metadata["llmwiki_knowledge_base_ids"] = llmwiki_rag.knowledge_base_ids
+        run_metadata["assistant_knowledge_base_ids"] = llmwiki_rag.assistant_knowledge_base_ids
         run_metadata["llmwiki_rag_source_count"] = llmwiki_rag.source_count
         try:
             await run_ctx.thread_store.update_metadata(
                 thread_id,
                 {
                     "llmwiki_knowledge_base_ids": llmwiki_rag.knowledge_base_ids,
+                    "assistant_knowledge_base_ids": llmwiki_rag.assistant_knowledge_base_ids,
                     "rag_mode_enabled": True,
                     "rag_mode": True,
                 },
@@ -1153,6 +1156,8 @@ async def start_run(
         body_context["rag_mode_enabled"] = True
     if "llmwiki_knowledge_base_ids" not in body_context and llmwiki_rag.knowledge_base_ids:
         body_context["llmwiki_knowledge_base_ids"] = llmwiki_rag.knowledge_base_ids
+    if "assistant_knowledge_base_ids" not in body_context and llmwiki_rag.assistant_knowledge_base_ids:
+        body_context["assistant_knowledge_base_ids"] = llmwiki_rag.assistant_knowledge_base_ids
     if not llmwiki_rag.knowledge_base_ids:
         existing_excluded = list(body_context.get("excluded_tools") or getattr(body, "excluded_tools", None) or [])
         if "llmwiki_search" not in existing_excluded:

offline-backend-20260512/backend/app/gateway/skill_knowledge_auto_sync.py

@@ -0,0 +1,155 @@
+"""Debounced file watcher and cron scheduler for opted-in knowledge bindings."""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from datetime import UTC, datetime
+from typing import Any
+
+from croniter import croniter
+
+from app.gateway.skill_knowledge_job_executor import _skill_directory
+from deerflow.config.system_settings import load_system_settings
+from deerflow.skill_knowledge import scan_skill
+
+logger = logging.getLogger(__name__)
+
+
+class SkillKnowledgeAutoSyncService:
+    def __init__(self, app: Any, *, poll_seconds: float = 10.0) -> None:
+        self.app = app
+        self.store = app.state.skill_knowledge_store
+        self.poll_seconds = poll_seconds
+        self._task: asyncio.Task | None = None
+        self._wake = asyncio.Event()
+        self._observed: dict[str, tuple[str, datetime]] = {}
+        self._schedule_expression = ""
+        self._next_schedule: datetime | None = None
+
+    def start(self) -> None:
+        if self._task is None or self._task.done():
+            self._task = asyncio.create_task(self._loop())
+
+    def nudge(self) -> None:
+        self._wake.set()
+
+    async def close(self) -> None:
+        if self._task is None:
+            return
+        self._task.cancel()
+        try:
+            await self._task
+        except asyncio.CancelledError:
+            pass
+
+    async def _queue_skill(
+        self,
+        skill_name: str,
+        bindings: list[dict[str, Any]],
+        *,
+        trigger: str,
+        review_mode: str,
+    ) -> None:
+        targets = [
+            {
+                "target_type": binding["target_type"],
+                "target_mode": binding["target_mode"],
+                "target_id": binding["target_id"],
+                "target_name": binding["target_name"],
+                "remote_business_key": binding.get("remote_business_key"),
+            }
+            for binding in bindings
+        ]
+        job = await self.store.create_job(
+            skill_names=[skill_name],
+            targets=targets,
+            created_by=None,
+            trigger=trigger,
+            review_mode=review_mode,
+        )
+        if job["total_count"]:
+            self.app.state.skill_knowledge_dispatcher.nudge()
+
+    async def _watch(
+        self,
+        bindings_by_skill: dict[str, list[dict[str, Any]]],
+        *,
+        debounce_seconds: int,
+        write_remote_enabled: bool,
+        review_mode: str,
+    ) -> None:
+        now = datetime.now(UTC)
+        for skill_name, bindings in bindings_by_skill.items():
+            try:
+                root = _skill_directory(self.app.state.config, skill_name)
+                scan = await asyncio.to_thread(scan_skill, skill_name, root)
+            except Exception:
+                logger.exception("Failed to fingerprint skill %s", skill_name)
+                continue
+            synced = {str(binding.get("synced_digest") or "") for binding in bindings}
+            if synced == {scan.source_digest}:
+                self._observed.pop(skill_name, None)
+                continue
+            await self.store.mark_skill_stale(skill_name)
+            previous = self._observed.get(skill_name)
+            if previous is None or previous[0] != scan.source_digest:
+                self._observed[skill_name] = (scan.source_digest, now)
+                continue
+            if (now - previous[1]).total_seconds() < debounce_seconds:
+                continue
+            if write_remote_enabled:
+                current = await self.store.list_bindings(skill_names=[skill_name])
+                stale = [binding for binding in current if binding.get("auto_sync_enabled") and binding.get("status") == "stale"]
+                if stale:
+                    await self._queue_skill(
+                        skill_name,
+                        stale,
+                        trigger="watcher",
+                        review_mode=review_mode,
+                    )
+            self._observed.pop(skill_name, None)
+
+    def _schedule_due(self, expression: str, now: datetime) -> bool:
+        if expression != self._schedule_expression or self._next_schedule is None:
+            self._schedule_expression = expression
+            self._next_schedule = croniter(expression, now).get_next(datetime)
+            return False
+        if now < self._next_schedule:
+            return False
+        self._next_schedule = croniter(expression, now).get_next(datetime)
+        return True
+
+    async def _tick(self) -> None:
+        settings = (await asyncio.to_thread(load_system_settings)).skill_knowledge_auto_sync
+        bindings = [row for row in await self.store.list_bindings() if row.get("auto_sync_enabled")]
+        by_skill: dict[str, list[dict[str, Any]]] = {}
+        for binding in bindings:
+            by_skill.setdefault(str(binding["skill_name"]), []).append(binding)
+        if settings.watch_enabled:
+            await self._watch(
+                by_skill,
+                debounce_seconds=settings.debounce_seconds,
+                write_remote_enabled=settings.write_remote_enabled,
+                review_mode=settings.review_mode,
+            )
+        if settings.schedule_enabled and settings.write_remote_enabled and by_skill and self._schedule_due(settings.schedule_cron, datetime.now(UTC)):
+            for skill_name, skill_bindings in by_skill.items():
+                await self._queue_skill(
+                    skill_name,
+                    skill_bindings,
+                    trigger="scheduled",
+                    review_mode=settings.review_mode,
+                )
+
+    async def _loop(self) -> None:
+        while True:
+            try:
+                await self._tick()
+            except Exception:
+                logger.exception("Skill knowledge automatic sync tick failed")
+            try:
+                await asyncio.wait_for(self._wake.wait(), timeout=self.poll_seconds)
+            except TimeoutError:
+                pass
+            self._wake.clear()

offline-backend-20260512/backend/app/gateway/skill_knowledge_job_dispatcher.py

@@ -0,0 +1,96 @@
+"""Small persistent-queue dispatcher for skill knowledge items."""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import socket
+from typing import Any
+from uuid import uuid4
+
+logger = logging.getLogger(__name__)
+
+
+class SkillKnowledgeJobDispatcher:
+    def __init__(
+        self,
+        store: Any,
+        executor: Any,
+        *,
+        interval_seconds: float = 2.0,
+        concurrency: int = 3,
+        lease_seconds: int = 900,
+    ) -> None:
+        self.store = store
+        self.executor = executor
+        self.interval_seconds = interval_seconds
+        self.lease_seconds = lease_seconds
+        self.owner = f"{socket.gethostname()}:{uuid4()}"
+        self.semaphore = asyncio.Semaphore(concurrency)
+        self._wake = asyncio.Event()
+        self._task: asyncio.Task | None = None
+        self._running: set[str] = set()
+
+    def start(self) -> None:
+        if self._task is None or self._task.done():
+            self._task = asyncio.create_task(self._loop())
+
+    def nudge(self) -> None:
+        self._wake.set()
+
+    async def close(self) -> None:
+        if self._task is not None:
+            self._task.cancel()
+            try:
+                await self._task
+            except asyncio.CancelledError:
+                pass
+
+    async def _run(self, item: dict) -> None:
+        item_id = str(item["id"])
+        async with self.semaphore:
+            heartbeat = asyncio.create_task(self._heartbeat(item_id))
+            try:
+                await self.executor.run_item(item)
+            finally:
+                heartbeat.cancel()
+                try:
+                    await heartbeat
+                except asyncio.CancelledError:
+                    pass
+                self._running.discard(item_id)
+                self._wake.set()
+
+    async def _heartbeat(self, item_id: str) -> None:
+        while True:
+            await asyncio.sleep(min(30, max(5, self.lease_seconds // 3)))
+            if not await self.store.renew_lease(
+                item_id,
+                owner=self.owner,
+                lease_seconds=self.lease_seconds,
+            ):
+                return
+
+    async def _loop(self) -> None:
+        while True:
+            try:
+                for item in await self.store.list_queued_items(limit=20):
+                    item_id = str(item["id"])
+                    if item_id in self._running:
+                        continue
+                    claimed = await self.store.claim_item(
+                        item_id,
+                        owner=self.owner,
+                        lease_seconds=self.lease_seconds,
+                    )
+                    if claimed is None:
+                        continue
+                    self._running.add(item_id)
+                    asyncio.create_task(self._run(claimed))
+            except Exception:
+                logger.exception("Skill knowledge dispatcher tick failed")
+            try:
+                await asyncio.wait_for(self._wake.wait(), timeout=self.interval_seconds)
+            except TimeoutError:
+                pass
+            self._wake.clear()

offline-backend-20260512/backend/app/gateway/skill_knowledge_job_executor.py

@@ -0,0 +1,403 @@
+"""Background executor for one skill x target matrix item."""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from pathlib import Path
+from typing import Any
+
+import httpx
+
+from deerflow.assistant_knowledge import export_package_from_skill_ir
+from deerflow.integrations.weknora.client import WeKnoraError
+from deerflow.integrations.weknora.runtime import build_weknora_client, get_resolved_llmwiki_runtime
+from deerflow.skill_knowledge import build_skill_ir, enrich_converted_documents, scan_skill
+from deerflow.skill_knowledge.graph_writer import parameterized_statements, plan_from_ir
+from deerflow.skills.parser import parse_skill_file
+from deerflow.skills.storage import get_or_new_skill_storage
+from deerflow.skills.types import SkillCategory
+
+logger = logging.getLogger(__name__)
+
+
+def _skill_directory(app_config: Any, skill_name: str) -> Path:
+    storage = get_or_new_skill_storage(app_config=app_config)
+    root = storage.get_skills_root_path().resolve()
+    for category in SkillCategory:
+        direct = root / category.value / skill_name
+        if (direct / "SKILL.md").is_file():
+            return direct
+    for category in SkillCategory:
+        category_root = root / category.value
+        if not category_root.is_dir():
+            continue
+        for skill_md in category_root.rglob("SKILL.md"):
+            if skill_md.is_symlink():
+                continue
+            parsed = parse_skill_file(skill_md, category=category, relative_path=skill_md.parent.relative_to(category_root))
+            if parsed and parsed.name == skill_name:
+                return skill_md.parent
+    raise FileNotFoundError(f"Skill '{skill_name}' not found under running skill roots")
+
+
+def _graph_projection(ir: dict[str, Any]) -> str:
+    rows = []
+    entities = {str(item.get("local_id")): item for item in ir.get("entities", [])}
+    for relation in ir.get("relations", []):
+        if float(relation.get("confidence") or 0) < 0.75 or not relation.get("evidence"):
+            continue
+        source = entities.get(str(relation.get("source_local_id")), {})
+        target = entities.get(str(relation.get("target_local_id")), {})
+        rows.append(
+            json.dumps(
+                {
+                    "source": source.get("name"),
+                    "source_type": source.get("type"),
+                    "predicate": relation.get("predicate"),
+                    "target": target.get("name"),
+                    "target_type": target.get("type"),
+                    "description": relation.get("description"),
+                    "confidence": relation.get("confidence"),
+                    "evidence": relation.get("evidence"),
+                },
+                ensure_ascii=False,
+            )
+        )
+    return "\n".join(rows)
+
+
+class SkillKnowledgeJobExecutor:
+    def __init__(self, app: Any) -> None:
+        self.app = app
+        self.store = app.state.skill_knowledge_store
+
+    async def _cancel_if_requested(self, item: dict[str, Any]) -> bool:
+        job = await self.store.get_job(str(item["job_id"]))
+        if not (job or {}).get("cancel_requested"):
+            return False
+        await self.store.update_item(
+            str(item["id"]),
+            status="canceled",
+            phase="completed",
+            progress=100,
+        )
+        return True
+
+    async def _assert_source_unchanged(self, item: dict[str, Any], expected_digest: str) -> None:
+        root = _skill_directory(self.app.state.config, str(item["skill_name"]))
+        current = await asyncio.to_thread(scan_skill, str(item["skill_name"]), root)
+        current = await enrich_converted_documents(current)
+        if current.source_digest == expected_digest:
+            return
+        await self.store.mark_skill_stale(str(item["skill_name"]))
+        raise RuntimeError("技能知识文件在归纳期间发生变化，旧版本保持激活，请重试")
+
+    async def _write_direct_graph(
+        self,
+        item: dict[str, Any],
+        snapshot_id: str,
+        ir: dict[str, Any],
+    ) -> dict[str, Any] | None:
+        config = self.app.state.config.llmwiki.weknora.graph
+        if not config.direct_write_enabled:
+            return None
+        if not config.neo4j_http_url or not config.username or not config.password:
+            raise RuntimeError("Neo4j direct writer 已开启但后端连接配置不完整")
+        plan = plan_from_ir(
+            ir,
+            target_type=str(item["target_type"]),
+            target_id=str(item["target_id"]),
+            binding_id=str(item["binding_id"]),
+            snapshot_id=snapshot_id,
+            job_id=str(item["job_id"]),
+        )
+        statements = parameterized_statements(plan)
+        if not statements:
+            return {"nodes": 0, "edges": 0, "statement_count": 0}
+        endpoint = f"{config.neo4j_http_url.rstrip('/')}/db/{config.database}/tx/commit"
+        async with httpx.AsyncClient(
+            auth=(config.username, config.password),
+            timeout=config.timeout_seconds,
+        ) as client:
+            response = await client.post(endpoint, json={"statements": statements})
+            response.raise_for_status()
+            payload = response.json()
+        errors = payload.get("errors") or []
+        if errors:
+            raise RuntimeError(f"Neo4j transaction failed: {str(errors[0].get('code') or 'unknown')}")
+        return {
+            "nodes": len(plan.nodes),
+            "edges": len(plan.edges),
+            "statement_count": len(statements),
+            "scope": plan.scope,
+        }
+
+    async def run_item(self, item: dict[str, Any]) -> None:
+        item_id = str(item["id"])
+        try:
+            await self.store.update_item(item_id, status="running", phase="scanning", progress=5)
+            root = _skill_directory(self.app.state.config, str(item["skill_name"]))
+            scan = await asyncio.to_thread(scan_skill, str(item["skill_name"]), root)
+            scan = await enrich_converted_documents(scan)
+            if await self._cancel_if_requested(item):
+                return
+            await self.store.update_item(item_id, phase="extracting", progress=30)
+            skill_record = await self.app.state.skill_store.get_any(str(item["skill_name"]))
+            ir = await asyncio.to_thread(
+                build_skill_ir,
+                scan,
+                display_name=(skill_record or {}).get("name_zh"),
+                description=str((skill_record or {}).get("description") or ""),
+            )
+            snapshot = await self.store.get_or_create_snapshot(
+                skill_name=str(item["skill_name"]),
+                source_digest=scan.source_digest,
+                extractor_version=str(ir["extractor_version"]),
+                prompt_hash=str(ir["prompt_hash"]),
+                model_name=str(ir["model_name"]),
+                manifest=scan.manifest,
+                ir=ir,
+                counts=scan.counts,
+            )
+            snapshot_id = str(snapshot["id"])
+            await self.store.update_item(item_id, phase="awaiting_review", progress=45, snapshot_id=snapshot_id)
+            job = await self.store.get_job(str(item["job_id"]))
+            review_mode = str((job or {}).get("review_mode") or "auto_high_confidence")
+            review_objects: list[tuple[str, dict]] = []
+            if review_mode == "required":
+                review_objects = [("entity", obj) for obj in ir.get("entities", [])] + [("relation", obj) for obj in ir.get("relations", [])]
+            elif review_mode == "auto_high_confidence":
+                review_objects = [(kind, obj) for kind, rows in (("entity", ir.get("entities", [])), ("relation", ir.get("relations", []))) for obj in rows if float(obj.get("confidence") or 0) < 0.9]
+            if review_objects:
+                existing = await self.store.list_reviews(item_id=item_id, status=None)
+                if not existing:
+                    await self.store.create_reviews(
+                        job_id=str(item["job_id"]),
+                        item_id=item_id,
+                        binding_id=str(item["binding_id"]),
+                        snapshot_id=snapshot_id,
+                        objects=review_objects,
+                    )
+                await self.store.update_item(item_id, status="awaiting_review", phase="awaiting_review", progress=50)
+                return
+            await self._project_and_activate(item, snapshot_id, ir, scan)
+        except asyncio.CancelledError:
+            raise
+        except Exception as exc:  # noqa: BLE001
+            logger.exception("Skill knowledge item failed: item=%s", item_id)
+            await self.store.update_item(
+                item_id,
+                status="failed",
+                phase="completed",
+                progress=100,
+                error_code=type(exc).__name__,
+                error_message=str(exc)[:2000],
+            )
+
+    async def resume_reviewed_item(self, item_id: str) -> None:
+        if await self.store.pending_review_count(item_id):
+            return
+        jobs = await self.store.list_jobs(limit=100)
+        item = None
+        for job in jobs:
+            detail = await self.store.get_job(str(job["id"]))
+            item = next((row for row in (detail or {}).get("items", []) if row["id"] == item_id), None)
+            if item:
+                break
+        if not item or not item.get("snapshot_id"):
+            return
+        snapshot = await self.store.get_snapshot(str(item["snapshot_id"]))
+        ir = await self.store.effective_ir(str(item["snapshot_id"]), item_id)
+        if not snapshot or not ir:
+            return
+        try:
+            await self.store.update_item(item_id, status="running", phase="merging", progress=55)
+            await self._project_and_activate(item, str(item["snapshot_id"]), ir, None)
+        except Exception as exc:  # noqa: BLE001
+            logger.exception("Reviewed skill knowledge item failed: item=%s", item_id)
+            await self.store.update_item(
+                item_id,
+                status="failed",
+                phase="completed",
+                progress=100,
+                error_code=type(exc).__name__,
+                error_message=str(exc)[:2000],
+            )
+
+    async def _project_and_activate(self, item: dict[str, Any], snapshot_id: str, ir: dict[str, Any], scan: Any | None) -> None:
+        if await self._cancel_if_requested(item):
+            return
+        expected_digest = str(ir.get("skill", {}).get("source_digest") or "")
+        if scan is None:
+            root = _skill_directory(self.app.state.config, str(item["skill_name"]))
+            scan = await asyncio.to_thread(scan_skill, str(item["skill_name"]), root)
+            scan = await enrich_converted_documents(scan)
+        await self._assert_source_unchanged(item, expected_digest)
+        await self.store.update_item(str(item["id"]), phase="writing_wiki", progress=65)
+        target_type = str(item["target_type"])
+        artifacts: list[dict[str, Any]] = []
+        binding_status = "synced"
+        page = ir["wiki"]["skill_page"]
+        if target_type == "assistant":
+            package = export_package_from_skill_ir(ir)
+            await self.app.state.assistant_knowledge_store.import_package(
+                package=package,
+                source_type="skill",
+                source_key=str(item["skill_name"]),
+                source_name=str(ir["skill"].get("display_name") or item["skill_name"]),
+                trigger="skill_direct",
+                created_by=(await self.store.get_job(str(item["job_id"]))).get("created_by"),
+                skill_name=str(item["skill_name"]),
+            )
+            artifacts.append({"kind": "assistant_wiki_revision", "slug": page["slug"], "content": page["content"], "business_key": f"assistant:wiki:{page['slug']}"})
+        elif str(item["target_mode"]) == "readonly_sidecar":
+            artifacts.append({"kind": "sidecar_wiki", "slug": page["slug"], "content": page["content"], "business_key": f"sidecar:{item['target_id']}:{page['slug']}"})
+            artifacts.append({"kind": "sidecar_graph", "slug": f"skills/{item['skill_name']}/graph-projection.jsonl", "content": _graph_projection(ir)})
+            binding_status = "pending_remote_write"
+        else:
+            mapping = await self.app.state.llmwiki_store.get_authorized(str(item["target_id"]), "system", write=True, is_admin=True)
+            if mapping is None:
+                raise ValueError("Target WeKnora mapping does not exist")
+            runtime = get_resolved_llmwiki_runtime(self.app.state.config)
+            if not runtime.weknora_enabled:
+                raise RuntimeError("普通知识库服务未配置")
+            client = build_weknora_client(runtime)
+            remote_kb_id = str(mapping["weknora_id"])
+            if scan is not None:
+                for file in scan.files:
+                    source_path = Path(scan.source_root) / file.path
+                    if file.text:
+                        remote = await client.create_manual_document(remote_kb_id, title=f"skills/{item['skill_name']}/source/{file.path}", content=file.text)
+                    else:
+                        remote = await client.upload_document(
+                            remote_kb_id,
+                            filename=f"{item['skill_name']}-{source_path.name}",
+                            content=await asyncio.to_thread(source_path.read_bytes),
+                        )
+                    remote_id = str(remote.get("id") or remote.get("knowledge_id") or "") or None
+                    artifacts.append(
+                        {
+                            "kind": "raw_source",
+                            "slug": file.path,
+                            "remote_id": remote_id,
+                            "business_key": f"weknora:document:{remote_id}" if remote_id else None,
+                            "remote_refs": [{"provider": "weknora", "remote_kind": "document", "remote_id": remote_id}] if remote_id else [],
+                        }
+                    )
+            target_mode = str(item["target_mode"])
+            if target_mode == "wiki":
+                remote_page: dict[str, Any]
+                try:
+                    await client.get_wiki_page(remote_kb_id, page["slug"])
+                except WeKnoraError as exc:
+                    if exc.status_code != 404:
+                        raise
+                    remote_page = await client.create_wiki_page(
+                        remote_kb_id,
+                        {
+                            "slug": page["slug"],
+                            "title": page["title"],
+                            "content": page["content"],
+                            "summary": str(ir["skill"].get("description") or ""),
+                            "page_type": "skill",
+                        },
+                    )
+                else:
+                    remote_page = await client.update_wiki_page(
+                        remote_kb_id,
+                        page["slug"],
+                        {
+                            "title": page["title"],
+                            "content": page["content"],
+                            "summary": str(ir["skill"].get("description") or ""),
+                        },
+                    )
+                remote_page_id = str(remote_page.get("id") or remote_page.get("page_id") or page["slug"])
+                artifacts.append(
+                    {
+                        "kind": "wiki_page",
+                        "slug": page["slug"],
+                        "content": page["content"],
+                        "remote_id": remote_page_id,
+                        "business_key": f"weknora:wiki_page:{remote_page_id}",
+                        "remote_refs": [
+                            {
+                                "provider": "weknora",
+                                "remote_kind": "wiki_page",
+                                "remote_id": remote_page_id,
+                                "slug": page["slug"],
+                            }
+                        ],
+                    }
+                )
+            else:
+                document_content = page["content"]
+                artifact_kind = "summary_document"
+                if target_mode == "faq":
+                    artifact_kind = "faq_projection"
+                    document_content = f"问题：{page['title']} 能解决什么问题，如何使用？\n\n回答：\n{page['content']}"
+                elif target_mode == "conversation_archive":
+                    artifact_kind = "conversation_archive"
+                    document_content = f"# 技能归纳会话归档：{page['title']}\n\n以下内容由技能知识归纳任务生成。\n\n{page['content']}"
+                remote_document = await client.create_manual_document(
+                    remote_kb_id,
+                    title=f"skills/{item['skill_name']}/{target_mode}.md",
+                    content=document_content,
+                )
+                remote_document_id = str(remote_document.get("id") or remote_document.get("knowledge_id") or "") or None
+                artifacts.append(
+                    {
+                        "kind": artifact_kind,
+                        "slug": f"skills/{item['skill_name']}/{target_mode}.md",
+                        "content": document_content,
+                        "remote_id": remote_document_id,
+                        "business_key": (f"weknora:document:{remote_document_id}" if remote_document_id else None),
+                        "remote_refs": (
+                            [
+                                {
+                                    "provider": "weknora",
+                                    "remote_kind": "document",
+                                    "remote_id": remote_document_id,
+                                }
+                            ]
+                            if remote_document_id
+                            else []
+                        ),
+                    }
+                )
+            projection = await client.create_manual_document(remote_kb_id, title=f"skills/{item['skill_name']}/graph-projection.jsonl", content=_graph_projection(ir))
+            projection_id = str(projection.get("id") or projection.get("knowledge_id") or "") or None
+            artifacts.append({"kind": "graph_projection", "slug": f"skills/{item['skill_name']}/graph-projection.jsonl", "remote_id": projection_id})
+            try:
+                graph_audit = await self._write_direct_graph(item, snapshot_id, ir)
+                if graph_audit is not None:
+                    artifacts.append(
+                        {
+                            "kind": "neo4j_direct_write_audit",
+                            "slug": f"skills/{item['skill_name']}/neo4j-audit.json",
+                            "content": json.dumps(graph_audit, ensure_ascii=False),
+                            "metadata": graph_audit,
+                        }
+                    )
+            except Exception as exc:  # noqa: BLE001
+                logger.exception("Optional Neo4j direct graph write failed: item=%s", item["id"])
+                binding_status = "partial_failed"
+                artifacts.append(
+                    {
+                        "kind": "neo4j_direct_write_audit",
+                        "slug": f"skills/{item['skill_name']}/neo4j-audit.json",
+                        "content": json.dumps(
+                            {"status": "failed", "error": type(exc).__name__},
+                            ensure_ascii=False,
+                        ),
+                        "metadata": {
+                            "status": "failed",
+                            "error_type": type(exc).__name__,
+                        },
+                    }
+                )
+        await self._assert_source_unchanged(item, expected_digest)
+        await self.store.update_item(str(item["id"]), phase="activating", progress=90)
+        await self.store.stage_and_activate(item_id=str(item["id"]), snapshot_id=snapshot_id, ir=ir, artifacts=artifacts, status=binding_status)

offline-backend-20260512/backend/config.yaml

@@ -448,7 +448,11 @@ auth_login:
 database:
   backend: sqlite
   sqlite_dir: .deer-flow/data
-  pool_size: 20 # 基础连接池(postgres/mysql 生效;sqlite 忽略)
+  # 高斯/OpenGauss 业务库示例（启用前先执行 uv sync --extra gauss）：
+  # backend: gauss
+  # gauss_url: $GAUSS_DATABASE_URL  # opengauss+asyncpg://user:pass@host:26000/deerflow
+  # checkpointer 请继续使用下方 sqlite；不要把 PostgreSQL 专用 checkpointer 迁移直接用于高斯。
+  pool_size: 20 # 基础连接池(postgres/gauss/mysql 生效;sqlite 忽略)
   max_overflow: 20 # 高峰期可临时超出 pool_size 的额外连接数,真实上限 = pool_size + max_overflow
   pool_timeout: 30 # 池满时等待空闲连接的秒数,超时报错
   pool_recycle: 1800 # 连接回收周期(秒);防火墙/pgbouncer/MySQL wait_timeout 静默断连兜底。多 worker 部署:每 worker 上限 = pool_size + max_overflow,总数 × worker 数 < DB max_connections
@@ -575,9 +579,18 @@ llmwiki:
     web_base_url: "http://119.254.86.251:8088"
     admin_email: "lqq@qq.com"
     admin_password: "1qaz@WSX"
-  # 本地 Wiki 向量化：只需把 enabled 改为 true，并填写下面 3 个 embedding 值。
-  # 默认只在页面点击“向量化”时执行；auto_sync=true 才会后台自动增量同步，
-  # 且服务启动时只启动定时器，不会立刻全量向量化。
+    # 高风险能力，默认关闭。连接参数仅允许后端配置；生产环境请使用环境变量。
+    graph:
+      direct_write_enabled: false
+      neo4j_http_url: ""
+      username: ""
+      password: ""
+      database: "neo4j"
+      timeout_seconds: 30
+  # 本地 Wiki 向量化：用于普通知识库“下载向量化数据包”和助手知识库向量检索。
+  # 已有相同内容哈希 + 相同 embedding fingerprint 的 Wiki 向量会直接复用，不会重复编码。
+  # 如需导出时自动补建向量，把 enabled 改为 true，并填写下面 embedding 服务地址。
+  # auto_sync=true 才会后台自动增量同步；服务启动时只启动定时器，不会立刻全量向量化。
   local_wiki_index:
     enabled: false
     # 免登录 POST /api/knowledge/vector-search 返回的 Wiki 一键跳转链接前缀。
@@ -585,7 +598,7 @@ llmwiki:
     auto_sync: false
     database_batch_size: 50
     embedding:
-      model: bge-m3
+      model: bge-embedding-m3
       base_url: ""
       api_key: ""
 

offline-backend-20260512/backend/packages/harness/deerflow/assistant_knowledge/__init__.py

@@ -0,0 +1,5 @@
+"""Assistant knowledge package helpers."""
+
+from deerflow.assistant_knowledge.importer import export_package_from_skill_ir
+
+__all__ = ["export_package_from_skill_ir"]

offline-backend-20260512/backend/packages/harness/deerflow/assistant_knowledge/importer.py

@@ -0,0 +1,22 @@
+"""Normalize heterogeneous sources into the assistant import-package shape."""
+
+from __future__ import annotations
+
+from typing import Any
+
+
+def export_package_from_skill_ir(ir: dict[str, Any]) -> dict[str, Any]:
+    skill = ir.get("skill") or {}
+    return {
+        "source": {
+            "type": "skill",
+            "skill_name": skill.get("name"),
+            "source_digest": skill.get("source_digest"),
+        },
+        "wiki_pages": [ir.get("wiki", {}).get("skill_page")],
+        "documents": ir.get("files") or [],
+        "chunks": [],
+        "entities": ir.get("entities") or [],
+        "relations": ir.get("relations") or [],
+        "graph": {"nodes": ir.get("entities") or [], "edges": ir.get("relations") or []},
+    }

offline-backend-20260512/backend/packages/harness/deerflow/assistant_knowledge/package_io.py

@@ -0,0 +1,109 @@
+"""File-backed assistant knowledge import/export packages.
+
+The assistant knowledge base is fed by WeKnora-exported data.  Keep that
+boundary explicit: a WeKnora export is first materialized as a durable JSONL
+package, then imported asynchronously from that package.  JSONL keeps large
+exports append-friendly and avoids treating the HTTP request body as the job
+state.
+"""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Iterable, Mapping
+from pathlib import Path
+from typing import Any
+
+PACKAGE_VERSION = 1
+PACKAGE_RECORD_TYPES = {
+    "source",
+    "wiki_page",
+    "document",
+    "chunk",
+    "vector",
+    "entity",
+    "relation",
+    "graph",
+}
+
+
+def package_path(root: Path, job_id: str) -> Path:
+    safe = "".join(ch for ch in str(job_id) if ch.isalnum() or ch in {"-", "_"})
+    return root / f"{safe}.jsonl"
+
+
+def write_jsonl_record(handle: Any, record_type: str, payload: Mapping[str, Any]) -> None:
+    handle.write(json.dumps({"record_type": record_type, "payload": dict(payload)}, ensure_ascii=False))
+    handle.write("\n")
+
+
+def write_package(path: Path, records: Iterable[tuple[str, Mapping[str, Any]]]) -> dict[str, int]:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    counts: dict[str, int] = {key: 0 for key in PACKAGE_RECORD_TYPES}
+    tmp_path = path.with_suffix(path.suffix + ".tmp")
+    with tmp_path.open("w", encoding="utf-8", newline="\n") as handle:
+        write_jsonl_record(
+            handle,
+            "source",
+            {
+                "package_version": PACKAGE_VERSION,
+            },
+        )
+        counts["source"] += 1
+        for record_type, payload in records:
+            if record_type not in PACKAGE_RECORD_TYPES:
+                continue
+            write_jsonl_record(handle, record_type, payload)
+            counts[record_type] = counts.get(record_type, 0) + 1
+    tmp_path.replace(path)
+    return counts
+
+
+def read_package(path: Path) -> dict[str, Any]:
+    package: dict[str, Any] = {
+        "source": {},
+        "wiki_pages": [],
+        "documents": [],
+        "chunks": [],
+        "vectors": [],
+        "entities": [],
+        "relations": [],
+        "graph": {"nodes": [], "edges": []},
+    }
+    with path.open("r", encoding="utf-8") as handle:
+        for line in handle:
+            line = line.strip()
+            if not line:
+                continue
+            record = json.loads(line)
+            if not isinstance(record, dict):
+                continue
+            record_type = str(record.get("record_type") or "")
+            payload = record.get("payload")
+            if not isinstance(payload, dict):
+                continue
+            if record_type == "source":
+                package["source"] = {**package.get("source", {}), **payload}
+            elif record_type == "wiki_page":
+                package["wiki_pages"].append(payload)
+            elif record_type == "document":
+                package["documents"].append(payload)
+            elif record_type == "chunk":
+                package["chunks"].append(payload)
+            elif record_type == "vector":
+                package["vectors"].append(payload)
+            elif record_type == "entity":
+                package["entities"].append(payload)
+            elif record_type == "relation":
+                package["relations"].append(payload)
+            elif record_type == "graph":
+                nodes = payload.get("nodes")
+                edges = payload.get("edges")
+                if isinstance(nodes, list):
+                    package["graph"]["nodes"].extend(item for item in nodes if isinstance(item, dict))
+                if isinstance(edges, list):
+                    package["graph"]["edges"].extend(item for item in edges if isinstance(item, dict))
+                for key, value in payload.items():
+                    if key not in {"nodes", "edges"}:
+                        package["graph"][key] = value
+    return package

offline-backend-20260512/backend/packages/harness/deerflow/config/database_config.py

@@ -14,6 +14,11 @@ busy timeout rather than failing immediately.
 Postgres mode: both use the same database URL but maintain independent
 connection pools with different lifecycles.
 
+Gauss mode: application ORM data uses the official openGauss SQLAlchemy
+dialect over asyncpg. LangGraph's PostgreSQL checkpointer is intentionally not
+reused for GaussDB; configure the legacy ``checkpointer:`` section separately
+(normally sqlite).
+
 MySQL mode: application ORM data uses MySQL. LangGraph's checkpointer does
 not have a built-in MySQL backend in this project, so deployments that choose
 MySQL for business tables should configure the legacy ``checkpointer:``
@@ -43,9 +48,15 @@ from pydantic import BaseModel, Field
 
 
 class DatabaseConfig(BaseModel):
-    backend: Literal["memory", "sqlite", "postgres", "mysql"] = Field(
+    backend: Literal["memory", "sqlite", "postgres", "gauss", "mysql"] = Field(
         default="memory",
-        description=("Storage backend for application data. 'memory' for development (no persistence across restarts), 'sqlite' for single-node deployment, 'postgres' for production multi-node deployment, 'mysql' for MySQL-backed business tables."),
+        description=(
+            "Storage backend for application data. 'memory' for development "
+            "(no persistence across restarts), 'sqlite' for single-node "
+            "deployment, 'postgres' for production multi-node deployment, "
+            "'gauss' for openGauss/GaussDB business tables, and 'mysql' for "
+            "MySQL-backed business tables."
+        ),
     )
     sqlite_dir: str = Field(
         default=".deer-flow/data",
@@ -60,6 +71,16 @@ class DatabaseConfig(BaseModel):
             "(the +asyncpg driver suffix is added automatically where needed)."
         ),
     )
+    gauss_url: str = Field(
+        default="",
+        description=(
+            "openGauss/GaussDB connection URL for the application ORM. "
+            "Use $GAUSS_DATABASE_URL in config.yaml to reference .env. "
+            "Example: opengauss+asyncpg://user:pass@host:26000/deerflow. "
+            "Plain opengauss:// or PostgreSQL-style URLs are normalized to the "
+            "official asynchronous openGauss SQLAlchemy dialect."
+        ),
+    )
     mysql_url: str = Field(
         default="",
         description=(
@@ -75,26 +96,26 @@ class DatabaseConfig(BaseModel):
     )
     pool_size: int = Field(
         default=5,
-        description="Connection pool size for the app ORM engine (postgres/mysql only).",
+        description="Connection pool size for the app ORM engine (postgres/gauss/mysql only).",
     )
     max_overflow: int = Field(
         default=10,
         description=(
-            "Extra connections allowed beyond pool_size under load (postgres/mysql only). "
+            "Extra connections allowed beyond pool_size under load (postgres/gauss/mysql only). "
             "The real connection ceiling is pool_size + max_overflow. SQLAlchemy default is 10."
         ),
     )
     pool_timeout: int = Field(
         default=30,
         description=(
-            "Seconds a caller waits for a free pooled connection before erroring (postgres/mysql only). "
+            "Seconds a caller waits for a free pooled connection before erroring (postgres/gauss/mysql only). "
             "SQLAlchemy default is 30."
         ),
     )
     pool_recycle: int = Field(
         default=1800,
         description=(
-            "Recycle pooled connections after this many seconds (postgres/mysql only). "
+            "Recycle pooled connections after this many seconds (postgres/gauss/mysql only). "
             "Prevents stale-connection errors when a firewall, pgbouncer, or MySQL "
             "wait_timeout silently drops idle connections. pool_pre_ping catches most "
             "cases, but pool_recycle is the belt-and-suspenders safety net. "
@@ -138,6 +159,32 @@ class DatabaseConfig(BaseModel):
             if url.startswith("postgresql://"):
                 url = url.replace("postgresql://", "postgresql+asyncpg://", 1)
             return url
+        if self.backend == "gauss":
+            url = self.gauss_url.strip()
+            if not url:
+                raise ValueError("database.gauss_url is required for backend='gauss'")
+
+            # GaussDB speaks the PostgreSQL wire protocol, but its server
+            # version string and DDL capabilities are not identical to
+            # PostgreSQL. Always select the official openGauss dialect instead
+            # of silently sending the URL through SQLAlchemy's generic PG
+            # dialect (the latter commonly connects and then fails while
+            # SQLAlchemy initializes/creates the schema).
+            for prefix in (
+                "opengauss://",
+                "gaussdb://",
+                "postgres://",
+                "postgresql://",
+                "postgresql+asyncpg://",
+            ):
+                if url.startswith(prefix):
+                    return "opengauss+asyncpg://" + url[len(prefix) :]
+            if url.startswith("opengauss+asyncpg://"):
+                return url
+            raise ValueError(
+                "database.gauss_url must use opengauss://, "
+                "opengauss+asyncpg://, or a PostgreSQL-style URL"
+            )
         if self.backend == "mysql":
             url = self.mysql_url
             if not url:

offline-backend-20260512/backend/packages/harness/deerflow/config/llmwiki_config.py

@@ -44,6 +44,7 @@ class WeKnoraConnectionConfig(BaseModel):
         repr=False,
         description=("Optional WeKnora administrator password used by DeerFlow's server-side WeKnora API client and detail-page iframe proxy. Prefer $ENV references."),
     )
+    graph: WeKnoraDirectGraphConfig = Field(default_factory=lambda: WeKnoraDirectGraphConfig())
     model_config = ConfigDict(extra="ignore")
 
     @field_validator("api_base_url", "web_base_url")
@@ -52,10 +53,27 @@ class WeKnoraConnectionConfig(BaseModel):
         return normalize_service_url(value)
 
 
+class WeKnoraDirectGraphConfig(BaseModel):
+    """Backend-only, opt-in Neo4j transactional HTTP writer settings."""
+
+    direct_write_enabled: bool = False
+    neo4j_http_url: str = ""
+    username: str = ""
+    password: str = Field(default="", repr=False)
+    database: str = Field(default="neo4j", pattern=r"^[A-Za-z0-9_.-]+$")
+    timeout_seconds: float = Field(default=30, gt=0, le=300)
+    model_config = ConfigDict(extra="ignore")
+
+    @field_validator("neo4j_http_url")
+    @classmethod
+    def _validate_url(cls, value: str) -> str:
+        return normalize_service_url(value)
+
+
 class LocalWikiEmbeddingConfig(BaseModel):
     enabled: bool = True
     provider: str = "openai"
-    model: str = "bge-m3"
+    model: str = "bge-embedding-m3"
     base_url: str = ""
     api_key: str = Field(default="", repr=False)
     dimensions: int | None = Field(

offline-backend-20260512/backend/packages/harness/deerflow/config/system_settings.py

@@ -160,6 +160,18 @@ class PromptPrefixSettings(BaseModel):
     model_config = ConfigDict(extra="ignore")
 
 
+class SkillKnowledgeAutoSyncSettings(BaseModel):
+    """Watcher/scheduler policy for bindings that explicitly opt in."""
+
+    watch_enabled: bool = True
+    schedule_enabled: bool = True
+    schedule_cron: str = Field(default="0 */6 * * *", min_length=5, max_length=128)
+    debounce_seconds: int = Field(default=120, ge=10, le=86_400)
+    write_remote_enabled: bool = True
+    review_mode: Literal["required", "auto_high_confidence", "off"] = "auto_high_confidence"
+    model_config = ConfigDict(extra="ignore")
+
+
 class AppearanceRouteParam(BaseModel):
     """Runtime query-param descriptor for top-bar shortcut buttons.
 
@@ -509,6 +521,11 @@ class SystemSettings(BaseModel):
     concurrency_monitor: ConcurrencyMonitorSettings = Field(default_factory=ConcurrencyMonitorSettings)
     model_concurrency: ModelConcurrencySettings = Field(default_factory=ModelConcurrencySettings)
     llmwiki: LlmWikiRuntimeOverride = Field(default_factory=LlmWikiRuntimeOverride)
+    skill_knowledge_auto_sync: SkillKnowledgeAutoSyncSettings = Field(default_factory=SkillKnowledgeAutoSyncSettings)
+    knowledge_base_product_version: Literal["v1", "v2"] = Field(
+        default="v1",
+        description="v1 opens the ordinary WeKnora experience; v2 opens the DeerFlow assistant knowledge base.",
+    )
     model_config = ConfigDict(extra="ignore")
 
 

offline-backend-20260512/backend/packages/harness/deerflow/integrations/weknora/client.py

@@ -494,7 +494,7 @@ class WeKnoraClient:
             await self._request(
                 "GET",
                 f"/api/v1/knowledgebase/{quote(knowledge_base_id, safe='')}/wiki/graph",
-                params={"mode": "overview", "limit": max(1, min(limit, 500))},
+                params={"mode": "overview", "limit": max(1, min(limit, 10000))},
             )
         )
         if not isinstance(value, dict):

offline-backend-20260512/backend/packages/harness/deerflow/integrations/weknora/local_index/__init__.py

@@ -1,7 +1,37 @@
 """DeerFlow-local mirror and Wiki-only retrieval for WeKnora Wiki pages."""
 
-from deerflow.integrations.weknora.local_index.retrieval import WikiRetrievalService, is_fully_vectorized
-from deerflow.integrations.weknora.local_index.search import WikiIndexError, WikiVectorSearchService
-from deerflow.integrations.weknora.local_index.sync import WikiSyncService
+from __future__ import annotations
 
-__all__ = ["WikiIndexError", "WikiRetrievalService", "WikiVectorSearchService", "WikiSyncService", "is_fully_vectorized"]
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from deerflow.integrations.weknora.local_index.retrieval import (
+        WikiRetrievalService,
+        is_fully_vectorized,
+    )
+    from deerflow.integrations.weknora.local_index.search import WikiIndexError, WikiVectorSearchService
+    from deerflow.integrations.weknora.local_index.sync import WikiSyncService
+
+__all__ = [
+    "WikiIndexError",
+    "WikiRetrievalService",
+    "WikiVectorSearchService",
+    "WikiSyncService",
+    "is_fully_vectorized",
+]
+
+
+def __getattr__(name: str) -> Any:
+    if name in {"WikiRetrievalService", "is_fully_vectorized"}:
+        from deerflow.integrations.weknora.local_index import retrieval
+
+        return getattr(retrieval, name)
+    if name in {"WikiIndexError", "WikiVectorSearchService"}:
+        from deerflow.integrations.weknora.local_index import search
+
+        return getattr(search, name)
+    if name == "WikiSyncService":
+        from deerflow.integrations.weknora.local_index.sync import WikiSyncService
+
+        return WikiSyncService
+    raise AttributeError(name)

offline-backend-20260512/backend/packages/harness/deerflow/persistence/assistant_knowledge/__init__.py

@@ -0,0 +1,4 @@
+from deerflow.persistence.assistant_knowledge.model import *  # noqa: F401,F403
+from deerflow.persistence.assistant_knowledge.store import AssistantKnowledgeRepository, make_assistant_knowledge_store  # noqa: F401
+
+__all__ = [name for name in globals() if name.startswith("AssistantKnowledge") or name == "make_assistant_knowledge_store"]

offline-backend-20260512/backend/packages/harness/deerflow/persistence/assistant_knowledge/model.py

@@ -0,0 +1,174 @@
+"""ORM model for DeerFlow-native assistant knowledge bases."""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime
+
+from sqlalchemy import Float, Index, Integer, LargeBinary, String, UniqueConstraint
+from sqlalchemy.orm import Mapped, mapped_column
+
+from deerflow.persistence.base import Base
+from deerflow.persistence.types import BeijingDateTime, PortableJSON, PortableLongText
+
+
+def _now() -> datetime:
+    return datetime.now(UTC)
+
+
+class AssistantKnowledgeBaseRow(Base):
+    __tablename__ = "assistant_knowledge_base"
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    name: Mapped[str] = mapped_column(String(255), default="知识梳理")
+    description: Mapped[str | None] = mapped_column(String(2048), nullable=True)
+    # ``global`` is the full-library aggregation base. ``custom`` bases are
+    # user-created assistant knowledge bases fed from WeKnora export packages.
+    # Nullable keeps startup column reconciliation safe for existing databases.
+    base_kind: Mapped[str | None] = mapped_column(String(24), nullable=True, index=True)
+    status: Mapped[str] = mapped_column(String(64), default="active")
+    created_by: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+    updated_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now, onupdate=_now)
+
+
+class AssistantKnowledgeSourceRow(Base):
+    __tablename__ = "assistant_knowledge_sources"
+    __table_args__ = (UniqueConstraint("base_id", "source_type", "source_key", name="uq_assistant_knowledge_source"),)
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    base_id: Mapped[str] = mapped_column(String(36), index=True)
+    source_type: Mapped[str] = mapped_column(String(32), index=True)
+    source_key: Mapped[str] = mapped_column(String(255))
+    knowledge_base_mapping_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
+    skill_name: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    upload_batch_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
+    source_name: Mapped[str] = mapped_column(String(255), default="")
+    last_import_job_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
+    status: Mapped[str] = mapped_column(String(24), default="active")
+    metadata_json: Mapped[dict | None] = mapped_column(PortableJSON(), default=dict, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+    updated_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now, onupdate=_now)
+
+
+class AssistantKnowledgeImportJobRow(Base):
+    __tablename__ = "assistant_knowledge_import_jobs"
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    base_id: Mapped[str] = mapped_column(String(36), index=True)
+    source_id: Mapped[str] = mapped_column(String(36), index=True)
+    trigger: Mapped[str] = mapped_column(String(48), default="manual_from_weknora")
+    status: Mapped[str] = mapped_column(String(32), default="queued", index=True)
+    phase: Mapped[str] = mapped_column(String(32), default="exporting")
+    counts_json: Mapped[dict | None] = mapped_column(PortableJSON(), default=dict, nullable=True)
+    last_error_message: Mapped[str | None] = mapped_column(String(2048), nullable=True)
+    created_by: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+    completed_at: Mapped[datetime | None] = mapped_column(BeijingDateTime(), nullable=True)
+
+
+class AssistantKnowledgeWikiPageRow(Base):
+    __tablename__ = "assistant_knowledge_wiki_pages"
+    __table_args__ = (UniqueConstraint("base_id", "slug", name="uq_assistant_wiki_slug"),)
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    base_id: Mapped[str] = mapped_column(String(36), index=True)
+    slug: Mapped[str] = mapped_column(String(512), index=True)
+    title: Mapped[str] = mapped_column(String(512), index=True)
+    page_type: Mapped[str] = mapped_column(String(32), index=True)
+    current_revision_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
+    content_markdown: Mapped[str] = mapped_column(PortableLongText(), default="")
+    summary: Mapped[str] = mapped_column(String(2048), default="")
+    status: Mapped[str] = mapped_column(String(24), default="active")
+    search_text: Mapped[str] = mapped_column(PortableLongText(), default="")
+    updated_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now, onupdate=_now)
+
+
+class AssistantKnowledgeWikiRevisionRow(Base):
+    __tablename__ = "assistant_knowledge_wiki_revisions"
+    __table_args__ = (UniqueConstraint("page_id", "revision_no", name="uq_assistant_wiki_revision"),)
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    page_id: Mapped[str] = mapped_column(String(36), index=True)
+    revision_no: Mapped[int] = mapped_column(Integer)
+    content_markdown: Mapped[str] = mapped_column(PortableLongText(), default="")
+    summary: Mapped[str] = mapped_column(String(2048), default="")
+    source_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
+    import_job_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
+    change_type: Mapped[str] = mapped_column(String(32), default="import")
+    status: Mapped[str] = mapped_column(String(24), default="published")
+    created_by: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+    published_at: Mapped[datetime | None] = mapped_column(BeijingDateTime(), nullable=True)
+
+
+class AssistantKnowledgeVectorRow(Base):
+    __tablename__ = "assistant_knowledge_vectors"
+    __table_args__ = (
+        Index("ix_assistant_knowledge_vector_base_fingerprint", "base_id", "embedding_fingerprint"),
+        Index("ix_assistant_knowledge_vector_page", "page_id"),
+        Index("ix_assistant_knowledge_vector_source", "source_id"),
+    )
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    base_id: Mapped[str] = mapped_column(String(36), index=True)
+    page_id: Mapped[str] = mapped_column(String(36), index=True)
+    source_id: Mapped[str] = mapped_column(String(36), index=True)
+    import_job_id: Mapped[str] = mapped_column(String(36), index=True)
+    source_vector_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    source_chunk_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    section_index: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    heading: Mapped[str | None] = mapped_column(String(1024), nullable=True)
+    section_content: Mapped[str] = mapped_column(PortableLongText(), nullable=False, default="")
+    content_hash: Mapped[str] = mapped_column(String(64), nullable=False, index=True)
+    embedding_model: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    embedding_fingerprint: Mapped[str | None] = mapped_column(String(64), nullable=True, index=True)
+    embedding_dimensions: Mapped[int] = mapped_column(Integer, nullable=False)
+    vector_blob: Mapped[bytes] = mapped_column(LargeBinary, nullable=False)
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+
+
+class AssistantKnowledgeEntityRow(Base):
+    __tablename__ = "assistant_knowledge_entities"
+    __table_args__ = (UniqueConstraint("base_id", "entity_type", "normalized_name", name="uq_assistant_entity"),)
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    base_id: Mapped[str] = mapped_column(String(36), index=True)
+    entity_type: Mapped[str] = mapped_column(String(64), index=True)
+    normalized_name: Mapped[str] = mapped_column(String(512))
+    display_name: Mapped[str] = mapped_column(String(512), index=True)
+    aliases_json: Mapped[list | None] = mapped_column(PortableJSON(), default=list, nullable=True)
+    attributes_json: Mapped[dict | None] = mapped_column(PortableJSON(), default=dict, nullable=True)
+    wiki_page_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
+    status: Mapped[str] = mapped_column(String(24), default="active")
+
+
+class AssistantKnowledgeEntityContributionRow(Base):
+    __tablename__ = "assistant_knowledge_entity_contributions"
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    entity_id: Mapped[str] = mapped_column(String(36), index=True)
+    source_id: Mapped[str] = mapped_column(String(36), index=True)
+    import_job_id: Mapped[str] = mapped_column(String(36), index=True)
+    source_wiki_slug: Mapped[str | None] = mapped_column(String(512), nullable=True)
+    source_chunk_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    source_skill_name: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    evidence_json: Mapped[list | None] = mapped_column(PortableJSON(), default=list, nullable=True)
+    confidence: Mapped[float] = mapped_column(Float, default=1.0)
+
+
+class AssistantKnowledgeRelationRow(Base):
+    __tablename__ = "assistant_knowledge_relations"
+    __table_args__ = (UniqueConstraint("base_id", "source_entity_id", "predicate", "target_entity_id", name="uq_assistant_relation"),)
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    base_id: Mapped[str] = mapped_column(String(36), index=True)
+    source_entity_id: Mapped[str] = mapped_column(String(36), index=True)
+    predicate: Mapped[str] = mapped_column(String(128), index=True)
+    target_entity_id: Mapped[str] = mapped_column(String(36), index=True)
+    description: Mapped[str] = mapped_column(String(2048), default="")
+    wiki_page_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
+    status: Mapped[str] = mapped_column(String(24), default="active")
+
+
+class AssistantKnowledgeRelationContributionRow(Base):
+    __tablename__ = "assistant_knowledge_relation_contributions"
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    relation_id: Mapped[str] = mapped_column(String(36), index=True)
+    source_id: Mapped[str] = mapped_column(String(36), index=True)
+    import_job_id: Mapped[str] = mapped_column(String(36), index=True)
+    source_wiki_slug: Mapped[str | None] = mapped_column(String(512), nullable=True)
+    graph_edge_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    evidence_json: Mapped[list | None] = mapped_column(PortableJSON(), default=list, nullable=True)
+    confidence: Mapped[float] = mapped_column(Float, default=1.0)

offline-backend-20260512/backend/packages/harness/deerflow/persistence/engine.py

@@ -176,7 +176,7 @@ async def _auto_create_postgres_db(url: str) -> None:
     try:
         async with maint_engine.connect() as conn:
             await conn.execute(text(f'CREATE DATABASE "{db_name}"'))
-        logger.info("Auto-created PostgreSQL database: %s", db_name)
+        logger.info("Auto-created PostgreSQL-compatible database: %s", db_name)
     finally:
         await maint_engine.dispose()
 
@@ -230,10 +230,10 @@ def _ensure_orm_columns_sync(sync_conn) -> None:
     added column are created as well. Table-level ``UniqueConstraint`` objects
     that are declared on the model but missing from the table are also created
     (as a ``CREATE UNIQUE INDEX`` of the same name, which is what the migrations
-    emit too, and works on SQLite/MySQL/PostgreSQL alike) — without this a newly
+    emit too, and works on SQLite/MySQL/PostgreSQL/openGauss alike) — without this a newly
     declared uniqueness rule (e.g. ``active_dedupe_key``) would silently be absent
     on an existing database, weakening dedupe.
-    Works on sqlite/mysql/postgres — the column DDL is compiled through the
+    Works on sqlite/mysql/postgres/gauss — the column DDL is compiled through the
     active dialect, so portable types (``PortableJSON``/``PortableLongText``/
     ``BeijingDateTime``) resolve to the right backend type. Everything is
     per-column best-effort: a failure is logged and startup continues.
@@ -355,13 +355,13 @@ async def init_engine(
     """Create the async engine and session factory, then auto-create tables.
 
     Args:
-        backend: "memory", "sqlite", "postgres", or "mysql".
-        url: SQLAlchemy async URL (for sqlite/postgres/mysql).
+        backend: "memory", "sqlite", "postgres", "gauss", or "mysql".
+        url: SQLAlchemy async URL (for sqlite/postgres/gauss/mysql).
         echo: Echo SQL to log.
-        pool_size: Base connection pool size (postgres/mysql).
-        max_overflow: Extra connections allowed beyond pool_size under load (postgres/mysql).
-        pool_timeout: Seconds to wait for a free pooled connection before erroring (postgres/mysql).
-        pool_recycle: Recycle connections after N seconds (postgres/mysql). Prevents stale
+        pool_size: Base connection pool size (postgres/gauss/mysql).
+        max_overflow: Extra connections allowed beyond pool_size under load (postgres/gauss/mysql).
+        pool_timeout: Seconds to wait for a free pooled connection before erroring (postgres/gauss/mysql).
+        pool_recycle: Recycle connections after N seconds (postgres/gauss/mysql). Prevents stale
             connections dropped by firewalls / MySQL wait_timeout / pgbouncer.
         sqlite_dir: Directory to create for SQLite (ensured to exist).
     """
@@ -379,11 +379,21 @@ async def init_engine(
     except RuntimeError:
         _engine_loop = None
 
-    if backend == "postgres":
+    if backend in {"postgres", "gauss"}:
         try:
             import asyncpg  # noqa: F401
         except ImportError:
-            raise ImportError("database.backend is set to 'postgres' but asyncpg is not installed.\nInstall it with:\n    uv sync --extra postgres\nOr switch to backend: sqlite in config.yaml for single-node deployment.") from None
+            extra = "gauss" if backend == "gauss" else "postgres"
+            raise ImportError(
+                f"database.backend is set to {backend!r} but asyncpg is not installed.\n"
+                f"Install it with:\n    uv sync --extra {extra}\n"
+                "Or switch to backend: sqlite in config.yaml for single-node deployment."
+            ) from None
+    if backend == "gauss" and find_spec("opengauss_sqlalchemy") is None:
+        raise ImportError(
+            "database.backend is set to 'gauss' but the openGauss SQLAlchemy dialect "
+            "is not installed.\nInstall it with:\n    uv sync --extra gauss"
+        )
     if backend == "mysql":
         _add_runtime_vendor_path()
         driver = _mysql_driver_name(url)
@@ -423,7 +433,7 @@ async def init_engine(
                 cursor.execute("PRAGMA foreign_keys=ON;")
             finally:
                 cursor.close()
-    elif backend == "postgres":
+    elif backend in {"postgres", "gauss"}:
         _engine = create_async_engine(
             url,
             echo=echo,
@@ -468,7 +478,7 @@ async def init_engine(
         async with _engine.begin() as conn:
             await conn.run_sync(Base.metadata.create_all)
     except Exception as exc:
-        if backend == "postgres" and "does not exist" in str(exc):
+        if backend in {"postgres", "gauss"} and "does not exist" in str(exc).lower():
             # Database not yet created — attempt to auto-create it, then retry.
             await _auto_create_postgres_db(url)
             # Rebuild engine against the now-existing database
@@ -511,7 +521,7 @@ async def init_engine(
     # cannot do on its own. Failures don't abort startup — see the helper.
     # await _run_alembic_upgrade(url)
 
-    logger.info("Persistence engine initialized: backend=%s", backend)
+    logger.info("Persistence engine initialized: backend=%s dialect=%s", backend, _engine.dialect.name)
 
 
 async def init_engine_from_config(config) -> None:

offline-backend-20260512/backend/packages/harness/deerflow/persistence/llmwiki_index/base.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from collections.abc import AsyncIterator
 from typing import Any
 
 
@@ -35,6 +36,23 @@ class LlmWikiIndexStore(ABC):
     @abstractmethod
     async def load_vector_snapshot(self, mapping_id: str, fingerprint: str) -> dict[str, Any]: ...
 
+    async def iter_vector_snapshot_rows(
+        self,
+        mapping_id: str,
+        fingerprint: str,
+        *,
+        batch_size: int = 1000,
+    ) -> AsyncIterator[dict[str, Any]]:
+        """Iterate vector rows for streaming export.
+
+        Implementations with a database backend should override this method so
+        very large vector packages do not need to be materialized in memory.
+        """
+        snapshot = await self.load_vector_snapshot(mapping_id, fingerprint)
+        for row in snapshot.get("rows") or []:
+            if isinstance(row, dict):
+                yield row
+
     @abstractmethod
     async def get_index_revision(self, mapping_id: str) -> tuple[int, str | None, str]: ...
 

offline-backend-20260512/backend/packages/harness/deerflow/persistence/llmwiki_index/sql.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+from collections.abc import AsyncIterator
 from datetime import UTC, datetime, timedelta
 from typing import Any
 from uuid import uuid4
@@ -181,6 +182,41 @@ class SqlLlmWikiIndexStore(LlmWikiIndexStore):
             rows = [{**_dict(vector), "page": _dict(page)} for vector, page in pairs]
             return {"revision": int(state.index_revision if state else 0), "fingerprint": fingerprint, "rows": rows}
 
+    async def iter_vector_snapshot_rows(
+        self,
+        mapping_id: str,
+        fingerprint: str,
+        *,
+        batch_size: int = 1000,
+    ) -> AsyncIterator[dict[str, Any]]:
+        batch = max(1, min(int(batch_size), 5000))
+        offset = 0
+        while True:
+            stmt = (
+                select(LlmWikiWikiVectorRow, LlmWikiWikiPageRow)
+                .join(LlmWikiWikiPageRow, LlmWikiWikiPageRow.id == LlmWikiWikiVectorRow.page_id)
+                .where(
+                    LlmWikiWikiVectorRow.knowledge_base_mapping_id == mapping_id,
+                    LlmWikiWikiVectorRow.embedding_fingerprint == fingerprint,
+                    LlmWikiWikiPageRow.index_status == "ready",
+                    LlmWikiWikiPageRow.is_remote_deleted.is_(False),
+                    LlmWikiWikiPageRow.status.in_(["published", "draft"]),
+                )
+                .order_by(LlmWikiWikiVectorRow.page_id, LlmWikiWikiVectorRow.section_index)
+                .limit(batch)
+                .offset(offset)
+            )
+            async with self._sf() as session:
+                pairs = list((await session.execute(stmt)).all())
+                rows = [{**_dict(vector), "page": _dict(page)} for vector, page in pairs]
+            if not rows:
+                break
+            for row in rows:
+                yield row
+            if len(rows) < batch:
+                break
+            offset += len(rows)
+
     async def get_index_revision(self, mapping_id: str) -> tuple[int, str | None, str]:
         async with self._sf() as session:
             state = await session.get(LlmWikiWikiSyncStateRow, mapping_id)

offline-backend-20260512/backend/packages/harness/deerflow/persistence/migrations/versions/20260901_01_skill_and_assistant_knowledge.py

@@ -0,0 +1,73 @@
+"""Add skill distillation and assistant knowledge tables.
+
+Revision ID: 20260901_01
+Revises: 20260825_01
+Create Date: 2026-09-01
+"""
+
+from __future__ import annotations
+
+from alembic import op
+
+from deerflow.persistence.assistant_knowledge.model import (
+    AssistantKnowledgeBaseRow,
+    AssistantKnowledgeEntityContributionRow,
+    AssistantKnowledgeEntityRow,
+    AssistantKnowledgeImportJobRow,
+    AssistantKnowledgeRelationContributionRow,
+    AssistantKnowledgeRelationRow,
+    AssistantKnowledgeSourceRow,
+    AssistantKnowledgeWikiPageRow,
+    AssistantKnowledgeWikiRevisionRow,
+)
+from deerflow.persistence.skill_knowledge.model import (
+    SkillKnowledgeArtifactRow,
+    SkillKnowledgeBindingRow,
+    SkillKnowledgeEntityContributionRow,
+    SkillKnowledgeEntityRow,
+    SkillKnowledgeRelationContributionRow,
+    SkillKnowledgeRelationRow,
+    SkillKnowledgeReviewRow,
+    SkillKnowledgeSnapshotRow,
+    SkillKnowledgeSyncItemRow,
+    SkillKnowledgeSyncJobRow,
+)
+
+revision = "20260901_01"
+down_revision = "20260825_01"
+branch_labels = None
+depends_on = None
+
+TABLES = [
+    SkillKnowledgeBindingRow,
+    SkillKnowledgeSnapshotRow,
+    SkillKnowledgeSyncJobRow,
+    SkillKnowledgeSyncItemRow,
+    SkillKnowledgeArtifactRow,
+    SkillKnowledgeReviewRow,
+    SkillKnowledgeEntityRow,
+    SkillKnowledgeEntityContributionRow,
+    SkillKnowledgeRelationRow,
+    SkillKnowledgeRelationContributionRow,
+    AssistantKnowledgeBaseRow,
+    AssistantKnowledgeSourceRow,
+    AssistantKnowledgeImportJobRow,
+    AssistantKnowledgeWikiPageRow,
+    AssistantKnowledgeWikiRevisionRow,
+    AssistantKnowledgeEntityRow,
+    AssistantKnowledgeEntityContributionRow,
+    AssistantKnowledgeRelationRow,
+    AssistantKnowledgeRelationContributionRow,
+]
+
+
+def upgrade() -> None:
+    bind = op.get_bind()
+    for model in TABLES:
+        model.__table__.create(bind, checkfirst=True)
+
+
+def downgrade() -> None:
+    bind = op.get_bind()
+    for model in reversed(TABLES):
+        model.__table__.drop(bind, checkfirst=True)

offline-backend-20260512/backend/packages/harness/deerflow/persistence/migrations/versions/20260902_01_assistant_knowledge_vectors.py

@@ -0,0 +1,25 @@
+"""Add assistant knowledge vector table.
+
+Revision ID: 20260902_01
+Revises: 20260901_01
+Create Date: 2026-09-02
+"""
+
+from __future__ import annotations
+
+from alembic import op
+
+from deerflow.persistence.assistant_knowledge.model import AssistantKnowledgeVectorRow
+
+revision = "20260902_01"
+down_revision = "20260901_01"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    AssistantKnowledgeVectorRow.__table__.create(op.get_bind(), checkfirst=True)
+
+
+def downgrade() -> None:
+    AssistantKnowledgeVectorRow.__table__.drop(op.get_bind(), checkfirst=True)

offline-backend-20260512/backend/packages/harness/deerflow/persistence/models/__init__.py

@@ -35,6 +35,18 @@ from deerflow.persistence.agents.model import (
     AgentRow,
     AgentSkillRow,
 )
+from deerflow.persistence.assistant_knowledge.model import (
+    AssistantKnowledgeBaseRow,
+    AssistantKnowledgeEntityContributionRow,
+    AssistantKnowledgeEntityRow,
+    AssistantKnowledgeImportJobRow,
+    AssistantKnowledgeRelationContributionRow,
+    AssistantKnowledgeRelationRow,
+    AssistantKnowledgeSourceRow,
+    AssistantKnowledgeVectorRow,
+    AssistantKnowledgeWikiPageRow,
+    AssistantKnowledgeWikiRevisionRow,
+)
 from deerflow.persistence.business_mapping.model import BusinessMappingRow
 from deerflow.persistence.compact_menu_overrides.model import CompactMenuOverrideRow
 from deerflow.persistence.concurrency.model import ConcurrencySampleRow
@@ -98,6 +110,18 @@ from deerflow.persistence.scheduled_tasks.model import (
     SchedulerThreadRow,
 )
 from deerflow.persistence.sensitive_words.model import SensitiveWordRow
+from deerflow.persistence.skill_knowledge.model import (
+    SkillKnowledgeArtifactRow,
+    SkillKnowledgeBindingRow,
+    SkillKnowledgeEntityContributionRow,
+    SkillKnowledgeEntityRow,
+    SkillKnowledgeRelationContributionRow,
+    SkillKnowledgeRelationRow,
+    SkillKnowledgeReviewRow,
+    SkillKnowledgeSnapshotRow,
+    SkillKnowledgeSyncItemRow,
+    SkillKnowledgeSyncJobRow,
+)
 from deerflow.persistence.skills.model import SkillDisplayAdapterRow, SkillFavoriteRow, SkillRow
 from deerflow.persistence.tags.model import TagAssignmentRow, TagRow
 from deerflow.persistence.task_buttons.model import TaskButtonRow
@@ -119,6 +143,15 @@ __all__ = [
     "AgentSkillRow",
     "AdminLeaderboardDailyStatRow",
     "BusinessMappingRow",
+    "AssistantKnowledgeBaseRow",
+    "AssistantKnowledgeSourceRow",
+    "AssistantKnowledgeImportJobRow",
+    "AssistantKnowledgeWikiPageRow",
+    "AssistantKnowledgeWikiRevisionRow",
+    "AssistantKnowledgeEntityRow",
+    "AssistantKnowledgeEntityContributionRow",
+    "AssistantKnowledgeRelationRow",
+    "AssistantKnowledgeRelationContributionRow",
     "CompactMenuOverrideRow",
     "ConcurrencySampleRow",
     "DashboardSessionRow",
@@ -185,6 +218,16 @@ __all__ = [
     "SkillRow",
     "SkillDisplayAdapterRow",
     "SkillFavoriteRow",
+    "SkillKnowledgeArtifactRow",
+    "SkillKnowledgeBindingRow",
+    "SkillKnowledgeEntityContributionRow",
+    "SkillKnowledgeEntityRow",
+    "SkillKnowledgeRelationContributionRow",
+    "SkillKnowledgeRelationRow",
+    "SkillKnowledgeReviewRow",
+    "SkillKnowledgeSnapshotRow",
+    "SkillKnowledgeSyncItemRow",
+    "SkillKnowledgeSyncJobRow",
     "TagAssignmentRow",
     "TagRow",
     "TaskButtonRow",

offline-backend-20260512/backend/packages/harness/deerflow/persistence/skill_knowledge/__init__.py

@@ -0,0 +1,15 @@
+from deerflow.persistence.skill_knowledge.model import (  # noqa: F401
+    SkillKnowledgeArtifactRow,
+    SkillKnowledgeBindingRow,
+    SkillKnowledgeEntityContributionRow,
+    SkillKnowledgeEntityRow,
+    SkillKnowledgeRelationContributionRow,
+    SkillKnowledgeRelationRow,
+    SkillKnowledgeReviewRow,
+    SkillKnowledgeSnapshotRow,
+    SkillKnowledgeSyncItemRow,
+    SkillKnowledgeSyncJobRow,
+)
+from deerflow.persistence.skill_knowledge.store import SkillKnowledgeRepository, make_skill_knowledge_store  # noqa: F401
+
+__all__ = [name for name in globals() if name.startswith("SkillKnowledge") or name == "make_skill_knowledge_store"]

offline-backend-20260512/backend/packages/harness/deerflow/persistence/skill_knowledge/model.py

@@ -0,0 +1,203 @@
+"""ORM rows for skill-to-knowledge distillation and review."""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime
+
+from sqlalchemy import Boolean, Float, Integer, String, UniqueConstraint, false, true
+from sqlalchemy.orm import Mapped, mapped_column
+
+from deerflow.persistence.base import Base
+from deerflow.persistence.types import BeijingDateTime, PortableJSON, PortableLongText
+
+
+def _now() -> datetime:
+    return datetime.now(UTC)
+
+
+class SkillKnowledgeBindingRow(Base):
+    __tablename__ = "skill_knowledge_bindings"
+    __table_args__ = (UniqueConstraint("skill_name", "target_type", "target_id", name="uq_skill_knowledge_binding"),)
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    skill_name: Mapped[str] = mapped_column(String(255), index=True)
+    target_type: Mapped[str] = mapped_column(String(32), index=True)
+    target_mode: Mapped[str] = mapped_column(String(32), default="wiki")
+    target_id: Mapped[str] = mapped_column(String(128), index=True)
+    target_name: Mapped[str] = mapped_column(String(255), default="")
+    remote_business_key: Mapped[str | None] = mapped_column(String(512), nullable=True)
+    remote_refs_json: Mapped[list | None] = mapped_column(PortableJSON(), default=list, nullable=True)
+    status: Mapped[str] = mapped_column(String(48), default="pending", index=True)
+    active: Mapped[bool] = mapped_column(Boolean, default=True, server_default=true(), index=True)
+    auto_sync_enabled: Mapped[bool] = mapped_column(Boolean, default=False, server_default=false(), index=True)
+    current_snapshot_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
+    source_digest: Mapped[str | None] = mapped_column(String(80), nullable=True)
+    synced_digest: Mapped[str | None] = mapped_column(String(80), nullable=True)
+    last_job_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
+    last_item_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
+    last_synced_at: Mapped[datetime | None] = mapped_column(BeijingDateTime(), nullable=True)
+    last_error_code: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    last_error_message: Mapped[str | None] = mapped_column(String(2048), nullable=True)
+    created_by: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+    updated_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now, onupdate=_now)
+
+
+class SkillKnowledgeSnapshotRow(Base):
+    __tablename__ = "skill_knowledge_snapshots"
+    __table_args__ = (UniqueConstraint("skill_name", "source_digest", "extractor_version", "prompt_hash", "model_name", name="uq_skill_knowledge_snapshot"),)
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    skill_name: Mapped[str] = mapped_column(String(255), index=True)
+    source_digest: Mapped[str] = mapped_column(String(80), index=True)
+    extractor_version: Mapped[str] = mapped_column(String(64))
+    prompt_hash: Mapped[str] = mapped_column(String(80))
+    model_name: Mapped[str] = mapped_column(String(128))
+    manifest_json: Mapped[list | None] = mapped_column(PortableJSON(), default=list, nullable=True)
+    ir_json: Mapped[dict | None] = mapped_column(PortableJSON(), default=dict, nullable=True)
+    status: Mapped[str] = mapped_column(String(24), default="ready")
+    counts_json: Mapped[dict | None] = mapped_column(PortableJSON(), default=dict, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+
+
+class SkillKnowledgeSyncJobRow(Base):
+    __tablename__ = "skill_knowledge_sync_jobs"
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    trigger: Mapped[str] = mapped_column(String(32), default="manual")
+    target_count: Mapped[int] = mapped_column(Integer, default=0)
+    targets_json: Mapped[list | None] = mapped_column(PortableJSON(), default=list, nullable=True)
+    status: Mapped[str] = mapped_column(String(32), default="queued", index=True)
+    total_count: Mapped[int] = mapped_column(Integer, default=0)
+    success_count: Mapped[int] = mapped_column(Integer, default=0)
+    partial_failed_count: Mapped[int] = mapped_column(Integer, default=0)
+    failed_count: Mapped[int] = mapped_column(Integer, default=0)
+    cancel_requested: Mapped[bool] = mapped_column(Boolean, default=False, server_default=false())
+    review_mode: Mapped[str] = mapped_column(String(32), default="auto_high_confidence")
+    created_by: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+    started_at: Mapped[datetime | None] = mapped_column(BeijingDateTime(), nullable=True)
+    completed_at: Mapped[datetime | None] = mapped_column(BeijingDateTime(), nullable=True)
+
+
+class SkillKnowledgeSyncItemRow(Base):
+    __tablename__ = "skill_knowledge_sync_items"
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    job_id: Mapped[str] = mapped_column(String(36), index=True)
+    binding_id: Mapped[str] = mapped_column(String(36), index=True)
+    target_type: Mapped[str] = mapped_column(String(32))
+    target_mode: Mapped[str] = mapped_column(String(32))
+    target_id: Mapped[str] = mapped_column(String(128))
+    skill_name: Mapped[str] = mapped_column(String(255), index=True)
+    snapshot_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
+    status: Mapped[str] = mapped_column(String(32), default="queued", index=True)
+    phase: Mapped[str] = mapped_column(String(32), default="scanning")
+    progress: Mapped[int] = mapped_column(Integer, default=0)
+    attempts: Mapped[int] = mapped_column(Integer, default=0)
+    active_dedupe_key: Mapped[str | None] = mapped_column(String(36), nullable=True, unique=True)
+    lease_owner: Mapped[str | None] = mapped_column(String(128), nullable=True, index=True)
+    lease_until: Mapped[datetime | None] = mapped_column(BeijingDateTime(), nullable=True, index=True)
+    last_error_code: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    last_error_message: Mapped[str | None] = mapped_column(String(2048), nullable=True)
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+    updated_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now, onupdate=_now)
+    started_at: Mapped[datetime | None] = mapped_column(BeijingDateTime(), nullable=True)
+    completed_at: Mapped[datetime | None] = mapped_column(BeijingDateTime(), nullable=True)
+
+
+class SkillKnowledgeArtifactRow(Base):
+    __tablename__ = "skill_knowledge_artifacts"
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    binding_id: Mapped[str] = mapped_column(String(36), index=True)
+    snapshot_id: Mapped[str] = mapped_column(String(36), index=True)
+    kind: Mapped[str] = mapped_column(String(48), index=True)
+    slug: Mapped[str | None] = mapped_column(String(512), nullable=True)
+    content_text: Mapped[str | None] = mapped_column(PortableLongText(), nullable=True)
+    remote_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    business_key: Mapped[str | None] = mapped_column(String(512), nullable=True)
+    remote_refs_json: Mapped[list | None] = mapped_column(PortableJSON(), default=list, nullable=True)
+    status: Mapped[str] = mapped_column(String(32), default="staged", index=True)
+    metadata_json: Mapped[dict | None] = mapped_column(PortableJSON(), default=dict, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+    updated_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now, onupdate=_now)
+
+
+class SkillKnowledgeReviewRow(Base):
+    __tablename__ = "skill_knowledge_reviews"
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    job_id: Mapped[str] = mapped_column(String(36), index=True)
+    item_id: Mapped[str] = mapped_column(String(36), index=True)
+    binding_id: Mapped[str] = mapped_column(String(36), index=True)
+    snapshot_id: Mapped[str] = mapped_column(String(36), index=True)
+    object_type: Mapped[str] = mapped_column(String(32), index=True)
+    object_key: Mapped[str] = mapped_column(String(128))
+    original_json: Mapped[dict | None] = mapped_column(PortableJSON(), default=dict, nullable=True)
+    edited_json: Mapped[dict | None] = mapped_column(PortableJSON(), nullable=True)
+    status: Mapped[str] = mapped_column(String(24), default="pending", index=True)
+    reason: Mapped[str | None] = mapped_column(String(1024), nullable=True)
+    reviewed_by: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    reviewed_at: Mapped[datetime | None] = mapped_column(BeijingDateTime(), nullable=True)
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+
+
+class SkillKnowledgeEntityRow(Base):
+    __tablename__ = "skill_knowledge_entities"
+    __table_args__ = (UniqueConstraint("target_type", "target_id", "entity_type", "normalized_name", name="uq_skill_knowledge_entity"),)
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    target_type: Mapped[str] = mapped_column(String(32), index=True)
+    target_id: Mapped[str] = mapped_column(String(128), index=True)
+    entity_type: Mapped[str] = mapped_column(String(64), index=True)
+    normalized_name: Mapped[str] = mapped_column(String(512))
+    display_name: Mapped[str] = mapped_column(String(512))
+    aliases_json: Mapped[list | None] = mapped_column(PortableJSON(), default=list, nullable=True)
+    attributes_json: Mapped[dict | None] = mapped_column(PortableJSON(), default=dict, nullable=True)
+    status: Mapped[str] = mapped_column(String(24), default="active")
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+    updated_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now, onupdate=_now)
+
+
+class SkillKnowledgeEntityContributionRow(Base):
+    __tablename__ = "skill_knowledge_entity_contributions"
+    __table_args__ = (UniqueConstraint("entity_id", "binding_id", "snapshot_id", "local_entity_id", name="uq_skill_entity_contribution"),)
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    entity_id: Mapped[str] = mapped_column(String(36), index=True)
+    binding_id: Mapped[str] = mapped_column(String(36), index=True)
+    snapshot_id: Mapped[str] = mapped_column(String(36), index=True)
+    local_entity_id: Mapped[str] = mapped_column(String(128))
+    evidence_json: Mapped[list | None] = mapped_column(PortableJSON(), default=list, nullable=True)
+    attributes_json: Mapped[dict | None] = mapped_column(PortableJSON(), default=dict, nullable=True)
+    confidence: Mapped[float] = mapped_column(Float, default=1.0)
+
+
+class SkillKnowledgeRelationRow(Base):
+    __tablename__ = "skill_knowledge_relations"
+    __table_args__ = (UniqueConstraint("target_type", "target_id", "source_entity_id", "predicate", "target_entity_id", name="uq_skill_knowledge_relation"),)
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    target_type: Mapped[str] = mapped_column(String(32), index=True)
+    target_id: Mapped[str] = mapped_column(String(128), index=True)
+    source_entity_id: Mapped[str] = mapped_column(String(36), index=True)
+    predicate: Mapped[str] = mapped_column(String(128), index=True)
+    target_entity_id: Mapped[str] = mapped_column(String(36), index=True)
+    description: Mapped[str] = mapped_column(String(2048), default="")
+    status: Mapped[str] = mapped_column(String(24), default="active")
+    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now)
+    updated_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=_now, onupdate=_now)
+
+
+class SkillKnowledgeRelationContributionRow(Base):
+    __tablename__ = "skill_knowledge_relation_contributions"
+    __table_args__ = (UniqueConstraint("relation_id", "binding_id", "snapshot_id", "local_relation_id", name="uq_skill_relation_contribution"),)
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True)
+    relation_id: Mapped[str] = mapped_column(String(36), index=True)
+    binding_id: Mapped[str] = mapped_column(String(36), index=True)
+    snapshot_id: Mapped[str] = mapped_column(String(36), index=True)
+    local_relation_id: Mapped[str] = mapped_column(String(128))
+    evidence_json: Mapped[list | None] = mapped_column(PortableJSON(), default=list, nullable=True)
+    confidence: Mapped[float] = mapped_column(Float, default=1.0)

offline-backend-20260512/backend/packages/harness/deerflow/persistence/skill_knowledge/store.py

@@ -0,0 +1,758 @@
+"""SQL repository for skill knowledge jobs, snapshots and contributions."""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime, timedelta
+from typing import Any
+from uuid import uuid4
+
+from sqlalchemy import delete, func, or_, select, update
+from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
+
+from deerflow.persistence.skill_knowledge.model import (
+    SkillKnowledgeArtifactRow,
+    SkillKnowledgeBindingRow,
+    SkillKnowledgeEntityContributionRow,
+    SkillKnowledgeEntityRow,
+    SkillKnowledgeRelationContributionRow,
+    SkillKnowledgeRelationRow,
+    SkillKnowledgeReviewRow,
+    SkillKnowledgeSnapshotRow,
+    SkillKnowledgeSyncItemRow,
+    SkillKnowledgeSyncJobRow,
+)
+from deerflow.skill_knowledge.merge import normalize_name, normalize_predicate
+
+
+def _now() -> datetime:
+    return datetime.now(UTC)
+
+
+def _dict(row: Any) -> dict[str, Any]:
+    list_json_fields = {
+        "aliases_json",
+        "evidence_json",
+        "manifest_json",
+        "refs_json",
+        "targets_json",
+    }
+    data = row.to_dict()
+    for key, value in tuple(data.items()):
+        if isinstance(value, datetime):
+            data[key] = value.isoformat()
+        elif key.endswith("_json"):
+            data[key[:-5]] = data.pop(key) or ([] if key in list_json_fields else {})
+    return data
+
+
+class SkillKnowledgeRepository:
+    def __init__(self, session_factory: async_sessionmaker[AsyncSession]) -> None:
+        self._sf = session_factory
+
+    async def _retire_target_orphans(self, session: AsyncSession, *, target_type: str, target_id: str) -> None:
+        entities = (
+            await session.execute(
+                select(SkillKnowledgeEntityRow).where(
+                    SkillKnowledgeEntityRow.target_type == target_type,
+                    SkillKnowledgeEntityRow.target_id == target_id,
+                )
+            )
+        ).scalars()
+        for entity in entities:
+            contribution = (await session.execute(select(SkillKnowledgeEntityContributionRow.id).where(SkillKnowledgeEntityContributionRow.entity_id == entity.id).limit(1))).scalar_one_or_none()
+            entity.status = "active" if contribution else "deleted"
+        relations = (
+            await session.execute(
+                select(SkillKnowledgeRelationRow).where(
+                    SkillKnowledgeRelationRow.target_type == target_type,
+                    SkillKnowledgeRelationRow.target_id == target_id,
+                )
+            )
+        ).scalars()
+        for relation in relations:
+            contribution = (await session.execute(select(SkillKnowledgeRelationContributionRow.id).where(SkillKnowledgeRelationContributionRow.relation_id == relation.id).limit(1))).scalar_one_or_none()
+            relation.status = "active" if contribution else "deleted"
+
+    async def create_job(
+        self,
+        *,
+        skill_names: list[str],
+        targets: list[dict[str, Any]],
+        created_by: str | None,
+        force: bool = False,
+        trigger: str = "manual",
+        review_mode: str = "auto_high_confidence",
+    ) -> dict[str, Any]:
+        names = list(dict.fromkeys(name.strip() for name in skill_names if name.strip()))
+        normalized_targets: list[dict[str, Any]] = []
+        seen_targets: set[tuple[str, str]] = set()
+        for target in targets:
+            target_type = str(target.get("target_type") or "").strip()
+            target_id = str(target.get("target_id") or "").strip()
+            if not target_type or not target_id or (target_type, target_id) in seen_targets:
+                continue
+            seen_targets.add((target_type, target_id))
+            normalized_targets.append(
+                {
+                    "target_type": target_type,
+                    "target_mode": str(target.get("target_mode") or ("wiki" if target_type == "weknora" else "assistant")),
+                    "target_id": target_id,
+                    "target_name": str(target.get("target_name") or target_id),
+                    "remote_business_key": target.get("remote_business_key"),
+                    "readonly": bool(target.get("readonly", False)),
+                }
+            )
+        job_id = str(uuid4())
+        now = _now()
+        async with self._sf() as session:
+            job = SkillKnowledgeSyncJobRow(
+                id=job_id,
+                trigger=trigger,
+                target_count=len(normalized_targets),
+                targets_json=normalized_targets,
+                status="queued",
+                total_count=0,
+                review_mode=review_mode,
+                created_by=created_by,
+                created_at=now,
+            )
+            session.add(job)
+            for skill_name in names:
+                for target in normalized_targets:
+                    stmt = select(SkillKnowledgeBindingRow).where(
+                        SkillKnowledgeBindingRow.skill_name == skill_name,
+                        SkillKnowledgeBindingRow.target_type == target["target_type"],
+                        SkillKnowledgeBindingRow.target_id == target["target_id"],
+                    )
+                    binding = (await session.execute(stmt)).scalar_one_or_none()
+                    if binding is None:
+                        binding = SkillKnowledgeBindingRow(
+                            id=str(uuid4()),
+                            skill_name=skill_name,
+                            target_type=target["target_type"],
+                            target_mode=target["target_mode"],
+                            target_id=target["target_id"],
+                            target_name=target["target_name"],
+                            remote_business_key=target.get("remote_business_key"),
+                            status="queued",
+                            created_by=created_by,
+                            created_at=now,
+                            updated_at=now,
+                        )
+                        session.add(binding)
+                    else:
+                        active_item = (
+                            await session.execute(
+                                select(SkillKnowledgeSyncItemRow.id)
+                                .where(
+                                    SkillKnowledgeSyncItemRow.binding_id == binding.id,
+                                    SkillKnowledgeSyncItemRow.status.in_(["queued", "running", "awaiting_review"]),
+                                )
+                                .limit(1)
+                            )
+                        ).scalar_one_or_none()
+                        if active_item is not None:
+                            continue
+                        binding.target_mode = target["target_mode"]
+                        binding.target_name = target["target_name"]
+                        binding.remote_business_key = target.get("remote_business_key")
+                        binding.status = "queued"
+                        binding.active = True
+                        binding.updated_at = now
+                    item = SkillKnowledgeSyncItemRow(
+                        id=str(uuid4()),
+                        job_id=job_id,
+                        binding_id=binding.id,
+                        target_type=target["target_type"],
+                        target_mode=target["target_mode"],
+                        target_id=target["target_id"],
+                        skill_name=skill_name,
+                        status="queued",
+                        phase="scanning",
+                        progress=0,
+                        active_dedupe_key=binding.id,
+                        created_at=now,
+                        updated_at=now,
+                    )
+                    session.add(item)
+                    binding.last_job_id = job_id
+                    binding.last_item_id = item.id
+                    job.total_count += 1
+            if job.total_count == 0:
+                job.status = "completed"
+                job.completed_at = now
+            await session.flush()
+            result = _dict(job)
+            result["force"] = force
+            await session.commit()
+        return result
+
+    async def list_bindings(self, *, skill_names: list[str] | None = None, target_id: str | None = None, include_detached: bool = False) -> list[dict[str, Any]]:
+        stmt = select(SkillKnowledgeBindingRow)
+        if skill_names:
+            stmt = stmt.where(SkillKnowledgeBindingRow.skill_name.in_(skill_names))
+        if target_id:
+            stmt = stmt.where(SkillKnowledgeBindingRow.target_id == target_id)
+        if not include_detached:
+            stmt = stmt.where(SkillKnowledgeBindingRow.active.is_(True))
+        stmt = stmt.order_by(SkillKnowledgeBindingRow.updated_at.desc())
+        async with self._sf() as session:
+            return [_dict(row) for row in (await session.execute(stmt)).scalars()]
+
+    async def get_binding(self, binding_id: str) -> dict[str, Any] | None:
+        async with self._sf() as session:
+            row = await session.get(SkillKnowledgeBindingRow, binding_id)
+            return _dict(row) if row else None
+
+    async def list_artifacts(self, binding_id: str, *, active_only: bool = True) -> list[dict[str, Any]]:
+        stmt = select(SkillKnowledgeArtifactRow).where(SkillKnowledgeArtifactRow.binding_id == binding_id)
+        if active_only:
+            stmt = stmt.where(SkillKnowledgeArtifactRow.status == "active")
+        async with self._sf() as session:
+            return [_dict(row) for row in (await session.execute(stmt)).scalars()]
+
+    async def has_active_item(self, binding_id: str) -> bool:
+        async with self._sf() as session:
+            value = (
+                await session.execute(
+                    select(SkillKnowledgeSyncItemRow.id)
+                    .where(
+                        SkillKnowledgeSyncItemRow.binding_id == binding_id,
+                        SkillKnowledgeSyncItemRow.status.in_(["queued", "running", "awaiting_review"]),
+                    )
+                    .limit(1)
+                )
+            ).scalar_one_or_none()
+            return value is not None
+
+    async def set_binding_auto_sync(self, binding_id: str, *, enabled: bool) -> dict[str, Any] | None:
+        async with self._sf() as session:
+            row = await session.get(SkillKnowledgeBindingRow, binding_id)
+            if row is None or not row.active:
+                return None
+            row.auto_sync_enabled = enabled
+            row.updated_at = _now()
+            await session.flush()
+            data = _dict(row)
+            await session.commit()
+            return data
+
+    async def get_job(self, job_id: str) -> dict[str, Any] | None:
+        async with self._sf() as session:
+            job = await session.get(SkillKnowledgeSyncJobRow, job_id)
+            if job is None:
+                return None
+            items = (await session.execute(select(SkillKnowledgeSyncItemRow).where(SkillKnowledgeSyncItemRow.job_id == job_id).order_by(SkillKnowledgeSyncItemRow.created_at))).scalars()
+            result = _dict(job)
+            result["items"] = [_dict(item) for item in items]
+            return result
+
+    async def list_jobs(self, *, limit: int = 50) -> list[dict[str, Any]]:
+        stmt = select(SkillKnowledgeSyncJobRow).order_by(SkillKnowledgeSyncJobRow.created_at.desc()).limit(limit)
+        async with self._sf() as session:
+            return [_dict(row) for row in (await session.execute(stmt)).scalars()]
+
+    async def list_queued_items(self, *, limit: int = 20) -> list[dict[str, Any]]:
+        now = _now()
+        stmt = (
+            select(SkillKnowledgeSyncItemRow)
+            .where(
+                or_(
+                    SkillKnowledgeSyncItemRow.status == "queued",
+                    ((SkillKnowledgeSyncItemRow.status == "running") & (SkillKnowledgeSyncItemRow.lease_until < now)),
+                )
+            )
+            .order_by(SkillKnowledgeSyncItemRow.created_at)
+            .limit(limit)
+        )
+        async with self._sf() as session:
+            return [_dict(row) for row in (await session.execute(stmt)).scalars()]
+
+    async def claim_item(
+        self,
+        item_id: str,
+        *,
+        owner: str,
+        lease_seconds: int = 900,
+    ) -> dict[str, Any] | None:
+        """Atomically claim a queued item, or reclaim one whose worker lease expired."""
+        now = _now()
+        stmt = (
+            update(SkillKnowledgeSyncItemRow)
+            .where(
+                SkillKnowledgeSyncItemRow.id == item_id,
+                or_(
+                    SkillKnowledgeSyncItemRow.status == "queued",
+                    ((SkillKnowledgeSyncItemRow.status == "running") & (SkillKnowledgeSyncItemRow.lease_until < now)),
+                ),
+            )
+            .values(
+                status="running",
+                started_at=func.coalesce(SkillKnowledgeSyncItemRow.started_at, now),
+                attempts=SkillKnowledgeSyncItemRow.attempts + 1,
+                lease_owner=owner,
+                lease_until=now + timedelta(seconds=lease_seconds),
+                updated_at=now,
+            )
+        )
+        async with self._sf() as session:
+            result = await session.execute(stmt)
+            if result.rowcount != 1:
+                await session.rollback()
+                return None
+            row = await session.get(SkillKnowledgeSyncItemRow, item_id)
+            data = _dict(row)
+            await session.commit()
+        await self.refresh_job(data["job_id"])
+        return data
+
+    async def renew_lease(self, item_id: str, *, owner: str, lease_seconds: int = 900) -> bool:
+        now = _now()
+        async with self._sf() as session:
+            result = await session.execute(
+                update(SkillKnowledgeSyncItemRow)
+                .where(
+                    SkillKnowledgeSyncItemRow.id == item_id,
+                    SkillKnowledgeSyncItemRow.status == "running",
+                    SkillKnowledgeSyncItemRow.lease_owner == owner,
+                )
+                .values(
+                    lease_until=now + timedelta(seconds=lease_seconds),
+                    updated_at=now,
+                )
+            )
+            await session.commit()
+            return result.rowcount == 1
+
+    async def update_item(
+        self,
+        item_id: str,
+        *,
+        status: str | None = None,
+        phase: str | None = None,
+        progress: int | None = None,
+        snapshot_id: str | None = None,
+        error_code: str | None = None,
+        error_message: str | None = None,
+    ) -> dict[str, Any] | None:
+        async with self._sf() as session:
+            row = await session.get(SkillKnowledgeSyncItemRow, item_id)
+            if row is None:
+                return None
+            if status is not None:
+                row.status = status
+                if status == "running" and row.started_at is None:
+                    row.started_at = _now()
+                    row.attempts += 1
+                if status in {"completed", "partial_failed", "failed", "canceled"}:
+                    row.completed_at = _now()
+                    row.active_dedupe_key = None
+                elif status == "queued":
+                    row.completed_at = None
+                if status != "running":
+                    row.lease_owner = None
+                    row.lease_until = None
+            if phase is not None:
+                row.phase = phase
+            if progress is not None:
+                row.progress = max(0, min(100, progress))
+            if snapshot_id is not None:
+                row.snapshot_id = snapshot_id
+            row.last_error_code = error_code
+            row.last_error_message = error_message
+            row.updated_at = _now()
+            await session.flush()
+            data = _dict(row)
+            await session.commit()
+        await self.refresh_job(data["job_id"])
+        return data
+
+    async def refresh_job(self, job_id: str) -> None:
+        async with self._sf() as session:
+            job = await session.get(SkillKnowledgeSyncJobRow, job_id)
+            if job is None:
+                return
+            rows = (await session.execute(select(SkillKnowledgeSyncItemRow.status, func.count()).where(SkillKnowledgeSyncItemRow.job_id == job_id).group_by(SkillKnowledgeSyncItemRow.status))).all()
+            counts = {str(status): int(count) for status, count in rows}
+            job.success_count = counts.get("completed", 0)
+            job.partial_failed_count = counts.get("partial_failed", 0)
+            job.failed_count = counts.get("failed", 0)
+            active = sum(counts.get(key, 0) for key in ("queued", "running", "awaiting_review"))
+            if counts.get("running", 0) or counts.get("awaiting_review", 0):
+                job.status = "running"
+                job.started_at = job.started_at or _now()
+            elif active == 0 and sum(counts.values()) >= job.total_count:
+                if job.failed_count and job.success_count == 0 and job.partial_failed_count == 0:
+                    job.status = "failed"
+                elif job.failed_count or job.partial_failed_count:
+                    job.status = "partial_failed"
+                else:
+                    job.status = "completed"
+                job.completed_at = _now()
+            await session.commit()
+
+    async def cancel_job(self, job_id: str) -> bool:
+        async with self._sf() as session:
+            job = await session.get(SkillKnowledgeSyncJobRow, job_id)
+            if job is None:
+                return False
+            job.cancel_requested = True
+            await session.execute(
+                SkillKnowledgeSyncItemRow.__table__.update()
+                .where(
+                    SkillKnowledgeSyncItemRow.job_id == job_id,
+                    SkillKnowledgeSyncItemRow.status.in_(["queued", "awaiting_review"]),
+                )
+                .values(
+                    status="canceled",
+                    phase="completed",
+                    completed_at=_now(),
+                    progress=100,
+                    active_dedupe_key=None,
+                    lease_owner=None,
+                    lease_until=None,
+                )
+            )
+            job.status = "canceled"
+            job.completed_at = _now()
+            await session.commit()
+            return True
+
+    async def requeue_item(self, item_id: str) -> dict[str, Any] | None:
+        async with self._sf() as session:
+            row = await session.get(SkillKnowledgeSyncItemRow, item_id)
+            if row is None:
+                return None
+            active_item = (
+                await session.execute(
+                    select(SkillKnowledgeSyncItemRow.id)
+                    .where(
+                        SkillKnowledgeSyncItemRow.binding_id == row.binding_id,
+                        SkillKnowledgeSyncItemRow.id != row.id,
+                        SkillKnowledgeSyncItemRow.status.in_(["queued", "running", "awaiting_review"]),
+                    )
+                    .limit(1)
+                )
+            ).scalar_one_or_none()
+            if active_item is not None:
+                return None
+            row.status = "queued"
+            row.phase = "scanning"
+            row.progress = 0
+            row.completed_at = None
+            row.active_dedupe_key = row.binding_id
+            row.updated_at = _now()
+            await session.flush()
+            data = _dict(row)
+            await session.commit()
+        await self.refresh_job(data["job_id"])
+        return data
+
+    async def get_or_create_snapshot(self, *, skill_name: str, source_digest: str, extractor_version: str, prompt_hash: str, model_name: str, manifest: list, ir: dict, counts: dict) -> dict[str, Any]:
+        stmt = select(SkillKnowledgeSnapshotRow).where(
+            SkillKnowledgeSnapshotRow.skill_name == skill_name,
+            SkillKnowledgeSnapshotRow.source_digest == source_digest,
+            SkillKnowledgeSnapshotRow.extractor_version == extractor_version,
+            SkillKnowledgeSnapshotRow.prompt_hash == prompt_hash,
+            SkillKnowledgeSnapshotRow.model_name == model_name,
+        )
+        async with self._sf() as session:
+            row = (await session.execute(stmt)).scalar_one_or_none()
+            if row is None:
+                row = SkillKnowledgeSnapshotRow(
+                    id=str(uuid4()),
+                    skill_name=skill_name,
+                    source_digest=source_digest,
+                    extractor_version=extractor_version,
+                    prompt_hash=prompt_hash,
+                    model_name=model_name,
+                    manifest_json=manifest,
+                    ir_json=ir,
+                    counts_json=counts,
+                    status="ready",
+                    created_at=_now(),
+                )
+                session.add(row)
+                await session.flush()
+            data = _dict(row)
+            await session.commit()
+            return data
+
+    async def get_snapshot(self, snapshot_id: str) -> dict[str, Any] | None:
+        async with self._sf() as session:
+            row = await session.get(SkillKnowledgeSnapshotRow, snapshot_id)
+            return _dict(row) if row else None
+
+    async def create_reviews(self, *, job_id: str, item_id: str, binding_id: str, snapshot_id: str, objects: list[tuple[str, dict]]) -> int:
+        rows = [
+            SkillKnowledgeReviewRow(
+                id=str(uuid4()),
+                job_id=job_id,
+                item_id=item_id,
+                binding_id=binding_id,
+                snapshot_id=snapshot_id,
+                object_type=kind,
+                object_key=str(obj.get("local_id") or obj.get("slug") or uuid4()),
+                original_json=obj,
+                status="pending",
+                created_at=_now(),
+            )
+            for kind, obj in objects
+        ]
+        async with self._sf() as session:
+            session.add_all(rows)
+            await session.commit()
+        return len(rows)
+
+    async def list_reviews(self, *, job_id: str | None = None, item_id: str | None = None, status: str | None = "pending") -> list[dict[str, Any]]:
+        stmt = select(SkillKnowledgeReviewRow)
+        if job_id:
+            stmt = stmt.where(SkillKnowledgeReviewRow.job_id == job_id)
+        if item_id:
+            stmt = stmt.where(SkillKnowledgeReviewRow.item_id == item_id)
+        if status:
+            stmt = stmt.where(SkillKnowledgeReviewRow.status == status)
+        stmt = stmt.order_by(SkillKnowledgeReviewRow.created_at)
+        async with self._sf() as session:
+            return [_dict(row) for row in (await session.execute(stmt)).scalars()]
+
+    async def review(self, review_ids: list[str], *, action: str, actor: str | None, edits: dict[str, dict] | None = None, reason: str | None = None) -> list[str]:
+        final_status = "approved" if action == "approve" else "rejected"
+        affected_items: set[str] = set()
+        async with self._sf() as session:
+            rows = (await session.execute(select(SkillKnowledgeReviewRow).where(SkillKnowledgeReviewRow.id.in_(review_ids)))).scalars()
+            for row in rows:
+                row.status = final_status
+                row.edited_json = (edits or {}).get(row.id)
+                row.reason = reason
+                row.reviewed_by = actor
+                row.reviewed_at = _now()
+                affected_items.add(row.item_id)
+            await session.commit()
+        return sorted(affected_items)
+
+    async def pending_review_count(self, item_id: str) -> int:
+        stmt = select(func.count()).select_from(SkillKnowledgeReviewRow).where(SkillKnowledgeReviewRow.item_id == item_id, SkillKnowledgeReviewRow.status == "pending")
+        async with self._sf() as session:
+            return int((await session.execute(stmt)).scalar_one())
+
+    async def effective_ir(self, snapshot_id: str, item_id: str) -> dict[str, Any] | None:
+        snapshot = await self.get_snapshot(snapshot_id)
+        if snapshot is None:
+            return None
+        ir = dict(snapshot.get("ir") or {})
+        reviews = await self.list_reviews(item_id=item_id, status=None)
+        if not reviews:
+            return ir
+        by_key = {(row["object_type"], row["object_key"]): row for row in reviews}
+        for key in ("entities", "relations"):
+            kind = key[:-1]
+            accepted = []
+            for obj in ir.get(key, []):
+                review = by_key.get((kind, str(obj.get("local_id"))))
+                if review and review.get("status") == "rejected":
+                    continue
+                accepted.append((review or {}).get("edited") or obj)
+            ir[key] = accepted
+        return ir
+
+    async def stage_and_activate(self, *, item_id: str, snapshot_id: str, ir: dict[str, Any], artifacts: list[dict[str, Any]], status: str = "synced") -> dict[str, Any]:
+        now = _now()
+        async with self._sf() as session:
+            item = await session.get(SkillKnowledgeSyncItemRow, item_id)
+            if item is None:
+                raise KeyError(item_id)
+            binding = await session.get(SkillKnowledgeBindingRow, item.binding_id)
+            if binding is None:
+                raise KeyError(item.binding_id)
+            if not binding.active:
+                raise RuntimeError("Binding was detached before staged activation")
+            await session.execute(
+                SkillKnowledgeArtifactRow.__table__.update()
+                .where(
+                    SkillKnowledgeArtifactRow.binding_id == binding.id,
+                    SkillKnowledgeArtifactRow.status == "active",
+                )
+                .values(status="retired", updated_at=now)
+            )
+            for artifact in artifacts:
+                session.add(
+                    SkillKnowledgeArtifactRow(
+                        id=str(uuid4()),
+                        binding_id=binding.id,
+                        snapshot_id=snapshot_id,
+                        kind=str(artifact.get("kind") or "wiki_page"),
+                        slug=artifact.get("slug"),
+                        content_text=artifact.get("content"),
+                        remote_id=artifact.get("remote_id"),
+                        business_key=artifact.get("business_key"),
+                        remote_refs_json=artifact.get("remote_refs") or [],
+                        status="active",
+                        metadata_json=artifact.get("metadata") or {},
+                        created_at=now,
+                        updated_at=now,
+                    )
+                )
+            # Retire contributions from the old active version only after all staged artifacts are ready.
+            await session.execute(delete(SkillKnowledgeEntityContributionRow).where(SkillKnowledgeEntityContributionRow.binding_id == binding.id))
+            await session.execute(delete(SkillKnowledgeRelationContributionRow).where(SkillKnowledgeRelationContributionRow.binding_id == binding.id))
+            entity_ids: dict[str, str] = {}
+            for entity in ir.get("entities", []):
+                normalized = normalize_name(str(entity.get("name") or ""))
+                if not normalized:
+                    continue
+                entity_type = str(entity.get("type") or "concept")
+                stmt = select(SkillKnowledgeEntityRow).where(
+                    SkillKnowledgeEntityRow.target_type == binding.target_type,
+                    SkillKnowledgeEntityRow.target_id == binding.target_id,
+                    SkillKnowledgeEntityRow.entity_type == entity_type,
+                    SkillKnowledgeEntityRow.normalized_name == normalized,
+                )
+                canonical = (await session.execute(stmt)).scalar_one_or_none()
+                if canonical is None:
+                    canonical = SkillKnowledgeEntityRow(
+                        id=str(uuid4()),
+                        target_type=binding.target_type,
+                        target_id=binding.target_id,
+                        entity_type=entity_type,
+                        normalized_name=normalized,
+                        display_name=str(entity.get("name") or normalized),
+                        aliases_json=entity.get("aliases") or [],
+                        attributes_json=entity.get("attributes") or {},
+                        status="active",
+                        created_at=now,
+                        updated_at=now,
+                    )
+                    session.add(canonical)
+                    await session.flush()
+                else:
+                    canonical.status = "active"
+                entity_ids[str(entity.get("local_id"))] = canonical.id
+                session.add(
+                    SkillKnowledgeEntityContributionRow(
+                        id=str(uuid4()),
+                        entity_id=canonical.id,
+                        binding_id=binding.id,
+                        snapshot_id=snapshot_id,
+                        local_entity_id=str(entity.get("local_id")),
+                        evidence_json=entity.get("evidence") or [],
+                        attributes_json=entity.get("attributes") or {},
+                        confidence=float(entity.get("confidence") or 0),
+                    )
+                )
+            for relation in ir.get("relations", []):
+                source_id = entity_ids.get(str(relation.get("source_local_id")))
+                target_id = entity_ids.get(str(relation.get("target_local_id")))
+                confidence = float(relation.get("confidence") or 0)
+                if not source_id or not target_id or confidence < 0.75 or not relation.get("evidence"):
+                    continue
+                predicate = normalize_predicate(str(relation.get("predicate") or "related_to"))
+                stmt = select(SkillKnowledgeRelationRow).where(
+                    SkillKnowledgeRelationRow.target_type == binding.target_type,
+                    SkillKnowledgeRelationRow.target_id == binding.target_id,
+                    SkillKnowledgeRelationRow.source_entity_id == source_id,
+                    SkillKnowledgeRelationRow.predicate == predicate,
+                    SkillKnowledgeRelationRow.target_entity_id == target_id,
+                )
+                canonical = (await session.execute(stmt)).scalar_one_or_none()
+                if canonical is None:
+                    canonical = SkillKnowledgeRelationRow(
+                        id=str(uuid4()),
+                        target_type=binding.target_type,
+                        target_id=binding.target_id,
+                        source_entity_id=source_id,
+                        predicate=predicate,
+                        target_entity_id=target_id,
+                        description=str(relation.get("description") or ""),
+                        status="active",
+                        created_at=now,
+                        updated_at=now,
+                    )
+                    session.add(canonical)
+                    await session.flush()
+                else:
+                    canonical.status = "active"
+                session.add(
+                    SkillKnowledgeRelationContributionRow(
+                        id=str(uuid4()),
+                        relation_id=canonical.id,
+                        binding_id=binding.id,
+                        snapshot_id=snapshot_id,
+                        local_relation_id=str(relation.get("local_id")),
+                        evidence_json=relation.get("evidence") or [],
+                        confidence=confidence,
+                    )
+                )
+            await self._retire_target_orphans(
+                session,
+                target_type=binding.target_type,
+                target_id=binding.target_id,
+            )
+            binding.current_snapshot_id = snapshot_id
+            binding.source_digest = str(ir.get("skill", {}).get("source_digest") or "")
+            binding.synced_digest = binding.source_digest
+            binding.status = status
+            binding.last_synced_at = now
+            binding.last_error_code = None
+            binding.last_error_message = None
+            binding.updated_at = now
+            item.snapshot_id = snapshot_id
+            item.status = "completed" if status in {"synced", "pending_remote_write"} else status
+            item.phase = "completed"
+            item.progress = 100
+            item.completed_at = now
+            item.active_dedupe_key = None
+            item.lease_owner = None
+            item.lease_until = None
+            item.updated_at = now
+            await session.flush()
+            result = _dict(binding)
+            await session.commit()
+        await self.refresh_job(item.job_id)
+        return result
+
+    async def detach_binding(
+        self,
+        binding_id: str,
+        *,
+        delete_contributions: bool,
+        cleanup_failed: bool = False,
+    ) -> dict[str, Any] | None:
+        async with self._sf() as session:
+            binding = await session.get(SkillKnowledgeBindingRow, binding_id)
+            if binding is None:
+                return None
+            binding.active = False
+            binding.status = "detached_delete_pending_cleanup" if delete_contributions and cleanup_failed else "detached_deleted" if delete_contributions else "detached_keep"
+            binding.updated_at = _now()
+            if delete_contributions:
+                await session.execute(delete(SkillKnowledgeEntityContributionRow).where(SkillKnowledgeEntityContributionRow.binding_id == binding_id))
+                await session.execute(delete(SkillKnowledgeRelationContributionRow).where(SkillKnowledgeRelationContributionRow.binding_id == binding_id))
+                await session.execute(SkillKnowledgeArtifactRow.__table__.update().where(SkillKnowledgeArtifactRow.binding_id == binding_id).values(status="deleted", updated_at=_now()))
+                await self._retire_target_orphans(
+                    session,
+                    target_type=binding.target_type,
+                    target_id=binding.target_id,
+                )
+            await session.flush()
+            data = _dict(binding)
+            await session.commit()
+            return data
+
+    async def mark_skill_stale(self, skill_name: str) -> int:
+        async with self._sf() as session:
+            result = await session.execute(
+                SkillKnowledgeBindingRow.__table__.update()
+                .where(
+                    SkillKnowledgeBindingRow.skill_name == skill_name,
+                    SkillKnowledgeBindingRow.active.is_(True),
+                    SkillKnowledgeBindingRow.status.in_(["synced", "partial_failed", "pending_remote_write"]),
+                )
+                .values(status="stale", updated_at=_now())
+            )
+            await session.commit()
+            return int(result.rowcount or 0)
+
+
+def make_skill_knowledge_store(session_factory):
+    return SkillKnowledgeRepository(session_factory) if session_factory is not None else None

offline-backend-20260512/backend/packages/harness/deerflow/persistence/types.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import json
-from datetime import datetime, timedelta, timezone
+from datetime import UTC, datetime, timedelta, timezone
 from typing import Any
 
 from sqlalchemy import JSON, DateTime, Text
@@ -14,6 +14,12 @@ from sqlalchemy.types import TypeDecorator
 # fixed offset is exact and avoids depending on the `tzdata` package (which the
 # stdlib `zoneinfo` needs on Windows / minimal containers).
 BEIJING_TZ = timezone(timedelta(hours=8))
+_POSTGRES_FAMILY_DIALECTS = frozenset({"postgresql", "opengauss"})
+
+
+def _is_postgres_family(dialect) -> bool:
+    """Return whether *dialect* preserves timezone-aware timestamps natively."""
+    return dialect.name in _POSTGRES_FAMILY_DIALECTS
 
 
 class PortableJSON(TypeDecorator):
@@ -87,17 +93,17 @@ class BeijingDateTime(TypeDecorator):
     def process_bind_param(self, value: Any, dialect) -> Any:
         if not isinstance(value, datetime):
             return value
-        if dialect.name == "postgresql":
+        if _is_postgres_family(dialect):
             return value
         if value.tzinfo is None:
             # Naive values follow the codebase convention of being UTC.
-            value = value.replace(tzinfo=timezone.utc)
+            value = value.replace(tzinfo=UTC)
         return value.astimezone(BEIJING_TZ).replace(tzinfo=None)
 
     def process_result_value(self, value: Any, dialect) -> Any:
         if not isinstance(value, datetime):
             return value
-        if dialect.name == "postgresql":
+        if _is_postgres_family(dialect):
             return value
         if value.tzinfo is None:
             # Stored as naive Beijing wall-clock; re-attach the zone.

offline-backend-20260512/backend/packages/harness/deerflow/skill_knowledge/__init__.py

@@ -0,0 +1,10 @@
+"""Skill knowledge distillation primitives.
+
+The package is deliberately independent from ``app.*``. Gateway composition
+injects persistence and remote adapters at runtime.
+"""
+
+from deerflow.skill_knowledge.extractor import build_skill_ir
+from deerflow.skill_knowledge.scanner import ScanResult, enrich_converted_documents, scan_skill
+
+__all__ = ["ScanResult", "build_skill_ir", "enrich_converted_documents", "scan_skill"]

offline-backend-20260512/backend/packages/harness/deerflow/skill_knowledge/extractor.py

@@ -0,0 +1,152 @@
+"""IR construction and validation.
+
+V1 intentionally starts with deterministic, evidence-backed extraction. The
+schema is compatible with a future model-backed extractor, while never letting
+unvalidated model output flow directly into persistence or graph writers.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import re
+from typing import Any
+
+from deerflow.skill_knowledge.scanner import ScanResult
+
+EXTRACTOR_VERSION = "skill-knowledge-v1"
+PROMPT_HASH = f"sha256:{hashlib.sha256(b'skill-knowledge-v1-evidence-only').hexdigest()}"
+PREDICATE_PATTERNS = {
+    "depends_on": re.compile(r"(?:依赖|需要|requires?)\s*[`\"“]?([^，。；;\n`\"”]{2,80})", re.I),
+    "uses": re.compile(r"(?:调用|使用|uses?)\s*[`\"“]?([^，。；;\n`\"”]{2,80})", re.I),
+    "produces": re.compile(r"(?:生成|输出|produces?)\s*[`\"“]?([^，。；;\n`\"”]{2,80})", re.I),
+    "consumes": re.compile(r"(?:读取|输入|consumes?)\s*[`\"“]?([^，。；;\n`\"”]{2,80})", re.I),
+}
+
+
+def _evidence(path: str, text: str, start: int, end: int) -> dict[str, Any]:
+    line_start = text.count("\n", 0, start) + 1
+    line_end = text.count("\n", 0, end) + 1
+    quote = " ".join(text[start:end].split())[:240]
+    return {"file_path": path, "line_start": line_start, "line_end": line_end, "quote": quote}
+
+
+def build_skill_ir(scan: ScanResult, *, display_name: str | None = None, description: str = "") -> dict[str, Any]:
+    entities: list[dict[str, Any]] = [
+        {
+            "local_id": "ent_skill",
+            "type": "skill",
+            "name": display_name or scan.skill_name,
+            "aliases": [scan.skill_name] if display_name and display_name != scan.skill_name else [],
+            "description": description,
+            "attributes": {},
+            "evidence": [],
+            "confidence": 1.0,
+        }
+    ]
+    relations: list[dict[str, Any]] = []
+    by_key: dict[tuple[str, str], str] = {("skill", (display_name or scan.skill_name).casefold()): "ent_skill"}
+
+    for file in scan.files:
+        if not file.text:
+            continue
+        for predicate, pattern in PREDICATE_PATTERNS.items():
+            for match in pattern.finditer(file.text):
+                name = match.group(1).strip(" `\"'“”()[]")
+                if not name or len(name) > 80:
+                    continue
+                key = ("concept", name.casefold())
+                entity_id = by_key.get(key)
+                if entity_id is None:
+                    entity_id = f"ent_{len(entities):04d}"
+                    by_key[key] = entity_id
+                    entities.append(
+                        {
+                            "local_id": entity_id,
+                            "type": "concept",
+                            "name": name,
+                            "aliases": [],
+                            "description": "从技能业务说明中抽取的知识对象",
+                            "attributes": {},
+                            "evidence": [_evidence(file.path, file.text, match.start(), match.end())],
+                            "confidence": 0.82,
+                        }
+                    )
+                relations.append(
+                    {
+                        "local_id": f"rel_{len(relations):04d}",
+                        "source_local_id": "ent_skill",
+                        "predicate": predicate,
+                        "target_local_id": entity_id,
+                        "description": " ".join(match.group(0).split())[:240],
+                        "relation_source": "explicit_text",
+                        "evidence": [_evidence(file.path, file.text, match.start(), match.end())],
+                        "confidence": 0.82,
+                    }
+                )
+        for ref in file.references:
+            name = ref["target_path"]
+            key = ("file", name.casefold())
+            entity_id = by_key.get(key)
+            if entity_id is None:
+                entity_id = f"ent_{len(entities):04d}"
+                by_key[key] = entity_id
+                entities.append(
+                    {
+                        "local_id": entity_id,
+                        "type": "file",
+                        "name": name,
+                        "aliases": [],
+                        "description": "技能文档引用的资料文件",
+                        "attributes": {},
+                        "evidence": [{"file_path": file.path, "line_start": 1, "line_end": 1, "quote": name}],
+                        "confidence": 1.0,
+                    }
+                )
+            relations.append(
+                {
+                    "local_id": f"rel_{len(relations):04d}",
+                    "source_local_id": "ent_skill",
+                    "predicate": "references",
+                    "target_local_id": entity_id,
+                    "description": f"{file.path} 引用 {name}",
+                    "relation_source": "deterministic_structure",
+                    "evidence": [{"file_path": file.path, "line_start": 1, "line_end": 1, "quote": name}],
+                    "confidence": 1.0,
+                }
+            )
+
+    title = display_name or scan.skill_name
+    summaries = [f"- **{item.title}**（`{item.path}`）" for item in scan.files[:100]]
+    content = "\n".join(
+        [
+            f"# {title}",
+            "",
+            description or "由技能知识文件安全归纳生成。",
+            "",
+            "## 知识资料",
+            "",
+            *(summaries or ["- 暂无可归纳知识文件"]),
+            "",
+            f"> 源摘要：`{scan.source_digest}`；代码与脚本文件未读取。",
+        ]
+    )
+    return {
+        "extractor_version": EXTRACTOR_VERSION,
+        "prompt_hash": PROMPT_HASH,
+        "model_name": "deterministic-v1",
+        "skill": {
+            "name": scan.skill_name,
+            "display_name": title,
+            "description": description,
+            "source_root": scan.source_root,
+            "source_digest": scan.source_digest,
+        },
+        "files": scan.manifest,
+        "entities": entities,
+        "relations": relations,
+        "wiki": {
+            "skill_page": {"slug": f"skills/{scan.skill_name}/overview", "title": title, "content": content},
+            "pages": [],
+        },
+        "safety": {"blocked_files": scan.blocked_files},
+    }

offline-backend-20260512/backend/packages/harness/deerflow/skill_knowledge/graph_writer.py

@@ -0,0 +1,115 @@
+"""Guarded graph-write plans.
+
+No caller can submit Cypher. The writer accepts only normalized node/edge
+records and executes fixed parameterized templates when explicitly enabled.
+"""
+
+from __future__ import annotations
+
+import hashlib
+from dataclasses import dataclass
+from typing import Any, Protocol
+
+
+class GraphTransaction(Protocol):
+    async def run(self, query: str, parameters: dict[str, Any]) -> Any: ...
+
+
+@dataclass(frozen=True, slots=True)
+class GraphWritePlan:
+    scope: str
+    nodes: tuple[dict[str, Any], ...]
+    edges: tuple[dict[str, Any], ...]
+
+
+NODE_QUERY = """
+MERGE (n:DeerFlowKnowledge {scope: $scope, entity_key: $entity_key})
+SET n.display_name = $display_name, n.entity_type = $entity_type,
+    n.binding_id = $binding_id, n.snapshot_id = $snapshot_id, n.job_id = $job_id,
+    n.confidence = $confidence, n.updated_at = datetime()
+""".strip()
+EDGE_QUERY = """
+MATCH (a:DeerFlowKnowledge {scope: $scope, entity_key: $source_key})
+MATCH (b:DeerFlowKnowledge {scope: $scope, entity_key: $target_key})
+MERGE (a)-[r:DEERFLOW_RELATION {edge_key: $edge_key}]->(b)
+SET r.predicate = $predicate, r.binding_id = $binding_id,
+    r.snapshot_id = $snapshot_id, r.job_id = $job_id, r.confidence = $confidence,
+    r.evidence = $evidence, r.updated_at = datetime()
+""".strip()
+
+
+def _key(*parts: str) -> str:
+    return hashlib.sha256("\x1f".join(parts).encode()).hexdigest()
+
+
+def plan_from_ir(
+    ir: dict[str, Any],
+    *,
+    target_type: str,
+    target_id: str,
+    binding_id: str,
+    snapshot_id: str,
+    job_id: str,
+) -> GraphWritePlan:
+    """Build a stable write plan; callers cannot supply executable Cypher."""
+    scope = f"{target_type}:{target_id}"
+    local_keys: dict[str, str] = {}
+    nodes: list[dict[str, Any]] = []
+    for entity in ir.get("entities") or []:
+        entity_type = str(entity.get("type") or "concept")
+        normalized_name = str(entity.get("normalized_name") or entity.get("name") or "").strip().casefold()
+        if not normalized_name:
+            continue
+        entity_key = _key(entity_type, normalized_name)
+        local_keys[str(entity.get("local_id") or normalized_name)] = entity_key
+        nodes.append(
+            {
+                "entity_key": entity_key,
+                "display_name": str(entity.get("name") or normalized_name),
+                "entity_type": entity_type,
+                "binding_id": binding_id,
+                "snapshot_id": snapshot_id,
+                "job_id": job_id,
+                "confidence": float(entity.get("confidence") or 1.0),
+            }
+        )
+    edges: list[dict[str, Any]] = []
+    for relation in ir.get("relations") or []:
+        source_key = local_keys.get(str(relation.get("source_local_id") or ""))
+        target_key = local_keys.get(str(relation.get("target_local_id") or ""))
+        evidence = list(relation.get("evidence") or [])
+        confidence = float(relation.get("confidence") or 0)
+        predicate = str(relation.get("predicate") or "related_to")
+        if not source_key or not target_key or confidence < 0.75 or not evidence:
+            continue
+        edges.append(
+            {
+                "edge_key": _key(source_key, predicate, target_key, binding_id, snapshot_id),
+                "source_key": source_key,
+                "target_key": target_key,
+                "predicate": predicate,
+                "binding_id": binding_id,
+                "snapshot_id": snapshot_id,
+                "job_id": job_id,
+                "confidence": confidence,
+                "evidence": evidence,
+            }
+        )
+    return GraphWritePlan(scope=scope, nodes=tuple(nodes), edges=tuple(edges))
+
+
+def parameterized_statements(plan: GraphWritePlan) -> list[dict[str, Any]]:
+    """Return Neo4j transactional HTTP statements using only fixed templates."""
+    statements = [{"statement": NODE_QUERY, "parameters": {"scope": plan.scope, **node}} for node in plan.nodes]
+    statements.extend({"statement": EDGE_QUERY, "parameters": {"scope": plan.scope, **edge}} for edge in plan.edges)
+    return statements
+
+
+async def execute_plan(tx: GraphTransaction, plan: GraphWritePlan, *, enabled: bool) -> dict[str, int]:
+    if not enabled:
+        return {"nodes": 0, "edges": 0}
+    for node in plan.nodes:
+        await tx.run(NODE_QUERY, {"scope": plan.scope, **node})
+    for edge in plan.edges:
+        await tx.run(EDGE_QUERY, {"scope": plan.scope, **edge})
+    return {"nodes": len(plan.nodes), "edges": len(plan.edges)}

offline-backend-20260512/backend/packages/harness/deerflow/skill_knowledge/merge.py

@@ -0,0 +1,31 @@
+"""Portable entity/relation normalization used by both knowledge products."""
+
+from __future__ import annotations
+
+import re
+import unicodedata
+
+
+def normalize_name(value: str) -> str:
+    text = unicodedata.normalize("NFKC", value or "").strip().casefold()
+    text = text.strip("`'\"“”‘’《》〈〉[](){}")
+    text = re.sub(r"\s+", " ", text)
+    return text
+
+
+def normalize_predicate(value: str) -> str:
+    normalized = normalize_name(value).replace(" ", "_")
+    aliases = {
+        "依赖": "depends_on",
+        "需要": "depends_on",
+        "requires": "depends_on",
+        "调用": "uses",
+        "使用": "uses",
+        "uses": "uses",
+        "生成": "produces",
+        "输出": "produces",
+        "读取": "consumes",
+        "输入": "consumes",
+        "引用": "references",
+    }
+    return aliases.get(normalized, normalized or "related_to")

offline-backend-20260512/backend/packages/harness/deerflow/skill_knowledge/scanner.py

@@ -0,0 +1,351 @@
+"""Safe, deterministic scanner for the knowledge-bearing part of a skill."""
+
+from __future__ import annotations
+
+import csv
+import hashlib
+import json
+import re
+import zipfile
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+CODE_SUFFIXES = {
+    ".py",
+    ".pyc",
+    ".pyo",
+    ".ts",
+    ".tsx",
+    ".js",
+    ".jsx",
+    ".mjs",
+    ".cjs",
+    ".sh",
+    ".bash",
+    ".zsh",
+    ".fish",
+    ".bat",
+    ".cmd",
+    ".ps1",
+    ".psm1",
+    ".java",
+    ".kt",
+    ".kts",
+    ".go",
+    ".rs",
+    ".c",
+    ".cc",
+    ".cpp",
+    ".h",
+    ".hpp",
+    ".cs",
+    ".php",
+    ".rb",
+    ".pl",
+    ".lua",
+    ".swift",
+    ".scala",
+    ".sql",
+    ".vue",
+    ".svelte",
+}
+TEXT_SUFFIXES = {".md", ".mdx", ".txt", ".json", ".yaml", ".yml", ".toml", ".ini", ".csv", ".tsv"}
+BINARY_KNOWLEDGE_SUFFIXES = {".xlsx", ".xls", ".pdf", ".docx", ".pptx", ".png", ".jpg", ".jpeg", ".webp", ".gif", ".bmp", ".zip"}
+EXCLUDED_DIRS = {".git", "node_modules", "__pycache__", ".pytest_cache", "dist", "build", ".mypy_cache", ".ruff_cache", ".address-edits"}
+EXCLUDED_FILENAMES = {
+    "package-lock.json",
+    "pnpm-lock.yaml",
+    "yarn.lock",
+    "bun.lockb",
+    "poetry.lock",
+    "uv.lock",
+    "pipfile.lock",
+    "cargo.lock",
+    "composer.lock",
+}
+SENSITIVE_NAME_RE = re.compile(
+    r"(^|[._-])(\.env|id_rsa|id_dsa|id_ecdsa|id_ed25519|private[_-]?key|credentials?|secrets?)([._-]|$)",
+    re.IGNORECASE,
+)
+SECRET_CONTENT_RE = re.compile(r"(?im)^\s*(?:api[_-]?key|access[_-]?key|secret(?:[_-]?key)?|password|token|private[_-]?key)\s*[:=]\s*[^\s]{8,}\s*$")
+PEM_PRIVATE_RE = re.compile(r"-----BEGIN (?:RSA |EC |OPENSSH )?PRIVATE KEY-----")
+FENCE_RE = re.compile(r"(?ms)^\s*(```|~~~).*?^\s*\1\s*$")
+MARKDOWN_LINK_RE = re.compile(r"\[[^\]]+\]\(([^)#?]+)(?:#[^)]*)?\)")
+MAX_TEXT_BYTES = 2 * 1024 * 1024
+MAX_ARCHIVE_FILES = 1_000
+MAX_ARCHIVE_ENTRY_BYTES = 20 * 1024 * 1024
+MAX_ARCHIVE_TOTAL_BYTES = 200 * 1024 * 1024
+
+
+@dataclass(slots=True)
+class ScannedFile:
+    path: str
+    kind: str
+    sha256: str
+    size: int
+    text: str = ""
+    title: str = ""
+    references: list[dict[str, Any]] = field(default_factory=list)
+    safety: dict[str, Any] = field(default_factory=lambda: {"status": "allowed", "warnings": []})
+
+    def manifest(self) -> dict[str, Any]:
+        return {
+            "path": self.path,
+            "kind": self.kind,
+            "sha256": self.sha256,
+            "size": self.size,
+            "text_digest": f"sha256:{hashlib.sha256(self.text.encode('utf-8')).hexdigest()}" if self.text else None,
+            "title": self.title,
+            "references": self.references,
+            "safety": self.safety,
+        }
+
+
+@dataclass(slots=True)
+class ScanResult:
+    skill_name: str
+    source_root: str
+    source_digest: str
+    files: list[ScannedFile]
+    blocked_files: list[dict[str, str]]
+    skipped_code_count: int
+    skipped_other_count: int
+
+    @property
+    def manifest(self) -> list[dict[str, Any]]:
+        return [item.manifest() for item in self.files] + [{"path": item["path"], "kind": "blocked", "safety": {"status": "blocked", "warnings": [item["reason"]]}} for item in self.blocked_files]
+
+    @property
+    def counts(self) -> dict[str, int]:
+        return {
+            "knowledge_files": len(self.files),
+            "blocked_files": len(self.blocked_files),
+            "skipped_code_files": self.skipped_code_count,
+            "skipped_other_files": self.skipped_other_count,
+        }
+
+
+def _safe_relative(path: Path, root: Path) -> str:
+    resolved = path.resolve()
+    resolved_root = root.resolve()
+    try:
+        relative = resolved.relative_to(resolved_root)
+    except ValueError as exc:
+        raise ValueError("Skill file escapes its source root") from exc
+    return relative.as_posix()
+
+
+def _strip_markdown_code(text: str) -> str:
+    return FENCE_RE.sub("\n[示例代码已按安全策略省略]\n", text)
+
+
+def _read_text(path: Path, suffix: str) -> str:
+    raw = path.read_bytes()
+    if len(raw) > MAX_TEXT_BYTES:
+        raise ValueError("text file exceeds 2 MiB parsing limit")
+    text = raw.decode("utf-8-sig", errors="replace")
+    if suffix in {".md", ".mdx"}:
+        return _strip_markdown_code(text)
+    if suffix in {".csv", ".tsv"}:
+        delimiter = "\t" if suffix == ".tsv" else ","
+        rows = list(csv.reader(text.splitlines(), delimiter=delimiter))
+        return "\n".join(" | ".join(cell.strip() for cell in row) for row in rows[:500])
+    if suffix == ".json":
+        try:
+            return json.dumps(json.loads(text), ensure_ascii=False, indent=2)
+        except json.JSONDecodeError:
+            return text
+    return text
+
+
+def _title(text: str, fallback: str) -> str:
+    match = re.search(r"(?m)^#\s+(.+?)\s*$", text)
+    return match.group(1).strip() if match else fallback
+
+
+def _kind(suffix: str) -> str:
+    return {
+        ".md": "markdown",
+        ".mdx": "markdown",
+        ".txt": "text",
+        ".csv": "table",
+        ".tsv": "table",
+        ".xlsx": "spreadsheet",
+        ".xls": "spreadsheet",
+        ".pdf": "pdf",
+        ".docx": "office",
+        ".pptx": "office",
+        ".png": "image",
+        ".jpg": "image",
+        ".jpeg": "image",
+        ".webp": "image",
+        ".gif": "image",
+        ".bmp": "image",
+        ".zip": "archive",
+    }.get(suffix, "structured_data")
+
+
+def _zip_members(path: Path, archive_relative: str) -> tuple[list[ScannedFile], list[dict[str, str]], int, int]:
+    files: list[ScannedFile] = []
+    blocked: list[dict[str, str]] = []
+    skipped_code = 0
+    skipped_other = 0
+    with zipfile.ZipFile(path) as archive:
+        members = archive.infolist()
+        if len(members) > MAX_ARCHIVE_FILES:
+            return [], [{"path": archive_relative, "reason": "压缩包文件数超过安全上限"}], 0, 0
+        total_size = sum(member.file_size for member in members)
+        if total_size > MAX_ARCHIVE_TOTAL_BYTES:
+            return [], [{"path": archive_relative, "reason": "压缩包展开体积超过安全上限"}], 0, 0
+        for member in members:
+            member_path = Path(member.filename.replace("\\", "/"))
+            virtual_path = f"{archive_relative}!/{member_path.as_posix()}"
+            unix_mode = member.external_attr >> 16
+            if member.is_dir():
+                continue
+            if member_path.is_absolute() or ".." in member_path.parts or (unix_mode & 0o170000) == 0o120000:
+                blocked.append({"path": virtual_path, "reason": "压缩包包含路径穿越或符号链接"})
+                continue
+            suffix = member_path.suffix.lower()
+            lower_name = member_path.name.lower()
+            if lower_name in EXCLUDED_FILENAMES or suffix in CODE_SUFFIXES:
+                skipped_code += 1
+                continue
+            if SENSITIVE_NAME_RE.search(lower_name) or suffix in {".pem", ".key", ".p12", ".pfx"}:
+                blocked.append({"path": virtual_path, "reason": "疑似密钥或敏感配置文件"})
+                continue
+            if suffix not in TEXT_SUFFIXES or member.file_size > MAX_ARCHIVE_ENTRY_BYTES:
+                skipped_other += 1
+                continue
+            raw = archive.read(member)
+            text = raw.decode("utf-8-sig", errors="replace")
+            if suffix in {".md", ".mdx"}:
+                text = _strip_markdown_code(text)
+            if SECRET_CONTENT_RE.search(text) or PEM_PRIVATE_RE.search(text):
+                blocked.append({"path": virtual_path, "reason": "内容命中高置信敏感信息规则"})
+                continue
+            files.append(
+                ScannedFile(
+                    path=virtual_path,
+                    kind=_kind(suffix),
+                    sha256=hashlib.sha256(raw).hexdigest(),
+                    size=len(raw),
+                    text=text,
+                    title=_title(text, member_path.stem),
+                )
+            )
+    return files, blocked, skipped_code, skipped_other
+
+
+def scan_skill(skill_name: str, skill_root: Path) -> ScanResult:
+    """Scan one already-resolved skill directory without following symlinks."""
+
+    root = skill_root.resolve()
+    if not root.is_dir():
+        raise FileNotFoundError(f"Skill '{skill_name}' source directory does not exist")
+    files: list[ScannedFile] = []
+    blocked: list[dict[str, str]] = []
+    skipped_code = 0
+    skipped_other = 0
+
+    for path in sorted(root.rglob("*"), key=lambda item: item.as_posix().lower()):
+        if path.is_symlink() or not path.is_file():
+            continue
+        relative = _safe_relative(path, root)
+        if any(part.lower() in EXCLUDED_DIRS for part in Path(relative).parts[:-1]):
+            continue
+        lower_name = path.name.lower()
+        suffix = path.suffix.lower()
+        if lower_name in EXCLUDED_FILENAMES or suffix in CODE_SUFFIXES:
+            skipped_code += 1
+            continue
+        if SENSITIVE_NAME_RE.search(lower_name) or suffix in {".pem", ".key", ".p12", ".pfx"}:
+            blocked.append({"path": relative, "reason": "疑似密钥或敏感配置文件"})
+            continue
+        if suffix not in TEXT_SUFFIXES | BINARY_KNOWLEDGE_SUFFIXES:
+            skipped_other += 1
+            continue
+
+        # Never upload an archive wholesale: it may contain excluded code or a
+        # blocked secret. Only independently validated knowledge members enter
+        # the manifest and digest.
+        if suffix == ".zip":
+            try:
+                nested, nested_blocked, nested_code, nested_other = _zip_members(path, relative)
+                files.extend(nested)
+                blocked.extend(nested_blocked)
+                skipped_code += nested_code
+                skipped_other += nested_other
+            except (OSError, zipfile.BadZipFile):
+                blocked.append({"path": relative, "reason": "压缩包损坏或格式不受支持"})
+            continue
+
+        raw = path.read_bytes()
+        digest = hashlib.sha256(raw).hexdigest()
+        text = ""
+        warnings: list[str] = []
+        if suffix in TEXT_SUFFIXES:
+            try:
+                text = _read_text(path, suffix)
+            except ValueError as exc:
+                warnings.append(str(exc))
+            if text and (SECRET_CONTENT_RE.search(text) or PEM_PRIVATE_RE.search(text)):
+                blocked.append({"path": relative, "reason": "内容命中高置信敏感信息规则"})
+                continue
+        references = []
+        if suffix in {".md", ".mdx"}:
+            for target in MARKDOWN_LINK_RE.findall(text):
+                normalized = target.replace("\\", "/").lstrip("./")
+                if normalized:
+                    references.append({"target_path": normalized, "evidence": "markdown_link", "confidence": 1.0})
+        files.append(
+            ScannedFile(
+                path=relative,
+                kind=_kind(suffix),
+                sha256=digest,
+                size=len(raw),
+                text=text,
+                title=_title(text, path.stem),
+                references=references,
+                safety={"status": "allowed", "warnings": warnings},
+            )
+        )
+
+    digest_input = "\n".join(f"{item.path}\0{item.sha256}" for item in files).encode("utf-8")
+    source_digest = f"sha256:{hashlib.sha256(digest_input).hexdigest()}"
+    return ScanResult(
+        skill_name=skill_name,
+        source_root=str(root),
+        source_digest=source_digest,
+        files=files,
+        blocked_files=blocked,
+        skipped_code_count=skipped_code,
+        skipped_other_count=skipped_other,
+    )
+
+
+async def enrich_converted_documents(scan: ScanResult) -> ScanResult:
+    """Extract Office/PDF/spreadsheet text without modifying the skill tree."""
+
+    from deerflow.utils.file_conversion import CONVERTIBLE_EXTENSIONS, extract_file_to_markdown
+
+    allowed: list[ScannedFile] = []
+    root = Path(scan.source_root)
+    for item in scan.files:
+        if Path(item.path).suffix.lower() in CONVERTIBLE_EXTENSIONS:
+            text = await extract_file_to_markdown(root / item.path)
+            if text:
+                text = _strip_markdown_code(text)
+                if SECRET_CONTENT_RE.search(text) or PEM_PRIVATE_RE.search(text):
+                    scan.blocked_files.append({"path": item.path, "reason": "转换文本命中高置信敏感信息规则"})
+                    continue
+                item.text = text
+                item.title = _title(text, item.title)
+            else:
+                item.safety["warnings"].append("文档转换不可用，仅保留原始附件")
+        allowed.append(item)
+    scan.files = allowed
+    digest_input = "\n".join(f"{item.path}\0{item.sha256}" for item in scan.files).encode("utf-8")
+    scan.source_digest = f"sha256:{hashlib.sha256(digest_input).hexdigest()}"
+    return scan

offline-backend-20260512/backend/packages/harness/deerflow/utils/file_conversion.py

@@ -256,6 +256,24 @@ async def convert_file_to_markdown(file_path: Path) -> Path | None:
         return None
 
 
+async def extract_file_to_markdown(file_path: Path) -> str | None:
+    """Extract a supported document to Markdown without creating a sidecar file.
+
+    Knowledge-ingest scanners use this variant because running a read-only
+    distillation task must never add generated ``.md`` files to a live skill.
+    Conversion always runs off the event loop; Office/PDF parsers can be CPU
+    intensive even for deceptively small inputs.
+    """
+
+    if file_path.suffix.lower() not in CONVERTIBLE_EXTENSIONS:
+        return None
+    try:
+        return await asyncio.to_thread(_do_convert, file_path, _get_pdf_converter())
+    except Exception as exc:
+        logger.error("Failed to extract %s as markdown: %s", file_path.name, exc)
+        return None
+
+
 # Regex for bold-only lines that look like section headings.
 # Targets SEC filing structural headings that pymupdf4llm renders as **bold**
 # rather than # Markdown headings (because they use same font size as body text,

offline-backend-20260512/backend/packages/harness/pyproject.toml

@@ -53,6 +53,10 @@ postgres = [
     "psycopg-pool>=3.3.0",
 ]
 mysql = ["asyncmy>=0.2.10"]
+gauss = [
+    "asyncpg>=0.29",
+    "opengauss-sqlalchemy>=2.4,<3",
+]
 pymupdf = ["pymupdf4llm>=0.0.17"]
 
 [build-system]

offline-backend-20260512/backend/pyproject.toml

@@ -30,6 +30,7 @@ dependencies = [
 [project.optional-dependencies]
 postgres = ["deerflow-harness[postgres]"]
 mysql = ["deerflow-harness[mysql]"]
+gauss = ["deerflow-harness[gauss]"]
 
 [dependency-groups]
 dev = [

offline-backend-20260512/backend/tests/test_artifact_library.py

@@ -0,0 +1,180 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+from app.gateway import deps
+from app.gateway.routers import artifacts
+
+
+class _FakeThreadStore:
+    def __init__(self, rows: list[dict]) -> None:
+        self.rows = rows
+        self.calls: list[dict] = []
+
+    async def search(self, **kwargs) -> list[dict]:
+        self.calls.append(kwargs)
+        offset = int(kwargs.get("offset", 0))
+        limit = int(kwargs.get("limit", 100))
+        return self.rows[offset : offset + limit]
+
+
+class _FakeAgentStore:
+    async def list_by_ids(self, agent_ids: list[str], user_id: str) -> list[dict]:
+        assert user_id == "owner"
+        agents = {
+            "analysis-agent": {
+                "id": "analysis-agent",
+                "name": "舆情分析智能体",
+            }
+        }
+        return [agents[agent_id] for agent_id in agent_ids if agent_id in agents]
+
+
+@pytest.mark.asyncio
+async def test_artifact_library_aggregates_generated_non_media_files(tmp_path: Path, monkeypatch):
+    thread_one = tmp_path / "thread-one" / "outputs"
+    thread_two = tmp_path / "thread-two" / "outputs"
+    thread_one.mkdir(parents=True)
+    thread_two.mkdir(parents=True)
+
+    report = thread_one / "研判报告.docx"
+    table = thread_one / "数据汇总.xlsx"
+    image = thread_one / "配图.png"
+    audio = thread_two / "录音.mp3"
+    notes = thread_two / "结论.md"
+    archive = thread_two / "证据.zip"
+    for path, content in (
+        (report, b"doc"),
+        (table, b"sheet"),
+        (image, b"image"),
+        (audio, b"audio"),
+        (notes, b"# notes"),
+        (archive, b"zip"),
+    ):
+        path.write_bytes(content)
+
+    os.utime(report, (100, 100))
+    os.utime(table, (200, 200))
+    os.utime(notes, (300, 300))
+    os.utime(archive, (400, 400))
+
+    rows = [
+        {
+            "thread_id": "thread-one",
+            "display_name": "战场环境综合研判",
+            "metadata": {"agent_id": "analysis-agent"},
+        },
+        {
+            "thread_id": "thread-two",
+            "display_name": "生成发展报告",
+            "metadata": {},
+        },
+    ]
+    store = _FakeThreadStore(rows)
+
+    class _Paths:
+        def sandbox_outputs_dir(self, thread_id: str, *, user_id: str) -> Path:
+            assert user_id == "owner"
+            return tmp_path / thread_id / "outputs"
+
+    async def _resolve_owner(_thread_id: str) -> str:
+        return "owner"
+
+    monkeypatch.setattr(artifacts, "get_paths", lambda: _Paths())
+    monkeypatch.setattr(artifacts, "aresolve_path_user_id", _resolve_owner)
+
+    items = await artifacts._scan_artifact_library(store)
+
+    assert [item["name"] for item in items] == ["证据.zip", "结论.md", "数据汇总.xlsx", "研判报告.docx"]
+    assert [item["file_kind"] for item in items] == ["archive", "text_data", "spreadsheet", "document"]
+    assert [item["source_type"] for item in items] == ["general_qa", "general_qa", "agent", "agent"]
+    assert items[-1]["thread_title"] == "战场环境综合研判"
+    assert items[-1]["agent_id"] == "analysis-agent"
+    assert all(item["name"] not in {"配图.png", "录音.mp3"} for item in items)
+    assert store.calls[0]["exclude_system"] is True
+
+
+@pytest.mark.asyncio
+async def test_artifact_library_endpoint_filters_searches_and_pages(tmp_path: Path, monkeypatch):
+    output_dir = tmp_path / "thread-one" / "outputs"
+    agent_output_dir = tmp_path / "thread-agent" / "outputs"
+    output_dir.mkdir(parents=True)
+    agent_output_dir.mkdir(parents=True)
+    (output_dir / "任务报告.md").write_text("report", encoding="utf-8")
+    (output_dir / "任务数据.csv").write_text("a,b", encoding="utf-8")
+    (agent_output_dir / "智能体结论.md").write_text("agent report", encoding="utf-8")
+
+    store = _FakeThreadStore(
+        [
+            {
+                "thread_id": "thread-one",
+                "display_name": "任务研判对话",
+                "metadata": {},
+            },
+            {
+                "thread_id": "thread-agent",
+                "display_name": "智能体研判对话",
+                "metadata": {"agent_id": "analysis-agent"},
+            },
+        ]
+    )
+
+    class _Paths:
+        def sandbox_outputs_dir(self, thread_id: str, *, user_id: str) -> Path:
+            return tmp_path / thread_id / "outputs"
+
+    async def _resolve_owner(_thread_id: str) -> str:
+        return "owner"
+
+    monkeypatch.setattr(artifacts, "get_paths", lambda: _Paths())
+    monkeypatch.setattr(artifacts, "aresolve_path_user_id", _resolve_owner)
+    monkeypatch.setattr(artifacts, "get_effective_user_id", lambda: "owner")
+    monkeypatch.setattr(deps, "get_thread_store", lambda _request: store)
+    monkeypatch.setattr(deps, "get_agent_store", lambda _request: _FakeAgentStore())
+    artifacts._artifact_library_cache.clear()
+
+    raw_endpoint = artifacts.list_artifact_library.__wrapped__
+    response = await raw_endpoint(
+        request=SimpleNamespace(),
+        query="研判对话",
+        file_kind="text_data",
+        source_type="general_qa",
+        offset=0,
+        limit=1,
+        refresh=True,
+    )
+
+    assert response.total == 1
+    assert response.offset == 0
+    assert response.limit == 1
+    assert [item.name for item in response.items] == ["任务报告.md"]
+
+    spreadsheet_response = await raw_endpoint(
+        request=SimpleNamespace(),
+        query="任务数据",
+        file_kind="spreadsheet",
+        source_type="all",
+        offset=0,
+        limit=50,
+        refresh=False,
+    )
+    assert spreadsheet_response.total == 1
+    assert spreadsheet_response.items[0].name == "任务数据.csv"
+
+    agent_response = await raw_endpoint(
+        request=SimpleNamespace(),
+        query=None,
+        file_kind="all",
+        source_type="agent",
+        offset=0,
+        limit=50,
+        refresh=False,
+    )
+    assert agent_response.total == 1
+    assert agent_response.items[0].source_type == "agent"
+    assert agent_response.items[0].agent_name == "舆情分析智能体"
+    assert agent_response.items[0].name == "智能体结论.md"

offline-backend-20260512/backend/tests/test_assistant_knowledge_repository.py

@@ -0,0 +1,193 @@
+import pytest
+import pytest_asyncio
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine
+
+from deerflow.persistence.assistant_knowledge import AssistantKnowledgeRepository
+from deerflow.persistence.assistant_knowledge.model import (
+    AssistantKnowledgeEntityContributionRow,
+    AssistantKnowledgeEntityRow,
+)
+from deerflow.persistence.base import Base
+
+
+class FakeEmbeddingClient:
+    dimensions = 3
+
+    async def embed_query(self, _query: str) -> list[float]:
+        return [1.0, 0.0, 0.0]
+
+
+@pytest_asyncio.fixture
+async def repository():
+    engine = create_async_engine("sqlite+aiosqlite:///:memory:")
+    async with engine.begin() as connection:
+        await connection.run_sync(Base.metadata.create_all)
+    yield AssistantKnowledgeRepository(async_sessionmaker(engine, expire_on_commit=False))
+    await engine.dispose()
+
+
+@pytest.mark.asyncio
+async def test_single_base_entity_alignment_search_and_revision(repository: AssistantKnowledgeRepository) -> None:
+    first = await repository.initialize(created_by="admin")
+    second = await repository.initialize(created_by="someone-else")
+    assert first["id"] == second["id"]
+
+    package = {
+        "source": {"type": "skill"},
+        "wiki_pages": [{"slug": "skills/demo/overview", "title": "示例知识", "content": "张三负责测试。"}],
+        "entities": [{"local_id": "e1", "type": "person", "name": "张三", "aliases": ["Zhang San"]}],
+        "relations": [],
+        "documents": [],
+        "chunks": [],
+        "graph": {"nodes": [], "edges": []},
+    }
+    await repository.import_package(
+        package=package,
+        source_type="skill",
+        source_key="demo",
+        source_name="示例技能",
+        trigger="skill_direct",
+        created_by="admin",
+        skill_name="demo",
+    )
+    await repository.import_package(
+        package=package,
+        source_type="weknora",
+        source_key="kb-1",
+        source_name="普通库",
+        trigger="manual_from_weknora",
+        created_by="admin",
+    )
+
+    results = await repository.list_pages(query="张三")
+    alias_results = await repository.list_pages(query="Zhang San")
+    revisions = await repository.list_revisions("skills/demo/overview")
+    assert {row["page_type"] for row in results} == {"source", "entity"}
+    assert [row["page_type"] for row in alias_results] == ["entity"]
+    assert len(revisions) == 2
+
+
+@pytest.mark.asyncio
+async def test_delete_source_keeps_entity_until_last_contribution_is_removed(
+    repository: AssistantKnowledgeRepository,
+) -> None:
+    package = {
+        "source": {},
+        "wiki_pages": [],
+        "entities": [{"local_id": "e1", "type": "concept", "name": "共享实体"}],
+        "relations": [],
+    }
+    for source_key in ("one", "two"):
+        await repository.import_package(
+            package=package,
+            source_type="skill",
+            source_key=source_key,
+            source_name=source_key,
+            trigger="skill_direct",
+            created_by="admin",
+            skill_name=source_key,
+        )
+
+    sources = await repository.list_sources()
+    await repository.delete_source(next(row["id"] for row in sources if row["source_key"] == "one"))
+    async with repository._sf() as session:
+        entity = (await session.execute(select(AssistantKnowledgeEntityRow))).scalar_one()
+        contributions = (await session.execute(select(AssistantKnowledgeEntityContributionRow))).scalars().all()
+        assert entity.status == "active"
+        assert len(contributions) == 1
+
+    await repository.delete_source(next(row["id"] for row in sources if row["source_key"] == "two"))
+    async with repository._sf() as session:
+        entity = (await session.execute(select(AssistantKnowledgeEntityRow))).scalar_one()
+        assert entity.status == "deleted"
+
+
+@pytest.mark.asyncio
+async def test_custom_base_import_is_also_mirrored_to_global_base(
+    repository: AssistantKnowledgeRepository,
+) -> None:
+    global_base = await repository.initialize(created_by="admin")
+    custom_base = await repository.create_base(
+        created_by="admin",
+        name="专题助手库",
+        description="只承接某个专题的 WeKnora 导出数据",
+    )
+    package = {
+        "source": {"type": "weknora"},
+        "wiki_pages": [{"slug": "wiki/topic", "title": "专题 Wiki", "content": "王五负责统筹。"}],
+        "entities": [{"local_id": "e1", "type": "person", "name": "王五"}],
+        "relations": [],
+    }
+
+    job = await repository.import_package(
+        package=package,
+        source_type="weknora",
+        source_key="kb-topic",
+        source_name="专题普通库",
+        trigger="manual_from_weknora",
+        created_by="admin",
+        mapping_id="kb-topic",
+        base_id=custom_base["id"],
+    )
+
+    assert job["base_id"] == custom_base["id"]
+    assert job["mirrored_global_base_id"] == global_base["id"]
+    assert len(await repository.list_pages(base_id=custom_base["id"], query="王五")) == 2
+    assert len(await repository.list_pages(base_id=global_base["id"], query="王五")) == 2
+    bases = await repository.list_bases()
+    assert [base["id"] for base in bases[:2]] == [global_base["id"], custom_base["id"]]
+
+
+@pytest.mark.asyncio
+async def test_imported_vectors_are_reused_for_assistant_search(
+    repository: AssistantKnowledgeRepository,
+) -> None:
+    base = await repository.initialize(created_by="admin")
+    job = await repository.import_package(
+        package={
+            "source": {"type": "weknora"},
+            "wiki_pages": [{"slug": "wiki/vector", "title": "向量页", "content": "页面正文"}],
+            "vectors": [
+                {
+                    "wiki_slug": "wiki/vector",
+                    "section_index": 0,
+                    "section_content": "张三负责向量化检索。",
+                    "embedding_model": "bge-embedding-m3",
+                    "embedding_dimensions": 3,
+                    "vector": [1.0, 0.0, 0.0],
+                },
+                {
+                    "wiki_slug": "wiki/vector",
+                    "section_index": 1,
+                    "section_content": "无关片段。",
+                    "embedding_model": "bge-embedding-m3",
+                    "embedding_dimensions": 3,
+                    "vector": [0.0, 1.0, 0.0],
+                },
+            ],
+        },
+        source_type="weknora",
+        source_key="kb-vector",
+        source_name="向量普通库",
+        trigger="manual_package_upload",
+        created_by="admin",
+    )
+
+    assert job["counts"]["vectors"] == 2
+    results = await repository.search_pages(
+        base_ids=[base["id"]],
+        query="这个查询不靠关键词",
+        embedding_client=FakeEmbeddingClient(),
+    )
+    assert results[0]["retrieval_mode"] == "vector"
+    assert results[0]["matched_section"]["embedding_model"] == "bge-embedding-m3"
+    assert results[0]["content_markdown"] == "张三负责向量化检索。"
+
+    sources = await repository.list_sources()
+    assert await repository.delete_source(sources[0]["id"])
+    assert await repository.search_pages(
+        base_ids=[base["id"]],
+        query="这个查询不靠关键词",
+        embedding_client=FakeEmbeddingClient(),
+    ) == []

offline-backend-20260512/backend/tests/test_gauss_database.py

@@ -0,0 +1,81 @@
+"""Regression coverage for the openGauss/GaussDB persistence backend."""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime
+
+import pytest
+from pydantic import ValidationError
+from sqlalchemy.engine import make_url
+from sqlalchemy.schema import CreateIndex, CreateTable
+
+from deerflow.config.database_config import DatabaseConfig
+from deerflow.persistence.types import BeijingDateTime
+
+
+@pytest.mark.parametrize(
+    ("configured", "expected"),
+    [
+        (
+            "opengauss://user:pass@127.0.0.1:26000/deerflow",
+            "opengauss+asyncpg://user:pass@127.0.0.1:26000/deerflow",
+        ),
+        (
+            "postgresql://user:pass@127.0.0.1:26000/deerflow?ssl=require",
+            "opengauss+asyncpg://user:pass@127.0.0.1:26000/deerflow?ssl=require",
+        ),
+        (
+            "opengauss+asyncpg://user:pass@127.0.0.1:26000/deerflow",
+            "opengauss+asyncpg://user:pass@127.0.0.1:26000/deerflow",
+        ),
+    ],
+)
+def test_gauss_urls_always_select_the_opengauss_async_dialect(
+    configured: str,
+    expected: str,
+) -> None:
+    config = DatabaseConfig(backend="gauss", gauss_url=configured)
+    assert config.app_sqlalchemy_url == expected
+
+
+def test_gauss_backend_requires_a_supported_nonempty_url() -> None:
+    with pytest.raises(ValueError, match="gauss_url is required"):
+        _ = DatabaseConfig(backend="gauss").app_sqlalchemy_url
+
+    with pytest.raises(ValueError, match="must use opengauss"):
+        _ = DatabaseConfig(
+            backend="gauss",
+            gauss_url="mysql://user:pass@127.0.0.1/deerflow",
+        ).app_sqlalchemy_url
+
+    with pytest.raises(ValidationError):
+        DatabaseConfig(backend="unknown")
+
+
+def test_gauss_timestamps_keep_timezone_information() -> None:
+    dialect = type("GaussDialect", (), {"name": "opengauss"})()
+    value = datetime(2026, 9, 2, 8, 30, tzinfo=UTC)
+    column_type = BeijingDateTime()
+
+    assert column_type.process_bind_param(value, dialect) is value
+    assert column_type.process_result_value(value, dialect) is value
+
+
+def test_official_async_dialect_compiles_the_complete_orm_schema() -> None:
+    """Every registered table/index must compile before startup touches GaussDB."""
+    pytest.importorskip("opengauss_sqlalchemy")
+
+    import deerflow.persistence.models  # noqa: F401 -- register ORM metadata
+    from deerflow.persistence.base import Base
+
+    dialect_cls = make_url("opengauss+asyncpg://user:pass@localhost/db").get_dialect(
+        _is_async=True
+    )
+    dialect = dialect_cls()
+
+    assert dialect.__class__.__module__.startswith("opengauss_sqlalchemy")
+    assert Base.metadata.sorted_tables
+    for table in Base.metadata.sorted_tables:
+        assert str(CreateTable(table).compile(dialect=dialect)).startswith("\nCREATE TABLE")
+        for index in table.indexes:
+            assert str(CreateIndex(index).compile(dialect=dialect)).startswith("CREATE")

offline-backend-20260512/backend/tests/test_llmwiki_local_index_repository.py

@@ -66,6 +66,9 @@ async def test_sql_store_atomically_replaces_vectors_and_marks_missing_deleted(t
     assert store.write_batch_size == 1
     assert len(snapshot["rows"]) == 3
     assert decode_vector(snapshot["rows"][0]["vector_blob"], 2).tolist() == pytest.approx([0.6, 0.8])
+    streamed = [row async for row in store.iter_vector_snapshot_rows("mapping-1", "fp", batch_size=2)]
+    assert [row["section_index"] for row in streamed] == [0, 1, 2]
+    assert streamed[0]["page"]["slug"] == "people/alice"
     page_statuses = await store.list_page_index_status("mapping-1")
     assert len(page_statuses) == 1
     assert page_statuses[0] == {

offline-backend-20260512/backend/tests/test_public_llmwiki_embed.py

@@ -68,7 +68,7 @@ def _published_row() -> dict[str, object]:
 
 
 @pytest.mark.asyncio
-async def test_public_knowledge_base_list_exposes_only_published_wiki_identity() -> None:
+async def test_public_knowledge_base_list_exposes_live_safe_metadata_and_generated_index(monkeypatch) -> None:
     store = PublicListStore(
         [
             {
@@ -83,21 +83,116 @@ async def test_public_knowledge_base_list_exposes_only_published_wiki_identity()
                 "owner_user_id": "system",
                 "name": "对话沉淀",
             },
+            {
+                **_published_row(),
+                "id": "stale-kb",
+                "weknora_id": "remote-missing",
+            },
         ]
     )
 
+    class Client:
+        async def list_knowledge_bases(self):
+            return [
+                {
+                    "id": "remote-one",
+                    "name": "远端公开知识库",
+                    "description": "远端公开说明",
+                    "type": "wiki",
+                    "knowledge_count": 12,
+                    "chunk_count": 34,
+                    "processing_count": 1,
+                    "share_count": 3,
+                    "created_at": "2026-07-01T08:00:00+08:00",
+                    "updated_at": "2026-08-01T08:00:00+08:00",
+                    "tenant_id": 10001,
+                    "creator_id": "secret-creator",
+                    "storage_config": {"secret_key": "must-not-leak"},
+                }
+            ]
+
+        async def get_knowledge_base(self, knowledge_base_id: str):
+            assert knowledge_base_id == "remote-one"
+            return {
+                "id": knowledge_base_id,
+                "vector_store_name": "System default",
+                "vector_store_source": "env",
+                "vector_store_engine_type": "elasticsearch",
+                "vector_store_status": "available",
+                "wiki_config": {"enabled": True},
+            }
+
+        async def list_wiki_pages(self, knowledge_base_id: str, **kwargs):
+            assert knowledge_base_id == "remote-one"
+            assert kwargs == {"page": 1, "page_size": 500, "query": ""}
+            return {
+                "pages": [
+                    {
+                        "id": "page-one",
+                        "slug": "concept/start",
+                        "title": "开始使用",
+                        "page_type": "concept",
+                        "status": "completed",
+                        "created_at": "2026-07-02T08:00:00+08:00",
+                        "updated_at": "2026-08-02T08:00:00+08:00",
+                    },
+                    {
+                        "id": "page-two",
+                        "slug": "summary/overview",
+                        "title": "知识概览",
+                        "page_type": "summary",
+                        "status": "completed",
+                        "created_at": "2026-07-03T08:00:00+08:00",
+                        "updated_at": "2026-08-03T08:00:00+08:00",
+                    },
+                ],
+                "total": 2,
+                "page": 1,
+                "page_size": 500,
+                "total_pages": 1,
+            }
+
+    monkeypatch.setattr(llmwiki, "_client_or_503", lambda: Client())
+
     payload = await llmwiki.list_public_knowledge_bases(_request(store))
 
     assert store.calls == [("__public_wiki__", "public", False)]
-    assert payload == {
-        "knowledge_bases": [
-            {
-                "id": "1063edee-332a-43b4-bbd9-73bf0a708b94",
-                "name": "公开知识库",
-            }
-        ],
-        "total": 1,
+    assert payload["total"] == 1
+    item = payload["knowledge_bases"][0]
+    assert item["id"] == "1063edee-332a-43b4-bbd9-73bf0a708b94"
+    assert item["name"] == "远端公开知识库"
+    assert item["description"] == "远端公开说明"
+    assert item["type"] == "wiki"
+    assert item["knowledge_count"] == 12
+    assert item["chunk_count"] == 34
+    assert item["processing_count"] == 1
+    assert item["share_count"] == 3
+    assert item["is_processing"] is True
+    assert item["remote_created_at"] == "2026-07-01T08:00:00+08:00"
+    assert item["remote_updated_at"] == "2026-08-01T08:00:00+08:00"
+    assert item["vector_store_engine_type"] == "elasticsearch"
+    assert item["wiki"] == {
+        "status": "ready",
+        "page_count": 2,
+        "loaded_page_count": 2,
+        "stats_complete": True,
+        "page_type_counts": {"concept": 1, "summary": 1},
+        "page_status_counts": {"completed": 2},
+        "index_mode": "generated",
+        "has_explicit_index": False,
+        "index_slug": "__index__",
+        "default_slug": "__index__",
+        "created_at": "2026-07-02T08:00:00+08:00",
+        "updated_at": "2026-08-03T08:00:00+08:00",
+        "embed_route": "/embed/knowledge/1063edee-332a-43b4-bbd9-73bf0a708b94?wikiSlug=__index__",
     }
+    assert item["local_index"]["state"] == "not_synced"
+    assert item["local_index"]["fully_vectorized"] is False
+    assert item["owner_user_id"] is None
+    assert "weknora_id" not in item
+    assert "tenant_id" not in item
+    assert "creator_id" not in item
+    assert "storage_config" not in item
 
 
 @pytest.mark.asyncio
@@ -178,6 +273,35 @@ async def test_public_wiki_page_list_is_normalized(monkeypatch) -> None:
     assert payload["pages"][0]["aliases"] == []
 
 
+def test_public_wiki_summary_prefers_an_explicit_index_page() -> None:
+    payload = llmwiki._public_wiki_summary(
+        "mapping-one",
+        [
+            {
+                "slug": "guide/start",
+                "title": "开始使用",
+                "page_type": "article",
+                "status": "completed",
+            },
+            {
+                "slug": "home",
+                "title": "知识首页",
+                "page_type": "index",
+                "status": "completed",
+            },
+        ],
+        total=2,
+        complete=True,
+    )
+
+    assert payload["status"] == "ready"
+    assert payload["index_mode"] == "explicit"
+    assert payload["has_explicit_index"] is True
+    assert payload["index_slug"] == "home"
+    assert payload["default_slug"] == "home"
+    assert payload["embed_route"] == "/embed/knowledge/mapping-one?wikiSlug=home"
+
+
 @pytest.mark.asyncio
 async def test_public_wiki_file_is_authorized_and_streamed(monkeypatch) -> None:
     store = PublicStore(_published_row())

offline-backend-20260512/backend/tests/test_skill_knowledge_graph_writer.py

@@ -0,0 +1,62 @@
+import pytest
+
+from deerflow.skill_knowledge.graph_writer import (
+    EDGE_QUERY,
+    NODE_QUERY,
+    execute_plan,
+    parameterized_statements,
+    plan_from_ir,
+)
+
+
+class RecordingTransaction:
+    def __init__(self) -> None:
+        self.calls: list[tuple[str, dict]] = []
+
+    async def run(self, query: str, parameters: dict) -> None:
+        self.calls.append((query, parameters))
+
+
+def _plan():
+    return plan_from_ir(
+        {
+            "entities": [
+                {"local_id": "a", "type": "person", "name": "张三", "confidence": 1},
+                {"local_id": "b", "type": "system", "name": "知识库", "confidence": 1},
+            ],
+            "relations": [
+                {
+                    "source_local_id": "a",
+                    "target_local_id": "b",
+                    "predicate": "uses",
+                    "confidence": 0.9,
+                    "evidence": [{"file": "SKILL.md", "line": 2}],
+                }
+            ],
+        },
+        target_type="weknora",
+        target_id="kb-1",
+        binding_id="binding-1",
+        snapshot_id="snapshot-1",
+        job_id="job-1",
+    )
+
+
+def test_direct_graph_plan_uses_only_fixed_parameterized_templates() -> None:
+    statements = parameterized_statements(_plan())
+    assert len(statements) == 3
+    assert {row["statement"] for row in statements} == {NODE_QUERY, EDGE_QUERY}
+    assert all("parameters" in row for row in statements)
+    assert all("张三" not in row["statement"] for row in statements)
+
+
+@pytest.mark.asyncio
+async def test_direct_graph_writer_obeys_explicit_enable_switch() -> None:
+    transaction = RecordingTransaction()
+    disabled = await execute_plan(transaction, _plan(), enabled=False)
+    assert disabled == {"nodes": 0, "edges": 0}
+    assert transaction.calls == []
+
+    enabled = await execute_plan(transaction, _plan(), enabled=True)
+    assert enabled == {"nodes": 2, "edges": 1}
+    assert len(transaction.calls) == 3

offline-backend-20260512/backend/tests/test_skill_knowledge_repository.py

@@ -0,0 +1,95 @@
+import pytest
+import pytest_asyncio
+from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine
+
+from deerflow.persistence.base import Base
+from deerflow.persistence.skill_knowledge import SkillKnowledgeRepository
+
+
+@pytest_asyncio.fixture
+async def repository():
+    engine = create_async_engine("sqlite+aiosqlite:///:memory:")
+    async with engine.begin() as connection:
+        await connection.run_sync(Base.metadata.create_all)
+    yield SkillKnowledgeRepository(async_sessionmaker(engine, expire_on_commit=False))
+    await engine.dispose()
+
+
+@pytest.mark.asyncio
+async def test_matrix_job_creates_one_item_and_binding_per_pair(repository: SkillKnowledgeRepository) -> None:
+    job = await repository.create_job(
+        skill_names=["a", "b", "c"],
+        targets=[
+            {"target_type": "weknora", "target_mode": "wiki", "target_id": "kb-1", "target_name": "一"},
+            {"target_type": "assistant", "target_mode": "assistant", "target_id": "base", "target_name": "知识梳理"},
+        ],
+        created_by="admin",
+    )
+    detail = await repository.get_job(job["id"])
+    bindings = await repository.list_bindings()
+
+    assert detail is not None
+    assert detail["total_count"] == 6
+    assert len(detail["items"]) == 6
+    assert len(bindings) == 6
+
+
+@pytest.mark.asyncio
+async def test_detach_delete_only_removes_binding_contributions(repository: SkillKnowledgeRepository) -> None:
+    job = await repository.create_job(
+        skill_names=["a", "b"],
+        targets=[{"target_type": "assistant", "target_mode": "assistant", "target_id": "base", "target_name": "知识梳理"}],
+        created_by="admin",
+        review_mode="off",
+    )
+    detail = await repository.get_job(job["id"])
+    assert detail is not None
+    binding = (await repository.list_bindings(skill_names=["a"]))[0]
+    detached = await repository.detach_binding(binding["id"], delete_contributions=True)
+    assert detached and detached["status"] == "detached_deleted"
+    assert len(await repository.list_bindings()) == 1
+
+
+@pytest.mark.asyncio
+async def test_item_claim_is_atomic_and_records_worker_lease(repository: SkillKnowledgeRepository) -> None:
+    job = await repository.create_job(
+        skill_names=["a"],
+        targets=[
+            {
+                "target_type": "assistant",
+                "target_mode": "assistant",
+                "target_id": "base",
+                "target_name": "知识梳理",
+            }
+        ],
+        created_by="admin",
+    )
+    detail = await repository.get_job(job["id"])
+    item_id = detail["items"][0]["id"]
+
+    first = await repository.claim_item(item_id, owner="worker-a")
+    second = await repository.claim_item(item_id, owner="worker-b")
+
+    assert first and first["lease_owner"] == "worker-a"
+    assert first["attempts"] == 1
+    assert second is None
+
+
+@pytest.mark.asyncio
+async def test_binding_allows_only_one_active_item(repository: SkillKnowledgeRepository) -> None:
+    target = {
+        "target_type": "assistant",
+        "target_mode": "assistant",
+        "target_id": "base",
+        "target_name": "知识梳理",
+    }
+    first = await repository.create_job(skill_names=["a"], targets=[target], created_by="admin")
+    duplicate = await repository.create_job(skill_names=["a"], targets=[target], created_by="admin")
+    assert first["total_count"] == 1
+    assert duplicate["total_count"] == 0
+    assert duplicate["status"] == "completed"
+
+    detail = await repository.get_job(first["id"])
+    await repository.update_item(detail["items"][0]["id"], status="failed")
+    retry = await repository.create_job(skill_names=["a"], targets=[target], created_by="admin")
+    assert retry["total_count"] == 1

offline-backend-20260512/backend/tests/test_skill_knowledge_scanner.py

@@ -0,0 +1,52 @@
+from pathlib import Path
+from zipfile import ZipFile
+
+from deerflow.skill_knowledge import build_skill_ir, scan_skill
+
+
+def test_scanner_excludes_code_blocks_source_and_sensitive_files(tmp_path: Path) -> None:
+    (tmp_path / "SKILL.md").write_text(
+        "# 示例\n本技能使用 客户画像数据。\n```python\nPASSWORD = 'must-not-enter-ir'\n```\n",
+        encoding="utf-8",
+    )
+    (tmp_path / "worker.py").write_text("# uses hidden system\nSECRET='never-read'", encoding="utf-8")
+    (tmp_path / ".env").write_text("TOKEN=super-secret-value", encoding="utf-8")
+
+    scan = scan_skill("example", tmp_path)
+    ir = build_skill_ir(scan)
+
+    assert scan.skipped_code_count == 1
+    assert scan.blocked_files == [{"path": ".env", "reason": "疑似密钥或敏感配置文件"}]
+    assert "must-not-enter-ir" not in str(ir)
+    assert "never-read" not in str(ir)
+    assert any(relation["predicate"] == "uses" for relation in ir["relations"])
+
+
+def test_code_only_change_does_not_change_digest(tmp_path: Path) -> None:
+    (tmp_path / "SKILL.md").write_text("# Stable knowledge", encoding="utf-8")
+    code = tmp_path / "worker.py"
+    code.write_text("print(1)", encoding="utf-8")
+    before = scan_skill("example", tmp_path).source_digest
+    code.write_text("print(2)", encoding="utf-8")
+    after = scan_skill("example", tmp_path).source_digest
+    assert before == after
+
+
+def test_zip_is_inspected_and_only_safe_knowledge_members_are_read(tmp_path: Path) -> None:
+    archive = tmp_path / "materials.zip"
+    with ZipFile(archive, "w") as output:
+        output.writestr("guide.md", "# 操作手册\n仅提炼这段知识。")
+        output.writestr("worker.py", "SECRET = 'code-must-not-be-read'")
+        output.writestr("../escape.md", "path traversal")
+
+    scan = scan_skill("example", tmp_path)
+
+    assert [file.path for file in scan.files] == ["materials.zip!/guide.md"]
+    assert scan.skipped_code_count == 1
+    assert scan.blocked_files == [
+        {
+            "path": "materials.zip!/../escape.md",
+            "reason": "压缩包包含路径穿越或符号链接",
+        }
+    ]
+    assert "code-must-not-be-read" not in str(scan.manifest)