fix: restore embedded wiki images and optimize vector sync

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

@@ -37,6 +37,34 @@ const WIKI_INDEX_TYPE_LABELS: Record<string, string> = {
 
 const WIKI_INDEX_TYPE_ORDER = ["summary", "entity", "concept"];
 
+export const WIKI_PROTECTED_IMAGE_MARKER = "/__llmwiki-protected-image__";
+
+const PROTECTED_WIKI_IMAGE_SOURCE =
+  /(?:storage:\/\/[0-9A-Za-z_-]+\/)?(?:resource|local|minio|cos|tos|s3|oss|ks3|obs):\/\/[^\s"'<>\])]+/gi;
+
+/**
+ * Browsers cannot load WeKnora's protected resource:// and object-storage
+ * schemes directly. Replace them with a safe marker before Markdown
+ * sanitization; the image component resolves that marker through DeerFlow.
+ */
+export function rewriteProtectedWikiImageSources(markdown: string): string {
+  return markdown.replace(
+    PROTECTED_WIKI_IMAGE_SOURCE,
+    (filePath) => `${WIKI_PROTECTED_IMAGE_MARKER}?file_path=${encodeURIComponent(filePath)}`,
+  );
+}
+
+export function parseProtectedWikiImageSource(source: string | undefined): string | null {
+  if (!source) return null;
+  try {
+    const url = new URL(source, window.location.origin);
+    if (url.pathname !== WIKI_PROTECTED_IMAGE_MARKER) return null;
+    return url.searchParams.get("file_path")?.trim() || null;
+  } catch {
+    return null;
+  }
+}
+
 export function stripWikiFrontmatter(value: string) {
   return value.replace(/^\s*---\s*\n[\s\S]*?\n---\s*(?:\n|$)/, "").trim();
 }

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

@@ -444,6 +444,23 @@ export async function getLlmWikiDocumentPreview(
   return response.blob();
 }
 
+export async function getLlmWikiProtectedFile(
+  knowledgeBaseId: string,
+  filePath: string,
+  publicAccess = false,
+): Promise<Blob> {
+  const base = publicAccess ? PUBLIC_API() : API();
+  const params = new URLSearchParams({ file_path: filePath });
+  const response = await apiFetch(
+    `${base}/knowledge-bases/${encodeURIComponent(knowledgeBaseId)}/files?${params.toString()}`,
+  );
+  if (!response.ok) {
+    await readJson<void>(response, "读取 Wiki 图片");
+    throw new Error("读取 Wiki 图片失败");
+  }
+  return response.blob();
+}
+
 export async function getLlmWikiGraph(knowledgeBaseId: string): Promise<LlmWikiGraph> {
   return readJson(
     await apiFetch(`${API()}/knowledge-bases/${encodeURIComponent(knowledgeBaseId)}/graph?limit=200`),

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

@@ -33,6 +33,7 @@ import { reasoningPlugins } from "@/core/streamdown";
 import {
   exportableWikiMarkdown,
   renderableWikiMarkdown,
+  rewriteProtectedWikiImageSources,
   safeWikiFileName,
   stripWikiFrontmatter,
   wikiLinkMarkdown,
@@ -49,6 +50,7 @@ import {
   type LlmWikiDocument,
   type LlmWikiWikiPage,
 } from "@/strategy-components/api/llmwiki";
+import { WikiProtectedImage } from "./WikiProtectedImage";
 
 type SourceContextResponse = {
   success?: boolean;
@@ -926,10 +928,18 @@ export function WeKnoraSourceDrawer({
                     </div>
                     <MarkdownContent
                       className="max-w-none"
-                      content={preview.text}
+                      content={rewriteProtectedWikiImageSources(preview.text)}
                       isLoading={false}
                       remarkPlugins={reasoningPlugins.remarkPlugins}
                       rehypePlugins={reasoningPlugins.rehypePlugins}
+                      components={{
+                        img: (props) => (
+                          <WikiProtectedImage
+                            {...props}
+                            knowledgeBaseId={target?.knowledgeBaseId || ""}
+                          />
+                        ),
+                      }}
                     />
                   </article>
                 </section>
@@ -964,10 +974,18 @@ export function WeKnoraSourceDrawer({
                   <div className="rounded-lg border bg-background p-5">
                     <MarkdownContent
                       className="max-w-none"
-                      content={wikiDisplayContent}
+                      content={rewriteProtectedWikiImageSources(wikiDisplayContent)}
                       isLoading={false}
                       remarkPlugins={reasoningPlugins.remarkPlugins}
                       rehypePlugins={reasoningPlugins.rehypePlugins}
+                      components={{
+                        img: (props) => (
+                          <WikiProtectedImage
+                            {...props}
+                            knowledgeBaseId={target?.knowledgeBaseId || ""}
+                          />
+                        ),
+                      }}
                       linkClassName={(href) =>
                         parseWeKnoraPreviewUrl(href)?.wikiSlug
                           ? "wiki-content-link font-medium text-[#be2323] decoration-[#be2323]/55 decoration-dashed hover:decoration-solid"

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

@@ -35,6 +35,7 @@ import { markdownToDocxBlob } from "@/core/scheduled-tasks/markdown-docx";
 import {
   exportableWikiMarkdown,
   renderableWikiMarkdown,
+  rewriteProtectedWikiImageSources,
   safeWikiFileName,
   stripWikiFrontmatter,
   wikiLinkMarkdown,
@@ -49,6 +50,7 @@ import {
   usePublicLlmWikiWikiPage,
   usePublicLlmWikiWikiPages,
 } from "@/strategy-components/api/llmwiki";
+import { WikiProtectedImage } from "./WikiProtectedImage";
 
 const LIST_PATH = "/page/workspace/knowledge";
 
@@ -209,7 +211,7 @@ export function WeKnoraWikiPageView({
       : pageData?.content,
   ));
   const displayMarkdown = pageData
-    ? renderableWikiMarkdown(
+    ? rewriteProtectedWikiImageSources(renderableWikiMarkdown(
         title,
         content,
         noteId,
@@ -222,7 +224,7 @@ export function WeKnoraWikiPageView({
           : undefined,
         publicEmbed ? pagesList : [],
         publicEmbed ? publicWikiIndex.data?.groups || [] : [],
-      )
+      ))
     : "";
   const exportMarkdown = pageData
     ? wikiLinkMarkdown(exportableWikiMarkdown(title, summary, content), noteId)
@@ -497,6 +499,15 @@ export function WeKnoraWikiPageView({
                 isLoading={false}
                 remarkPlugins={reasoningPlugins.remarkPlugins}
                 rehypePlugins={reasoningPlugins.rehypePlugins}
+                components={{
+                  img: (props) => (
+                    <WikiProtectedImage
+                      {...props}
+                      knowledgeBaseId={noteId}
+                      publicAccess={publicEmbed}
+                    />
+                  ),
+                }}
                 linkClassName={(href) =>
                   publicEmbed && publicWikiTargetFromHref(href, noteId)
                     ? "wiki-content-link border-b border-dashed !border-[var(--primary)] font-medium !text-primary !no-underline hover:border-solid focus-visible:rounded-sm focus-visible:outline-2 focus-visible:outline-offset-2 focus-visible:outline-primary"

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

@@ -0,0 +1,75 @@
+import { ImageOff, Loader2 } from "lucide-react";
+import { type ImgHTMLAttributes, useEffect, useState } from "react";
+
+import { parseProtectedWikiImageSource } from "@/core/llmwiki/wiki-page-markdown";
+import { getLlmWikiProtectedFile } from "@/strategy-components/api/llmwiki";
+
+type WikiProtectedImageProps = ImgHTMLAttributes<HTMLImageElement> & {
+  knowledgeBaseId: string;
+  publicAccess?: boolean;
+};
+
+export function WikiProtectedImage({
+  knowledgeBaseId,
+  publicAccess = false,
+  src,
+  alt,
+  className,
+  ...props
+}: WikiProtectedImageProps) {
+  const filePath = parseProtectedWikiImageSource(src);
+  const [objectUrl, setObjectUrl] = useState("");
+  const [error, setError] = useState("");
+
+  useEffect(() => {
+    if (!filePath || !knowledgeBaseId) {
+      setObjectUrl("");
+      setError("");
+      return;
+    }
+    let cancelled = false;
+    let nextObjectUrl = "";
+    setObjectUrl("");
+    setError("");
+    void getLlmWikiProtectedFile(knowledgeBaseId, filePath, publicAccess)
+      .then((blob) => {
+        if (cancelled) return;
+        nextObjectUrl = URL.createObjectURL(blob);
+        setObjectUrl(nextObjectUrl);
+      })
+      .catch((reason: unknown) => {
+        if (!cancelled) {
+          setError(reason instanceof Error ? reason.message : "Wiki 图片加载失败");
+        }
+      });
+    return () => {
+      cancelled = true;
+      if (nextObjectUrl) URL.revokeObjectURL(nextObjectUrl);
+    };
+  }, [filePath, knowledgeBaseId, publicAccess]);
+
+  if (!filePath) {
+    return <img {...props} src={src} alt={alt} className={className} />;
+  }
+  if (error) {
+    return (
+      <span
+        className="my-3 flex min-h-24 items-center justify-center gap-2 rounded-lg border border-dashed bg-muted/40 px-4 text-sm text-muted-foreground"
+        data-wiki-image-error={error}
+        role="img"
+        aria-label={alt || "Wiki 图片加载失败"}
+      >
+        <ImageOff className="size-5" />
+        {alt || "Wiki 图片加载失败"}
+      </span>
+    );
+  }
+  if (!objectUrl) {
+    return (
+      <span className="my-3 flex min-h-24 items-center justify-center rounded-lg bg-muted/30 text-muted-foreground">
+        <Loader2 className="size-5 animate-spin" aria-label="正在加载 Wiki 图片" />
+      </span>
+    );
+  }
+  return <img {...props} src={objectUrl} alt={alt} className={className} />;
+}

offline-backend-20260512/backend/CLAUDE.md

@@ -1016,6 +1016,11 @@ Both can be modified at runtime via Gateway API endpoints or `DeerFlowClient` me
 `llmwiki.weknora.api_base_url` is the provider switch: non-empty enables WeKnora, empty preserves the legacy LLMWiki UI. WeKnora is independently deployed and owns PostgreSQL/Redis/object storage/vector storage/parsers/models; DeerFlow config contains only `api_base_url` and optional `web_base_url`. DeerFlow deliberately sends no WeKnora credential, so the WeKnora endpoint must be restricted to a trusted network and allow DeerFlow traffic itself or through its reverse proxy. There is intentionally no `default_embedding_model_id`: `WeKnoraClient` queries `/api/v1/models` and uses WeKnora's active Embedding model when creating a KB; Wiki creation additionally resolves an active KnowledgeQA model inside WeKnora for synthesis. The native BFF is `app/gateway/routers/llmwiki.py`; ownership/publication mappings live in `deerflow.persistence.llmwiki`; the browser only sees DeerFlow mapping IDs. Knowledge-base owners publish immediately (`private` -> `published`) without a review queue, and can unpublish ordinary bases. The conversation-deposit KB is system-owned, always published, shared by all DeerFlow users, excluded from My Knowledge, and immutable through user/admin detail operations; recognition requires both the reserved name and `system` owner. Existing same-name remote mappings are adopted in place as the system deposit, preserving mapping ids and document links. Its user-facing description, generated Markdown source and tags use `cmzs` branding; list/detail reads synchronize stale remote descriptions, and search results, source previews and embedded-detail JSON recursively replace legacy brand text in historical conversation-deposit payloads. Deposit requests must reference a thread accessible to the current account. Personal knowledge-base list/search views are scoped to the current DeerFlow account even for admins; frontend LLMWiki caches include the live account id, and admin write power does not make other users' private KBs appear as owned. Only `system_role === "admin"` sees the WeKnora connection-config tab. Knowledge cards navigate to a dedicated WeKnora-style detail route. Wiki-enabled bases render and default to `Wiki -> documents -> graph`; standard bases omit Wiki and render `documents -> graph`. The detail page supports sequential multi-file upload with an aggregate result for writable ordinary bases, hides it for public read-only/system-deposit bases, and uses a generic loading label while the embed session starts. It also supports URL and published manual-Markdown imports, browses real Wiki pages/content, proxies authenticated original-file previews, pages through `/chunks/{knowledge_id}`, and renders the Wiki page graph from `/knowledgebase/{kb_id}/wiki/graph`; document details use a right sheet rather than the removed KB modal. The credential-free proxy uses one WeKnora service identity, so per-user separation is enforced by DeerFlow mappings while all remote KBs physically share that identity's WeKnora workspace. WeKnora iframe detail pages first call `/api/llmwiki/knowledge-bases/{id}/weknora/session` through the normal DeerFlow API auth path, then load `/platform/knowledge-bases/{weknora_id}` with a signed `deerflow_weknora_embed` cookie for `/platform`, `/assets`, `/locales`, root assets, and API calls. Injected fetch/XHR rewriting sends all iframe `/api/v1/*`, including colliding `/api/v1/auth/*`, through `/api/llmwiki/weknora-embed/api/v1/*`; Gateway auth/CSRF bypass only applies to those signed iframe proxy requests, the proxy still revalidates the mapped KB, and cookie security honors `X-Forwarded-Proto` for HTTPS reverse proxies. `llmwiki_search` is conditionally registered in `tools.py`, is read-only, and rechecks the current DeerFlow user, agent `llmwiki_knowledge_base_ids`, and thread-context selection on every call. Source previews prefer the cleaned Wiki/LLMWiki page text when a retrieved chunk maps to a Wiki page, with raw split chunks as the fallback. Agent bindings use an inline WeKnora-style searchable/grouped multi-select in the standard agent create/detail UI; new chats use the same interaction pattern as a compact composer dropdown, and selections are written to thread metadata/context and restored on reopen. The DeerFlow frontend is native React and the WeKnora frontend is not modified or embedded. Full deployment and behavior notes: `docs/LLMWIKI_WEKNORA_INTEGRATION_ZH.md`. Tests: `tests/test_llmwiki_weknora.py`.
 
 The embedded detail proxy hides WeKnora's outer `.main > .aside_box` application sidebar and expands the route outlet, but deliberately keeps the Wiki page's internal directory/index sidebar. Do not add an HTML `sandbox` attribute to this trusted, permission-filtered detail iframe: a sandboxed ancestor causes Chromium's built-in Blob PDF viewer to render the browser-blocked page. The Gateway follows upstream redirects only for `/knowledge/{id}/preview` and `/knowledge-bases/{id}/files`, keeping PDF bytes and protected Wiki images inside the signed proxy instead of sending the browser to WeKnora object storage.
+Native/private and public Wiki readers must rewrite protected Markdown image
+sources to their mapping-scoped DeerFlow `/files?file_path=` endpoints and load
+them as Blob URLs. The embedded guard also rewrites protected fetch/XHR values
+and hydrates late `img[data-protected-src]` placeholders; never expose the
+service token or relax the current-KB authorization to make images work.
 
 **Embedded native knowledge-base switching:** The original WeKnora breadcrumb picker remains available inside the detail iframe. A signed 15-minute embed cookie carries only the current DeerFlow user's visible mappings (their own plus published bases); the proxied `/api/v1/knowledge-bases` response is filtered to that allowlist. Choosing another entry performs a full iframe navigation, rechecks the DeerFlow mapping and read/write permission, then reissues the cookie for the selected base. Never expose the upstream service-admin list directly or permit an arbitrary remote knowledge-base ID.
 
@@ -1442,8 +1447,10 @@ detail. Never call `/knowledge-search` from internal search, RAG, or the
 `deerflow.integrations.weknora.local_index` and
 `deerflow.persistence.llmwiki_index`; preserve the harness → app import
 firewall. Gateway composition injects the store/search/sync services through
-`local_index.runtime`, and the lease-protected scheduler starts only when both
-`enabled` and `sync_enabled` are true.
+`local_index.runtime`. `enabled` turns on local vector search and manual
+vectorization; `auto_sync` is the separate opt-in for the lease-protected
+scheduler. The scheduler sleeps for one full configured interval before its
+first scan, so application startup must never trigger immediate vectorization.
 
 Vectors are normalized little-endian float32 BLOBs. A page mirror and all of
 its vectors are replaced in one transaction only after strict batched
@@ -1456,6 +1463,10 @@ input, not an outbound API field: fixed-dimension BGE/OpenAI-compatible
 endpoints can reject a `dimensions` request parameter with HTTP 400. When the
 value is omitted, the embedding client probes and freezes the first response's
 dimension before sync lease/rebuild decisions.
+Embedding errors must retain the upstream status and bounded response body in
+logs, without exposing API keys. SQL vector writes are split into statements
+of at most `database_batch_size` rows (default 50) inside the same atomic page
+replacement transaction.
 During a fingerprint rebuild the mapping uses the Wiki-page API until every
 page is successfully rebuilt. `WikiRetrievalService` partitions mixed requests
 per mapping, so ready libraries stay local while incomplete libraries use that

offline-backend-20260512/backend/README.md

@@ -777,15 +777,24 @@ processed Wiki content. A knowledge base uses exact NumPy cosine similarity
 only after its whole Wiki has completed one coherent vectorization pass; until
 then internal search calls WeKnora's Wiki-page search/list API and reads the
 matched Wiki pages. It never falls back to raw documents or chunks. The local
-vector feature is disabled by default. Configure
-`llmwiki.local_wiki_index.embedding` first, then enable `enabled`,
-`sync_enabled`, and `internal_search_enabled` in `config.yaml`.
+vector feature is disabled by default. Configure the three
+`llmwiki.local_wiki_index.embedding` values (`model`, `base_url`, and
+`api_key`), then change only `enabled` to `true` in `config.yaml`. Manual
+vectorization is the default. Set `auto_sync: true` only when periodic
+background synchronization is wanted; the scheduler waits one full interval
+after startup before its first scan, so boot does not trigger a rebuild.
+`database_batch_size` caps each vector insert statement (default `50`) while
+the page replacement remains atomic, preventing a large Wiki from flooding
+the SQL connection in one statement.
 The optional `embedding.dimensions` value is used only to validate the returned
 vector size and build the index fingerprint; it is deliberately not sent in
 the embedding request because fixed-dimension BGE/OpenAI-compatible endpoints
 may reject that extra field with HTTP 400. Omit it to discover the dimension
 from the first successful response.
 
+Embedding failures log the upstream HTTP status and a bounded response body.
+The client never sends a `dimensions` request parameter.
+
 The migration creates `llmwiki_wiki_pages`, `llmwiki_wiki_vectors`, and
 `llmwiki_wiki_sync_states`. Wiki Markdown and normalized float32 vectors are
 stored in DeerFlow's configured SQL database. Multi-worker sync is protected by
@@ -839,6 +848,13 @@ 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`.
+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
+readers use the corresponding `/api/public/llmwiki/...` route, which requires a
+published mapping. The embedded WeKnora guard applies the same mapping-scoped
+proxy to document previews and Markdown images, including images inserted after
+the drawer/page first renders.
 
 Wiki management also provides **导出 Excel**. `GET
 /api/llmwiki/knowledge-bases/{id}/wiki/export.xlsx` exports every processed Wiki

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

@@ -998,7 +998,7 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
         local_wiki_index = app.state.config.llmwiki.local_wiki_index
         if (
             local_wiki_index.enabled
-            and local_wiki_index.sync_enabled
+            and local_wiki_index.auto_sync
             and getattr(app.state, "llmwiki_sync_service", None) is not None
         ):
             from app.gateway.llmwiki_index_scheduler import local_wiki_index_scheduler_loop

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

@@ -321,13 +321,16 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
         from deerflow.integrations.weknora.local_index.runtime import register_local_wiki_index_runtime
         from deerflow.persistence.llmwiki_index import make_llmwiki_index_store
 
-        app.state.llmwiki_index_store = make_llmwiki_index_store(sf)
+        local_index_config = config.llmwiki.local_wiki_index
+        app.state.llmwiki_index_store = make_llmwiki_index_store(
+            sf,
+            write_batch_size=local_index_config.database_batch_size,
+        )
         app.state.llmwiki_embedding = None
         app.state.llmwiki_vector_cache = None
         app.state.llmwiki_vector_search = None
         app.state.llmwiki_sync_service = None
         app.state.llmwiki_retrieval_service = None
-        local_index_config = config.llmwiki.local_wiki_index
         from deerflow.integrations.weknora.runtime import build_weknora_client, get_resolved_llmwiki_runtime
 
         resolved_llmwiki = get_resolved_llmwiki_runtime(config)

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

@@ -48,6 +48,10 @@ async def _sync_once(app: FastAPI) -> None:
 
 async def local_wiki_index_scheduler_loop(app: FastAPI) -> None:
     interval = app.state.config.llmwiki.local_wiki_index.sync_interval_seconds
+    logger.info(
+        "Local Wiki auto-sync scheduler started; first scan delayed by %s seconds",
+        interval,
+    )
     while True:
-        await _sync_once(app)
         await asyncio.sleep(interval)
+        await _sync_once(app)

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

@@ -61,6 +61,10 @@ router = APIRouter(tags=["llmwiki"])
 proxy_router = APIRouter(tags=["llmwiki-proxy"])
 
 _MAX_UPLOAD_BYTES = 100 * 1024 * 1024
+_PROTECTED_WIKI_FILE_PATH = re.compile(
+    r"^(?:storage://[0-9A-Za-z_-]+/)?(?:resource|local|minio|cos|tos|s3|oss|ks3|obs)://",
+    re.IGNORECASE,
+)
 _WEKNORA_ADMIN_SESSION_CACHE: dict[str, dict[str, Any]] = {}
 _WEKNORA_PUBLIC_PREFIX = "/deerflow"
 _WEKNORA_PUBLIC_ROOT_PATHS = (
@@ -242,7 +246,7 @@ def _store(request: Request) -> LlmWikiStore:
 def _schedule_local_wiki_sync(request: Request, mapping: dict[str, Any]) -> None:
     service = getattr(request.app.state, "llmwiki_sync_service", None)
     config = request.app.state.config.llmwiki.local_wiki_index
-    if service is not None and config.enabled and mapping.get("wiki_index_enabled", True):
+    if service is not None and config.enabled and config.auto_sync and mapping.get("wiki_index_enabled", True):
         from app.gateway.llmwiki_index_scheduler import spawn_local_wiki_index_job
 
         spawn_local_wiki_index_job(request.app, service.sync_mapping(mapping, force=True))
@@ -1085,6 +1089,8 @@ html.deerflow-source-detail-open [data-deerflow-source-detail-panel="true"] [cla
 
   function rewriteApiUrl(value) {{
     try {{
+      const protectedUrl = protectedImageApiUrl(value);
+      if (protectedUrl) return protectedUrl;
       const next = new URL(value, location.origin);
       if (next.origin !== location.origin) return value;
       const publicPrefix = cfg.publicPrefix || "";
@@ -1104,6 +1110,19 @@ html.deerflow-source-detail-open [data-deerflow-source-detail-panel="true"] [cla
       return value;
     }}
   }}
+  function protectedImageApiUrl(value) {{
+    const raw = String(value || "").trim();
+    if (!/^(?:storage:\\/\\/[0-9A-Za-z_-]+\\/)?(?:resource|local|minio|cos|tos|s3|oss|ks3|obs):\\/\\//i.test(raw)) {{
+      return "";
+    }}
+    const endpoint =
+      cfg.apiPrefix +
+      "/api/v1/knowledge-bases/" +
+      encodeURIComponent(cfg.weknoraId) +
+      "/files?file_path=" +
+      encodeURIComponent(raw);
+    return new URL(endpoint, location.origin).href;
+  }}
   const nativeFetch = window.fetch.bind(window);
   window.fetch = (input, init) => {{
     if (input instanceof Request) {{
@@ -1116,6 +1135,67 @@ html.deerflow-source-detail-open [data-deerflow-source-detail-panel="true"] [cla
     return nativeXhrOpen.call(this, method, rewriteApiUrl(url), ...rest);
   }};
 
+  // WeKnora renders protected Markdown images as transparent placeholders and
+  // hydrates them asynchronously.  In an embedded page that hydration can run
+  // before its own API client has picked up our proxy prefix, leaving a 1x1
+  // GIF forever.  Resolve the same protected source through the scoped proxy
+  // as a fallback.  The backend validates the scheme and current KB again.
+  function hydrateProtectedImage(image) {{
+    const protectedSrc = String(image?.getAttribute?.("data-protected-src") || "").trim();
+    const proxyUrl = protectedImageApiUrl(protectedSrc);
+    if (!proxyUrl) return;
+    if (
+      image.dataset.deerflowProtectedSource === protectedSrc &&
+      String(image.getAttribute("src") || "").startsWith("blob:")
+    ) return;
+    const attempts = Number(image.dataset.deerflowProtectedAttempts || "0");
+    if (attempts >= 3 || image.dataset.deerflowProtectedLoading === "1") return;
+    image.dataset.deerflowProtectedLoading = "1";
+    image.dataset.deerflowProtectedAttempts = String(attempts + 1);
+    nativeFetch(proxyUrl, {{ credentials: "same-origin", headers: {{ Accept: "image/*" }} }})
+      .then((response) => {{
+        if (!response.ok) throw new Error(`protected image request failed (${{response.status}})`);
+        return response.blob();
+      }})
+      .then((blob) => {{
+        const objectUrl = URL.createObjectURL(blob);
+        image.dataset.deerflowProtectedSource = protectedSrc;
+        image.removeAttribute("data-img-loading");
+        image.removeAttribute("data-wiki-image-error");
+        image.src = objectUrl;
+        image.addEventListener("load", () => URL.revokeObjectURL(objectUrl), {{ once: true }});
+      }})
+      .catch(() => {{
+        image.dataset.wikiImageError = "true";
+        window.setTimeout(() => {{
+          delete image.dataset.deerflowProtectedLoading;
+          hydrateProtectedImage(image);
+        }}, 1200 * (attempts + 1));
+      }})
+      .finally(() => {{
+        delete image.dataset.deerflowProtectedLoading;
+      }});
+  }}
+  function hydrateProtectedImages(scope = document) {{
+    if (scope?.matches?.("img[data-protected-src]")) hydrateProtectedImage(scope);
+    scope?.querySelectorAll?.("img[data-protected-src]").forEach(hydrateProtectedImage);
+  }}
+  const protectedImageObserver = new MutationObserver((mutations) => {{
+    mutations.forEach((mutation) => {{
+      if (mutation.type === "attributes") hydrateProtectedImage(mutation.target);
+      mutation.addedNodes?.forEach((node) => {{
+        if (node?.nodeType === Node.ELEMENT_NODE) hydrateProtectedImages(node);
+      }});
+    }});
+  }});
+  protectedImageObserver.observe(document.documentElement, {{
+    childList: true,
+    subtree: true,
+    attributes: true,
+    attributeFilter: ["data-protected-src"]
+  }});
+  hydrateProtectedImages();
+
   function tone(color, alpha) {{
     return `color-mix(in srgb, ${{color}} ${{Math.round(alpha * 100)}}%, transparent)`;
   }}
@@ -1519,6 +1599,7 @@ html.deerflow-source-detail-open [data-deerflow-source-detail-panel="true"] [cla
     enforceLocation();
     purgeGuides();
     hideWeKnoraShell();
+    hydrateProtectedImages();
     if (tickSourceDetail() && sourceDetailMode) {{
       clearInterval(maintenanceTimer);
       try {{ readyObserver.disconnect(); }} catch (_) {{}}
@@ -1820,6 +1901,32 @@ async def _public_mapping(request: Request, mapping_id: str) -> dict[str, Any]:
     return row
 
 
+def _validate_protected_wiki_file_path(file_path: str) -> str:
+    value = file_path.strip()
+    if not value or "\x00" in value or not _PROTECTED_WIKI_FILE_PATH.match(value):
+        raise HTTPException(status_code=422, detail="Unsupported Wiki image path")
+    return value
+
+
+async def _wiki_file_response(row: dict[str, Any], file_path: str, *, public: bool) -> Response:
+    try:
+        content, media_type = await _client_or_503().get_knowledge_base_file(
+            str(row["weknora_id"]),
+            _validate_protected_wiki_file_path(file_path),
+        )
+    except WeKnoraError as exc:
+        _raise_upstream(exc)
+    return Response(
+        content=content,
+        media_type=media_type,
+        headers={
+            "Cache-Control": "public, max-age=300" if public else "private, max-age=60",
+            "Content-Disposition": "inline",
+            "X-Content-Type-Options": "nosniff",
+        },
+    )
+
+
 async def _verify_document(client: WeKnoraClient, row: dict[str, Any], document_id: str) -> dict[str, Any]:
     document = await client.get_document(document_id)
     if str(document.get("knowledge_base_id") or "") != str(row["weknora_id"]):
@@ -2510,6 +2617,18 @@ async def list_public_wiki_pages(
     return {**result, "pages": pages}
 
 
+@router.get("/api/public/llmwiki/knowledge-bases/{mapping_id}/files")
+async def get_public_wiki_file(
+    request: Request,
+    mapping_id: str,
+    file_path: str = Query(..., min_length=1, max_length=8192),
+) -> Response:
+    """Serve one protected image belonging to a published Wiki."""
+
+    row = await _public_mapping(request, mapping_id)
+    return await _wiki_file_response(row, file_path, public=True)
+
+
 _PUBLIC_WIKI_INDEX_TYPES = ("summary", "entity", "concept", "synthesis", "comparison")
 
 
@@ -2820,6 +2939,18 @@ async def preview_document(request: Request, mapping_id: str, document_id: str)
     )
 
 
+@router.get("/api/llmwiki/knowledge-bases/{mapping_id}/files")
+async def get_wiki_file(
+    request: Request,
+    mapping_id: str,
+    file_path: str = Query(..., min_length=1, max_length=8192),
+) -> Response:
+    """Serve one protected Wiki image after normal knowledge-base authorization."""
+
+    row, _, _ = await _authorized_mapping(request, mapping_id, write=False)
+    return await _wiki_file_response(row, file_path, public=False)
+
+
 @router.get("/api/llmwiki/knowledge-bases/{mapping_id}/graph")
 async def get_knowledge_base_graph(
     request: Request,

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

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import logging
 from typing import Any
 from uuid import uuid4
 
@@ -14,6 +15,7 @@ from deerflow.integrations.weknora.local_index.retrieval import is_fully_vectori
 from deerflow.integrations.weknora.local_index.search import WikiIndexError
 
 router = APIRouter(prefix="/api/llmwiki", tags=["llmwiki-index"])
+logger = logging.getLogger(__name__)
 
 
 class WikiVectorSearchRequest(BaseModel):
@@ -181,6 +183,7 @@ async def _run_sync(app, mapping_ids: list[str], force: bool) -> None:
             try:
                 await service.sync_mapping(mapping, force=force)
             except Exception:
+                logger.exception("Manual Wiki vectorization failed mapping_id=%s", mapping.get("id"))
                 continue
 
 
@@ -191,6 +194,7 @@ async def _run_one_sync(app, mapping: dict[str, Any], *, force: bool) -> None:
     try:
         await service.sync_mapping(mapping, force=force)
     except Exception:
+        logger.exception("Manual Wiki vectorization failed mapping_id=%s", mapping.get("id"))
         return
 
 

offline-backend-20260512/backend/config.yaml

@@ -556,47 +556,17 @@ llmwiki:
     web_base_url: "http://119.254.86.251:8088"
     admin_email: "lqq@qq.com"
     admin_password: "1qaz@WSX"
-  # DeerFlow-local Wiki mirror/vector index. Keep disabled until the
-  # OpenAI-compatible embedding endpoint below is configured and reachable.
+  # 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。
   local_wiki_index:
     enabled: false
-    sync_enabled: false
-    internal_search_enabled: false
-    reference_ui_enabled: false
-    sync_interval_seconds: 120
-    chunk_max_chars: 1000
-    chunk_overlap_chars: 120
-    chunk_min_chars: 80
+    auto_sync: false
+    database_batch_size: 50
     embedding:
-      enabled: true
-      provider: openai
       model: bge-m3
       base_url: ""
       api_key: ""
-      # Expected BGE-M3 response size for local validation/indexing only.
-      # This value is not sent as an embedding API request parameter.
-      dimensions: 1024
-      batch_size: 32
-      timeout_seconds: 30
-      max_retries: 3
-    engine: numpy_exact
-    strict_vector: true
-    top_k_sections: 30
-    top_k_pages: 8
-    max_sections_per_page: 2
-    min_similarity: 0.35
-    cache_max_knowledge_bases: 20
-    cache_revision_check_seconds: 5
-    external_api:
-      enabled: false
-      api_key: ""
-      previous_api_key: ""
-      max_query_chars: 1000
-      max_knowledge_bases: 20
-      max_results: 50
-      default_results: 10
-      include_content_max_chars: 12000
-      requests_per_minute: 60
 
 log_system:
   enabled: true

offline-backend-20260512/backend/docs/LLMWIKI_DEERFLOW_LOCAL_VECTOR_INDEX_ZH.md

@@ -279,65 +279,25 @@ llmwiki:
 
   local_wiki_index:
     enabled: true
-
-    # 同步
-    sync_enabled: true
-    sync_interval_seconds: 120
-    sync_page_size: 100
-    sync_concurrency: 3
-    sync_lease_seconds: 600
-    sync_request_timeout_seconds: 30
-    sync_max_retries: 3
-    sync_retry_backoff_seconds: 5
-
-    # 切分
-    chunk_max_chars: 1000
-    chunk_overlap_chars: 120
-    chunk_min_chars: 80
-    title_prefix_enabled: true
-
-    # Embedding：OpenAI-compatible /v1/embeddings
+    # 默认只允许手动向量化；需要定时增量同步时再开启。
+    auto_sync: false
+    # 每条 SQL 最多插入的向量行数；整页替换仍在同一事务内完成。
+    database_batch_size: 50
     embedding:
-      enabled: true
-      provider: openai
       model: bge-m3
       base_url: "$WIKI_EMBEDDING_BASE_URL"
       api_key: "$WIKI_EMBEDDING_API_KEY"
-      dimensions: 1024
-      batch_size: 32
-      timeout_seconds: 30
-      max_retries: 3
-
-    # 检索
-    engine: numpy_exact
-    strict_vector: true
-    top_k_sections: 30
-    top_k_pages: 8
-    max_sections_per_page: 2
-    min_similarity: 0.35
-    keyword_boost_enabled: false
-    keyword_boost_weight: 0.15
-    cache_max_knowledge_bases: 20
-    cache_revision_check_seconds: 5
-
-    # 外部接口
-    external_api:
-      enabled: false
-      api_key: "$LLMWIKI_EXTERNAL_SEARCH_API_KEY"
-      max_query_chars: 1000
-      max_knowledge_bases: 20
-      max_results: 50
-      default_results: 10
-      include_content_max_chars: 12000
-      requests_per_minute: 60
 ```
 
+日常部署只需配置上面三个 Embedding 值并将 `enabled` 改为 `true`。
+`auto_sync=false` 时仅由页面“手动向量化”触发；即使设为 `true`，定时器也会先等待一个完整同步周期，不会在服务刚启动时立即重建索引。其余切分、检索、超时与重试参数继续使用代码默认值，只有确有调优需要时才覆盖。
+
 ### 6.1 配置校验规则
 
 启动时应执行：
 
 1. `enabled=true` 时，Embedding `base_url` 和 `model` 必须非空；
-2. `dimensions` 可省略，但第一次成功 Embedding 后必须固定并写入索引 profile；
+2. 不向上游发送 `dimensions` 参数；第一次成功 Embedding 后从响应自动发现维度，并固定到索引 profile；
 3. `chunk_overlap_chars < chunk_max_chars`；
 4. `top_k_pages <= top_k_sections`；
 5. `external_api.enabled=true` 时必须配置非空 API Key；
@@ -1885,9 +1845,7 @@ class WikiVectorEngine(ABC):
 
 ```text
 local_wiki_index.enabled
-local_wiki_index.sync_enabled
-local_wiki_index.internal_search_enabled
-local_wiki_index.reference_ui_enabled
+local_wiki_index.auto_sync
 local_wiki_index.external_api.enabled
 ```
 

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

@@ -59,7 +59,7 @@ class LocalWikiEmbeddingConfig(BaseModel):
     base_url: str = ""
     api_key: str = Field(default="", repr=False)
     dimensions: int | None = Field(
-        default=1024,
+        default=None,
         ge=1,
         le=65536,
         description="Expected response vector dimensions; not sent to the embedding endpoint.",
@@ -108,9 +108,7 @@ class LocalWikiIndexConfig(BaseModel):
     """DeerFlow-owned mirror and exact-vector index for WeKnora Wiki pages."""
 
     enabled: bool = False
-    sync_enabled: bool = False
-    internal_search_enabled: bool = False
-    reference_ui_enabled: bool = False
+    auto_sync: bool = False
     sync_interval_seconds: int = Field(default=120, ge=10, le=86400)
     sync_page_size: int = Field(default=100, ge=1, le=500)
     sync_concurrency: int = Field(default=3, ge=1, le=32)
@@ -133,9 +131,31 @@ class LocalWikiIndexConfig(BaseModel):
     keyword_boost_weight: float = Field(default=0.15, ge=0, le=1)
     cache_max_knowledge_bases: int = Field(default=20, ge=1, le=1000)
     cache_revision_check_seconds: float = Field(default=5, ge=0, le=3600)
+    database_batch_size: int = Field(default=50, ge=1, le=500)
     external_api: LocalWikiExternalApiConfig = Field(default_factory=LocalWikiExternalApiConfig)
     model_config = ConfigDict(extra="ignore")
 
+    @model_validator(mode="before")
+    @classmethod
+    def _migrate_legacy_flags(cls, value):
+        if isinstance(value, dict) and "auto_sync" not in value and "sync_enabled" in value:
+            value = {**value, "auto_sync": value.get("sync_enabled")}
+        return value
+
+    @property
+    def sync_enabled(self) -> bool:
+        """Backward-compatible status name; automatic work is one explicit switch."""
+
+        return self.auto_sync
+
+    @property
+    def internal_search_enabled(self) -> bool:
+        return self.enabled
+
+    @property
+    def reference_ui_enabled(self) -> bool:
+        return self.enabled
+
     @model_validator(mode="after")
     def _validate_index(self):
         if self.chunk_overlap_chars >= self.chunk_max_chars:

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

@@ -2,8 +2,8 @@
 
 from __future__ import annotations
 
-from collections.abc import Mapping, Sequence
 import time
+from collections.abc import Mapping, Sequence
 from typing import Any
 from urllib.parse import quote
 
@@ -430,6 +430,51 @@ class WeKnoraClient:
         media_type = response.headers.get("content-type", "application/octet-stream").split(";", 1)[0].strip()
         return response.content, media_type or "application/octet-stream"
 
+    async def get_knowledge_base_file(
+        self,
+        knowledge_base_id: str,
+        file_path: str,
+    ) -> tuple[bytes, str]:
+        """Return one protected Wiki attachment using DeerFlow's WeKnora identity."""
+
+        owns_client = self._http_client is None
+        client = self._http_client or httpx.AsyncClient(
+            timeout=httpx.Timeout(self.timeout_seconds),
+            follow_redirects=True,
+            trust_env=False,
+        )
+        try:
+            for attempt in range(2):
+                response = await client.request(
+                    "GET",
+                    f"{self.base_url}/api/v1/knowledge-bases/{quote(knowledge_base_id, safe='')}/files",
+                    params={"file_path": file_path},
+                    headers=await self._headers(force_refresh=attempt > 0, accept="*/*"),
+                    follow_redirects=True,
+                )
+                if response.status_code not in {401, 403} or not self._has_admin_credentials or attempt > 0:
+                    break
+        except httpx.TimeoutException as exc:
+            raise WeKnoraError("WeKnora Wiki image request timed out", status_code=504) from exc
+        except httpx.HTTPError as exc:
+            raise WeKnoraError("WeKnora Wiki image is unavailable", status_code=502) from exc
+        finally:
+            if owns_client:
+                await client.aclose()
+        if response.status_code == 404:
+            raise WeKnoraError("WeKnora Wiki image was not found", status_code=404)
+        if response.status_code in {401, 403}:
+            if self._has_admin_credentials:
+                raise WeKnoraError("WeKnora rejected DeerFlow's authenticated Wiki image request", status_code=502)
+            raise WeKnoraError(
+                "WeKnora rejected DeerFlow's anonymous internal request; allow DeerFlow in WeKnora or its reverse proxy",
+                status_code=502,
+            )
+        if response.status_code >= 400:
+            raise WeKnoraError(f"WeKnora returned HTTP {response.status_code}", status_code=502)
+        media_type = response.headers.get("content-type", "application/octet-stream").split(";", 1)[0].strip()
+        return response.content, media_type or "application/octet-stream"
+
     async def reprocess_document(self, knowledge_id: str) -> dict[str, Any]:
         value = _data(await self._request("POST", f"/api/v1/knowledge/{quote(knowledge_id, safe='')}/reparse"))
         return dict(value) if isinstance(value, dict) else {}

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

@@ -15,6 +15,21 @@ from deerflow.integrations.weknora.local_index.vector_codec import normalize_vec
 logger = logging.getLogger(__name__)
 
 
+def _upstream_error_detail(response: httpx.Response) -> str:
+    """Return a bounded upstream reason without hiding the useful response body."""
+
+    try:
+        payload = response.json()
+        if isinstance(payload, dict):
+            detail = payload.get("detail") or payload.get("message") or payload.get("error") or payload
+        else:
+            detail = payload
+        value = str(detail)
+    except (ValueError, TypeError):
+        value = response.text
+    return " ".join(value.split())[:2000] or "no response body"
+
+
 class WikiEmbeddingError(RuntimeError):
     def __init__(self, message: str, *, category: str = "request", retryable: bool = True) -> None:
         super().__init__(message)
@@ -76,11 +91,15 @@ class StrictWikiEmbeddingClient:
             started = monotonic()
             try:
                 response = await (await self._get_client()).post(url, headers=headers, json=payload)
-                if response.status_code in {401, 403}:
-                    raise WikiEmbeddingError("Embedding authentication failed", category="authentication", retryable=False)
-                if response.status_code == 429 or response.status_code >= 500:
-                    raise WikiEmbeddingError(f"Embedding service returned HTTP {response.status_code}", category="upstream", retryable=True)
-                response.raise_for_status()
+                if response.status_code >= 400:
+                    detail = _upstream_error_detail(response)
+                    category = "authentication" if response.status_code in {401, 403} else "upstream"
+                    retryable = response.status_code == 429 or response.status_code >= 500
+                    raise WikiEmbeddingError(
+                        f"Embedding service returned HTTP {response.status_code}: {detail}",
+                        category=category,
+                        retryable=retryable,
+                    )
                 data = response.json()
                 items = sorted(data.get("data", []), key=lambda item: int(item.get("index", 0))) if isinstance(data, dict) else []
                 if len(items) != len(texts):
@@ -98,8 +117,20 @@ class StrictWikiEmbeddingClient:
             except httpx.TimeoutException:
                 last_error = WikiEmbeddingError("Embedding request timed out", category="timeout", retryable=True)
             except (httpx.HTTPError, ValueError, TypeError) as exc:
-                last_error = WikiEmbeddingError(f"Embedding request failed ({type(exc).__name__})", category="request", retryable=isinstance(exc, httpx.HTTPError))
+                reason = " ".join(str(exc).split())[:2000] or type(exc).__name__
+                last_error = WikiEmbeddingError(
+                    f"Embedding request failed ({type(exc).__name__}): {reason}",
+                    category="request",
+                    retryable=isinstance(exc, httpx.HTTPError),
+                )
             if last_error is not None and (not last_error.retryable or attempt >= self.config.max_retries):
+                logger.error(
+                    "Wiki embedding batch failed model=%s batch=%d category=%s reason=%s",
+                    self.config.model,
+                    len(texts),
+                    last_error.category,
+                    last_error,
+                )
                 raise last_error
             await asyncio.sleep(min(30.0, 0.5 * (2**attempt)))
         raise last_error or WikiEmbeddingError("Embedding request failed")

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

@@ -6,8 +6,12 @@ from deerflow.persistence.llmwiki_index.model import LlmWikiWikiPageRow, LlmWiki
 from deerflow.persistence.llmwiki_index.sql import SqlLlmWikiIndexStore
 
 
-def make_llmwiki_index_store(session_factory):
-    return MemoryLlmWikiIndexStore() if session_factory is None else SqlLlmWikiIndexStore(session_factory)
+def make_llmwiki_index_store(session_factory, *, write_batch_size: int = 50):
+    return (
+        MemoryLlmWikiIndexStore()
+        if session_factory is None
+        else SqlLlmWikiIndexStore(session_factory, write_batch_size=write_batch_size)
+    )
 
 
 __all__ = [

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

@@ -6,7 +6,7 @@ from datetime import UTC, datetime, timedelta
 from typing import Any
 from uuid import uuid4
 
-from sqlalchemy import case, delete, func, or_, select, update
+from sqlalchemy import case, delete, func, insert, or_, select, update
 from sqlalchemy.exc import IntegrityError
 from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
 
@@ -27,8 +27,9 @@ def _dict(row) -> dict[str, Any]:
 
 
 class SqlLlmWikiIndexStore(LlmWikiIndexStore):
-    def __init__(self, session_factory: async_sessionmaker[AsyncSession]) -> None:
+    def __init__(self, session_factory: async_sessionmaker[AsyncSession], *, write_batch_size: int = 50) -> None:
         self._sf = session_factory
+        self.write_batch_size = max(1, min(int(write_batch_size), 500))
 
     async def _ensure_state(self, session: AsyncSession, mapping_id: str) -> LlmWikiWikiSyncStateRow:
         row = await session.get(LlmWikiWikiSyncStateRow, mapping_id)
@@ -82,24 +83,29 @@ class SqlLlmWikiIndexStore(LlmWikiIndexStore):
             row.updated_at = now
             await session.flush()
             await session.execute(delete(LlmWikiWikiVectorRow).where(LlmWikiWikiVectorRow.page_id == row.id))
-            session.add_all(
-                [
-                    LlmWikiWikiVectorRow(
-                        id=str(uuid4()),
-                        page_id=row.id,
-                        knowledge_base_mapping_id=mapping_id,
-                        section_index=int(item["section_index"]),
-                        heading=item.get("heading"),
-                        section_content=str(item["section_content"]),
-                        content_hash=str(item["content_hash"]),
-                        embedding_fingerprint=fingerprint,
-                        embedding_dimensions=int(item["embedding_dimensions"]),
-                        vector_blob=bytes(item["vector_blob"]),
-                        created_at=now,
-                    )
-                    for item in vectors
-                ]
-            )
+            vector_rows = [
+                {
+                    "id": str(uuid4()),
+                    "page_id": row.id,
+                    "knowledge_base_mapping_id": mapping_id,
+                    "section_index": int(item["section_index"]),
+                    "heading": item.get("heading"),
+                    "section_content": str(item["section_content"]),
+                    "content_hash": str(item["content_hash"]),
+                    "embedding_fingerprint": fingerprint,
+                    "embedding_dimensions": int(item["embedding_dimensions"]),
+                    "vector_blob": bytes(item["vector_blob"]),
+                    "created_at": now,
+                }
+                for item in vectors
+            ]
+            # Keep the page replacement atomic, but cap each SQL statement so
+            # large Wiki pages cannot flood the database with one huge insert.
+            for start in range(0, len(vector_rows), self.write_batch_size):
+                await session.execute(
+                    insert(LlmWikiWikiVectorRow),
+                    vector_rows[start : start + self.write_batch_size],
+                )
             state = await self._ensure_state(session, mapping_id)
             state.index_revision = int(state.index_revision or 0) + 1
             state.embedding_fingerprint = fingerprint

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

@@ -40,20 +40,31 @@ async def test_sql_store_atomically_replaces_vectors_and_marks_missing_deleted(t
         )
         await session.commit()
 
-    store = SqlLlmWikiIndexStore(session_factory)
+    store = SqlLlmWikiIndexStore(session_factory, write_batch_size=1)
     assert await store.try_acquire_sync_lease("mapping-1", owner="worker", sync_id="sync-1", lease_seconds=60, fingerprint="fp")
     assert not await store.try_acquire_sync_lease("mapping-1", owner="worker", sync_id="sync-2", lease_seconds=60, fingerprint="fp")
     page = normalize_wiki_page("mapping-1", {"id": "p1", "slug": "people/alice", "title": "Alice", "content": "Alice founded Acme.", "status": "published"})
     blob, dimensions = encode_vector([3.0, 4.0], dimensions=2)
     saved = await store.replace_page_vectors(
         page=page,
-        vectors=[{"section_index": 0, "heading": "History", "section_content": "Alice founded Acme.", "content_hash": "section", "embedding_dimensions": dimensions, "vector_blob": blob}],
+        vectors=[
+            {
+                "section_index": index,
+                "heading": "History",
+                "section_content": f"Alice founded Acme. Part {index}",
+                "content_hash": f"section-{index}",
+                "embedding_dimensions": dimensions,
+                "vector_blob": blob,
+            }
+            for index in range(3)
+        ],
         fingerprint="fp",
         sync_id="sync-1",
     )
     snapshot = await store.load_vector_snapshot("mapping-1", "fp")
     assert saved["slug"] == "people/alice"
-    assert len(snapshot["rows"]) == 1
+    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])
 
     assert await store.mark_missing_pages_deleted("mapping-1", "sync-2") == 1

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

@@ -690,6 +690,24 @@ async def test_weknora_client_proxies_original_document_preview_without_credenti
     assert media_type == "text/plain"
 
 
+@pytest.mark.asyncio
+async def test_weknora_client_fetches_protected_wiki_image() -> None:
+    async def handler(request: httpx.Request) -> httpx.Response:
+        assert request.url.path == "/api/v1/knowledge-bases/kb-one/files"
+        assert request.url.params["file_path"] == "resource://wiki/figure.png"
+        return httpx.Response(200, content=b"png-bytes", headers={"Content-Type": "image/png"})
+
+    async with httpx.AsyncClient(transport=httpx.MockTransport(handler)) as http_client:
+        client = WeKnoraClient(base_url="http://weknora.local:8080", http_client=http_client)
+        content, media_type = await client.get_knowledge_base_file(
+            "kb-one",
+            "resource://wiki/figure.png",
+        )
+
+    assert content == b"png-bytes"
+    assert media_type == "image/png"
+
+
 @pytest.mark.asyncio
 async def test_memory_store_enforces_owner_and_public_visibility() -> None:
     store = MemoryLlmWikiStore()
@@ -1358,6 +1376,10 @@ def test_weknora_iframe_html_rewrites_public_prefix_paths() -> None:
     assert '"allowedWeKnoraIds":["remote-kb","other-visible-kb"]' in injected
     assert ".breadcrumb-link.dropdown" not in injected
     assert "reloadForKnowledgeBaseSwitch" in injected
+    assert "function protectedImageApiUrl" in injected
+    assert 'img[data-protected-src]' in injected
+    assert 'encodeURIComponent(cfg.weknoraId)' in injected
+    assert 'credentials: "same-origin"' in injected
 
 
 def test_weknora_iframe_filters_kb_list_to_signed_visible_mappings() -> None:

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

@@ -1,10 +1,13 @@
 from __future__ import annotations
 
+import asyncio
 import json
+from types import SimpleNamespace
 
 import httpx
 import pytest
 
+from app.gateway import llmwiki_index_scheduler
 from deerflow.config.llmwiki_config import LocalWikiEmbeddingConfig, LocalWikiIndexConfig
 from deerflow.integrations.weknora.local_index.embedding import StrictWikiEmbeddingClient
 from deerflow.integrations.weknora.local_index.sync import WikiSyncService
@@ -85,6 +88,24 @@ async def test_configured_dimensions_are_validated_but_not_sent_to_bge() -> None
     assert requests == [{"model": "bge-embedding-m3", "input": ["Wiki page"]}]
 
 
+@pytest.mark.asyncio
+async def test_embedding_error_includes_upstream_reason(caplog) -> None:
+    def handler(request: httpx.Request) -> httpx.Response:
+        return httpx.Response(400, json={"detail": "unsupported request parameter: dimensions"})
+
+    config = LocalWikiEmbeddingConfig(
+        model="bge-embedding-m3",
+        base_url="http://embedding.test/v1",
+        max_retries=0,
+    )
+    async with httpx.AsyncClient(transport=httpx.MockTransport(handler)) as http_client:
+        client = StrictWikiEmbeddingClient(config, http_client=http_client)
+        with pytest.raises(RuntimeError, match="unsupported request parameter: dimensions"):
+            await client.embed_query("Wiki page")
+
+    assert "unsupported request parameter: dimensions" in caplog.text
+
+
 @pytest.mark.asyncio
 async def test_omitted_dimensions_are_discovered_from_first_response() -> None:
     def handler(request: httpx.Request) -> httpx.Response:
@@ -167,3 +188,39 @@ async def test_fingerprint_change_automatically_rebuilds_entire_mapping() -> Non
     assert store.states["kb"]["state"] == "idle"
     assert store.states["kb"]["embedding_fingerprint"] == "fp-new"
     assert {vector["embedding_fingerprint"] for vectors in store.vectors.values() for vector in vectors} == {"fp-new"}
+
+
+@pytest.mark.asyncio
+async def test_auto_sync_scheduler_waits_before_first_scan(monkeypatch) -> None:
+    calls: list[str] = []
+
+    async def fake_sleep(seconds: float) -> None:
+        calls.append(f"sleep:{seconds}")
+        raise asyncio.CancelledError
+
+    async def fake_sync_once(app) -> None:
+        calls.append("sync")
+
+    monkeypatch.setattr(llmwiki_index_scheduler.asyncio, "sleep", fake_sleep)
+    monkeypatch.setattr(llmwiki_index_scheduler, "_sync_once", fake_sync_once)
+    app = SimpleNamespace(
+        state=SimpleNamespace(
+            config=SimpleNamespace(
+                llmwiki=SimpleNamespace(
+                    local_wiki_index=SimpleNamespace(sync_interval_seconds=120),
+                )
+            )
+        )
+    )
+
+    with pytest.raises(asyncio.CancelledError):
+        await llmwiki_index_scheduler.local_wiki_index_scheduler_loop(app)
+
+    assert calls == ["sleep:120"]
+
+
+def test_local_wiki_index_accepts_legacy_sync_flag() -> None:
+    config = LocalWikiIndexConfig.model_validate({"sync_enabled": True})
+
+    assert config.auto_sync is True
+    assert config.sync_enabled is True

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

@@ -177,6 +177,37 @@ async def test_public_wiki_page_list_is_normalized(monkeypatch) -> None:
     assert payload["pages"][0]["aliases"] == []
 
 
+@pytest.mark.asyncio
+async def test_public_wiki_file_is_authorized_and_streamed(monkeypatch) -> None:
+    store = PublicStore(_published_row())
+
+    class Client:
+        async def get_knowledge_base_file(self, knowledge_base_id: str, file_path: str):
+            assert knowledge_base_id == "remote-one"
+            assert file_path == "storage://tenant/resource://wiki/figure.png"
+            return b"png-bytes", "image/png"
+
+    monkeypatch.setattr(llmwiki, "_client_or_503", lambda: Client())
+
+    response = await llmwiki.get_public_wiki_file(
+        _request(store),
+        "kb-one",
+        "storage://tenant/resource://wiki/figure.png",
+    )
+
+    assert store.calls == [("kb-one", "__public_wiki__", False, False)]
+    assert response.body == b"png-bytes"
+    assert response.media_type == "image/png"
+    assert response.headers["cache-control"] == "public, max-age=300"
+
+
+def test_wiki_file_proxy_rejects_non_weknora_urls() -> None:
+    with pytest.raises(HTTPException) as exc_info:
+        llmwiki._validate_protected_wiki_file_path("https://example.test/private.png")
+
+    assert exc_info.value.status_code == 422
+
+
 @pytest.mark.asyncio
 async def test_public_wiki_index_preserves_weknora_group_and_item_order(monkeypatch) -> None:
     store = PublicStore(_published_row())