feat(knowledge): 出处标记渲染 + 相关笔记按标签精确匹配 + LLM 回退落草稿

A) 前端把 ^[inferred]/^[ambiguous] 渲染成「推测」「存疑」小角标（原先原样显示文本）
B) 相关笔记只按"区分性标签"精确匹配（去掉标题/摘要关键词检索——中文分词导致
   助手/知识图谱/页面 等泛词误关联，如「AI助手打招呼」被错误拉进 Related）；
   无区分性标签则不关联、不渲染 ## Related 段
C) LLM 提炼启用但失败时不再静默回退成"原文堆砌"：
   - 回退笔记落为 draft（草稿）而非 approved，便于复核
   - capture_thread 记 WARNING 并返回 llm_fallback；router 透出该标志
   - llm_extractor 在 JSON 解析失败/无 notes 时补充可诊断的 WARNING 日志

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

frontend-web/src/strategy-components/MarkdownRenderer.tsx

@@ -174,7 +174,20 @@ const MarkdownRenderer: React.FC<MarkdownRendererProps> = ({
       },
     );
   }
-  // 2) Numeric [[123]] citation badges.
+  // 2) wiki-capture provenance markers ^[inferred] / ^[ambiguous] → small badges
+  //    (otherwise they render as raw text). They mark model-inferred / uncertain
+  //    statements per the wiki-capture spec.
+  processed = processed
+    .replace(
+      /\s*\^\[inferred\]/g,
+      ' <sup title="模型推断，非原文直接得出" class="ml-0.5 rounded bg-amber-50 px-1 text-[10px] font-medium text-amber-600 align-super dark:bg-amber-500/15 dark:text-amber-300">推测</sup>',
+    )
+    .replace(
+      /\s*\^\[ambiguous\]/g,
+      ' <sup title="不确定 / 存在冲突" class="ml-0.5 rounded bg-rose-50 px-1 text-[10px] font-medium text-rose-600 align-super dark:bg-rose-500/15 dark:text-rose-300">存疑</sup>',
+    );
+
+  // 3) Numeric [[123]] citation badges.
   const contentWithCitationBadge = processed.replace(
     /\[\[(\d+)\]\]/g,
     '<button type="button" data-citation-index="$1" class="inline-flex items-center justify-center min-w-[1.5rem] h-6 px-2 mx-0.5 rounded-md border text-xs font-semibold align-middle bg-red-50 text-red-600 border-red-200 dark:bg-slate-700 dark:text-slate-100 dark:border-slate-500 cursor-pointer transition-all hover:opacity-85">[$1]</button>',

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

@@ -227,7 +227,12 @@ async def ingest_thread(thread_id: str, body: IngestThreadRequest, request: Requ
         )
     except EmptyThreadError:
         raise HTTPException(status_code=400, detail="Thread has no sediment-able content")
-    return {"note": result["note"], "created": result["created"], "notes": result.get("notes", [result["note"]])}
+    return {
+        "note": result["note"],
+        "created": result["created"],
+        "notes": result.get("notes", [result["note"]]),
+        "llm_fallback": result.get("llm_fallback", False),
+    }
 
 
 @router.post("/ingest/threads/batch")

offline-backend-20260512/backend/packages/harness/deerflow/knowledge/llm_extractor.py

@@ -252,10 +252,17 @@ async def extract_thread_llm_multi(
         return []
 
     if not data:
+        logger.warning("Knowledge LLM extraction for thread %s returned no parsable JSON; using rule-based fallback", thread_id)
         return []
 
     note_dicts = _notes_from_data(data)
     if not note_dicts:
+        logger.warning(
+            "Knowledge LLM extraction for thread %s produced no notes (should_ingest=%s, reason=%r); using rule-based fallback",
+            thread_id,
+            data.get("should_ingest"),
+            str(data.get("reason") or "")[:200],
+        )
         return []
     if not allow_multi:
         note_dicts = note_dicts[:1]

offline-backend-20260512/backend/packages/harness/deerflow/knowledge/service.py

@@ -94,46 +94,40 @@ class KnowledgeService:
         return draft
 
     async def _find_related_notes(self, draft: NoteDraft) -> list[dict[str, Any]]:
-        """Find existing notes on the same topic to cross-link under ## Related.
+        """Find existing notes to cross-link under ## Related — by **shared tags**.
 
-        Uses the existing keyword/hybrid search over the title (+ summary). The
-        draft's own note does not exist yet, so we exclude same-title hits to
-        avoid self-links when re-capturing or deduping. Best-effort: any failure
-        yields no links rather than blocking the capture.
+        Matching is strictly tag-based: a candidate must share at least one of the
+        draft's *distinctive* tags (the per-note project / ``knowledge-base``
+        defaults are dropped, else everything matches everything). Free-text
+        title/summary matching was removed because CJK tokenization made generic
+        words (助手 / 知识图谱 / 页面) link unrelated notes. No distinctive tags →
+        no related notes (better empty than noisy). Best-effort.
         """
         if not self.config.related_notes_enabled:
             return []
-        # Build a topical query from the title + summary + the note's *distinctive*
-        # tags (the per-note project/knowledge-base defaults are dropped, else
-        # every note would match every other). CJK runs tokenize as a single
-        # token, so shared distinctive tags are what make cross-topic matches work.
         default_tags = {t.lower() for t in (self.project_name or "", "knowledge-base", "zncm") if t}
         distinctive = [t for t in (draft.tags or []) if t and t.lower() not in default_tags]
-        query = " ".join(p for p in [draft.title or "", draft.summary or "", *distinctive] if p).strip()
-        if not query:
-            return []
-        limit = max(1, self.config.related_notes_limit)
-        mode = "hybrid" if self.embedding_client.is_enabled() else "keyword"
-        try:
-            items = await search_notes(self.repo, query, mode=mode, limit=limit + 3, embedding_client=self.embedding_client)
-        except Exception:
-            logger.exception("Failed to find related notes for '%s'", draft.title)
+        if not distinctive:
             return []
         own_title = (draft.title or "").strip().lower()
-        out: list[dict[str, Any]] = []
-        seen: set[str] = set()
-        for it in items:
-            nid = it.get("note_id")
-            title = (it.get("title") or "").strip()
-            if not nid or nid in seen or not title:
+        # Rank candidates by how many distinctive tags they share with the draft.
+        shared: dict[str, dict[str, Any]] = {}
+        for tag in distinctive:
+            try:
+                items, _total = await self.repo.list_notes(tag=tag, limit=50, offset=0)
+            except Exception:
+                logger.exception("Related-notes tag lookup failed for tag '%s'", tag)
                 continue
-            if title.lower() == own_title:  # the draft itself / a duplicate
-                continue
-            seen.add(nid)
-            out.append({"id": nid, "title": title})
-            if len(out) >= limit:
-                break
-        return out
+            for it in items:
+                nid = it.get("id")
+                title = (it.get("title") or "").strip()
+                if not nid or not title or title.lower() == own_title:
+                    continue
+                entry = shared.setdefault(nid, {"id": nid, "title": title, "count": 0})
+                entry["count"] += 1
+        ranked = sorted(shared.values(), key=lambda e: e["count"], reverse=True)
+        limit = max(1, self.config.related_notes_limit)
+        return [{"id": e["id"], "title": e["title"]} for e in ranked[:limit]]
 
     async def _persist_draft(self, draft: NoteDraft, *, created_by: str | None) -> dict[str, Any]:
         """Create a DB row, write the vault file, attach sources, return note."""
@@ -250,7 +244,9 @@ class KnowledgeService:
         # knowledge, not raw chat. A single conversation may split into several
         # cross-linked notes. Falls back to the rule-based extractor (always one
         # note) when the LLM is disabled/unavailable or returns nothing usable.
-        if self.config.llm_extract_enabled:
+        llm_attempted = self.config.llm_extract_enabled
+        llm_fallback = False
+        if llm_attempted:
             drafts = await llm_extractor.extract_thread_llm_multi(
                 messages,
                 thread_id=thread_id,
@@ -265,6 +261,12 @@ class KnowledgeService:
                 max_input_chars=self.config.extract_max_input_chars,
                 allow_multi=self.config.multi_note_enabled,
             )
+            if not drafts:
+                # The LLM path was supposed to run but produced nothing usable
+                # (API error / non-JSON / empty). We will fall back to the
+                # rule-based extractor, which yields a low-quality verbatim dump
+                # with no entities/tags — flag it so it lands as a draft.
+                llm_fallback = True
         if not drafts:
             single = extractor.extract_thread(
                 messages,
@@ -281,7 +283,24 @@ class KnowledgeService:
         if not drafts:
             raise EmptyThreadError(f"Thread {thread_id} has no sediment-able content")
 
-        normalized = normalize_status(status)
+        # When the LLM was enabled but silently fell back to rule-based extraction,
+        # the note is a verbatim dump (no distillation / entities / tags). Land it
+        # as a *draft* for review instead of silently approving it, and log loudly
+        # so the extraction failure is diagnosable (see also the WARNING/EXCEPTION
+        # logged inside llm_extractor.extract_thread_llm_multi).
+        effective_status = status
+        if llm_fallback:
+            logger.warning(
+                "Knowledge capture for thread %s fell back to rule-based extraction "
+                "(LLM distillation produced nothing usable); landing as draft. "
+                "Check extraction model '%s' / earlier logs for the cause.",
+                thread_id,
+                self.config.extract_model_name or "models[0]",
+            )
+            if normalize_status(status) == "approved":
+                effective_status = "draft"
+
+        normalized = normalize_status(effective_status)
         for draft in drafts:
             draft.status = normalized
         # Cross-link sibling notes from the same conversation by title, so the
@@ -294,7 +313,7 @@ class KnowledgeService:
                     draft.related = list(dict.fromkeys([*siblings, *draft.related]))
 
         notes = [await self._persist_draft(draft, created_by=created_by) for draft in drafts]
-        return {"note": notes[0], "created": True, "notes": notes}
+        return {"note": notes[0], "created": True, "notes": notes, "llm_fallback": llm_fallback}
 
     async def capture_search(
         self,