feat(ai-writing): 检索类型加回「知识库检索」(直连内网 ES 接口)

弱模型常识别不了配置技能(deepseek-chat 等),恢复旧的"不用技能"检索路径
作为独立检索类型 knowledge_base:绕过技能编排,直接调
ai_writing.intranet_search_url 内网知识库 ES 接口(IntranetSearchProvider)
对检索词并发检索;未配地址 → 无素材 + skill_error 告警。

- researcher.py: material_source 归一保留 knowledge_base + 新增直连内网分支
- state.py: material_source 三值注释
- 前端 AIWritingForm/WritingFormContext/types: 下拉新增「知识库检索」
- tests/test_ai_writing_knowledge_base.py: 直连/未配/不编排技能 3 用例
- backend CLAUDE.md: 检索类型段更新为三值

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

frontend-web/src/open-canvas/components/ai-writing/AIWritingForm.tsx

@@ -40,11 +40,12 @@ const INPUT_CLASS =
 
 const DEFAULT_KEYWORD_COUNT = 1;
 const DEFAULT_AUDIENCE = '普通读者';
-type MaterialSource = 'general' | 'notebook';
+type MaterialSource = 'general' | 'knowledge_base' | 'notebook';
 const MAX_INTENT_LENGTH = 500;
 
 const MATERIAL_SOURCE_OPTIONS: Array<{ value: MaterialSource; label: string; desc: string }> = [
   { value: 'general', label: '通用检索', desc: '用素材收集专家配置的技能检索（默认：知识库检索）' },
+  { value: 'knowledge_base', label: '知识库检索', desc: '直连内网知识库检索接口，不走技能编排' },
   { value: 'notebook', label: '我的空间', desc: '从空间中检索素材' },
 ];
 

frontend-web/src/open-canvas/contexts/WritingFormContext.tsx

@@ -37,8 +37,8 @@ export interface WritingFormValue {
   /** 是否在成稿里加参考文献（正文 [n] 角标 + 文末「参考文献」列表，像论文）。默认开。 */
   addReferences: boolean;
   setAddReferences: (v: boolean) => void;
-  materialSource: 'general' | 'notebook';
-  setMaterialSource: (v: 'general' | 'notebook') => void;
+  materialSource: 'general' | 'knowledge_base' | 'notebook';
+  setMaterialSource: (v: 'general' | 'knowledge_base' | 'notebook') => void;
   /** 不收集素材，直接用通用知识写作：勾选后跳过素材检索阶段（含素材确认暂停点）。默认关。 */
   skipResearch: boolean;
   setSkipResearch: (v: boolean) => void;
@@ -75,7 +75,7 @@ export function WritingFormProvider({
   initialDocumentIds,
 }: {
   children: ReactNode;
-  initialMaterialSource?: 'general' | 'notebook';
+  initialMaterialSource?: 'general' | 'knowledge_base' | 'notebook';
   initialNotebookId?: number | null;
   initialDocumentIds?: number[];
 }) {
@@ -88,7 +88,7 @@ export function WritingFormProvider({
   const [audience, setAudience] = useState('普通读者');
   const [strictMode, setStrictMode] = useState(false);
   const [addReferences, setAddReferences] = useState(true);
-  const [materialSource, setMaterialSource] = useState<'general' | 'notebook'>(initialMaterialSource ?? 'general');
+  const [materialSource, setMaterialSource] = useState<'general' | 'knowledge_base' | 'notebook'>(initialMaterialSource ?? 'general');
   const [skipResearch, setSkipResearch] = useState(false);
   const [selectedNotebookId, setSelectedNotebookId] = useState<number | null>(initialNotebookId ?? null);
   const [selectedDocumentIds, setSelectedDocumentIds] = useState<number[]>(initialDocumentIds ?? []);
@@ -105,8 +105,10 @@ export function WritingFormProvider({
     if (payload.target_audience?.trim()) setAudience(payload.target_audience.trim());
     if (payload.writing_mode === 'strict') setStrictMode(true);
     else if (payload.writing_mode === 'loose') setStrictMode(false);
-    // 旧值 skill / knowledge_base 已合并为通用检索 general
-    if (payload.material_source === 'skill' || payload.material_source === 'knowledge_base' || payload.material_source === 'general') {
+    // knowledge_base=知识库检索（直连内网 ES）保留；旧值 skill 归一为通用检索 general
+    if (payload.material_source === 'knowledge_base') {
+      setMaterialSource('knowledge_base');
+    } else if (payload.material_source === 'skill' || payload.material_source === 'general') {
       setMaterialSource('general');
     }
     if (payload.outline?.trim()) {

frontend-web/src/open-canvas/types/ai-writing.ts

@@ -265,8 +265,9 @@ export interface AIWritingRequest {
   writingMode: "strict" | "loose";
   /** 是否在成稿里加参考文献（正文 [n] 角标 + 文末「参考文献」列表，像论文）。默认 true。 */
   addReferences?: boolean;
-  /** 检索类型：general=通用检索（素材收集专家按配置技能检索，默认知识库检索技能）/ notebook=我的空间 */
-  materialSource: "general" | "notebook";
+  /** 检索类型：general=通用检索（素材收集专家按配置技能检索，默认知识库检索技能）/
+   *  knowledge_base=知识库检索（直连内网知识库 ES 接口，不走技能编排）/ notebook=我的空间 */
+  materialSource: "general" | "knowledge_base" | "notebook";
   /** 不收集素材，直接用通用知识写作：勾选后跳过整个素材检索流程（含素材确认暂停点），
    *  直接进入大纲规划；后端强制 loose 写作模式。默认 false。 */
   skipResearch?: boolean;

offline-backend-20260512/backend/CLAUDE.md

@@ -273,7 +273,7 @@ The AI-writing graph (`packages/harness/deerflow/agents/ai_writing/`) has four s
 
 Seeding mirrors the roundtable pattern: verified `config.yaml` + `SOUL.md` ship in `app/gateway/routers/_ai_writing_seed_assets/<id>/`; `_ai_writing_seed.py::ensure_ai_writing_functional_agents()` copies them to `.deer-flow/agents/` when missing (never overwrites local edits), called from the startup lifespan (before `_sync_legacy_agents`, so the agents appear in `/api/agents` on first boot) and self-heals from `GET /api/ai-writing/sessions` + `GET /api/ai-writing/article-types`. Tests: `tests/test_ai_writing_seed.py`, `tests/test_ai_writing_agent_runner.py`.
 
-**Skill-driven retrieval (检索类型).** `material_source` has exactly two values: `general` (通用检索, default) and `notebook` (我的空间); legacy `knowledge_base`/`skill` are normalized to `general` by the researcher node. General retrieval is orchestrated from the **skills configured on the `ai-writing-researcher` agent** (`config.yaml → skills`, editable from the agent management page): the built-in `knowledge-base-search` skill (知识库检索) runs the native keyword fan-out web/intranet search (the old default path), while any other configured skill searches with a `description`-targeted query; skills run in configured order with early-stop at `max_materials`, and an empty/unresolvable skills config falls back to the built-in knowledge search so retrieval always works. The skill itself is seeded from `_ai_writing_seed_assets/skills/knowledge-base-search/SKILL.md` into `skills/public/` (never overwrites), and a pre-existing researcher `config.yaml` lacking the `skills` key is patched in place with the default (explicit `skills: []` is respected). **PAUSE-1 input append flow**: when the user types natural language into the material-confirm input (`re_search`/legacy `enable_skill_search` + `user_query`), the node matches the most relevant configured skill via `SkillPicker(candidates=…)` (skipped when only one skill is configured), searches with the user's words, and **appends** the new materials to the existing package (URL-dedup, original order preserved, no LLM re-rank/truncation, 100-item hard cap). **Skill-failure fault tolerance**: a configured skill that times out / raises / has all its keyword searches fail (vs. legitimately returning 0 results) no longer just silently moves on — the researcher node emits a `skill_search_step` of type `skill_error` (carrying the error `reason`), and if the configured skills collected **no** new materials at all it runs a `fallback` pass over other available skills (`_fallback_candidate_skills`: the built-in `knowledge-base-search` native path first, then other enabled catalog skills not yet tried) instead of erroring out. The frontend `SkillStepsCard` renders `skill_error` (rose) and `fallback` (amber) so the timeline shows "技能报错 → 改用其他技能搜集". Tests: `tests/test_ai_writing_material_source.py`.
+**Skill-driven retrieval (检索类型).** `material_source` has three values: `general` (通用检索, default), `knowledge_base` (知识库检索), and `notebook` (我的空间); legacy `skill` is normalized to `general` by the researcher node. **`knowledge_base` 直连内网 ES 接口** — it bypasses skill orchestration entirely and calls `IntranetSearchProvider` over `ai_writing.intranet_search_url` (concurrent over the generated keywords; `intranet_knowledge_base`/`intranet_search_timeout` apply); an unconfigured url → no materials + a `skill_error` notice. This is the "弱模型识别不了技能" escape hatch — the old no-skill retrieval path restored as an explicit option (weak models like deepseek-chat often fail to invoke configured skills). Tests: `tests/test_ai_writing_knowledge_base.py`. General retrieval is orchestrated from the **skills configured on the `ai-writing-researcher` agent** (`config.yaml → skills`, editable from the agent management page): the built-in `knowledge-base-search` skill (知识库检索) runs the native keyword fan-out web/intranet search (the old default path), while any other configured skill searches with a `description`-targeted query; skills run in configured order with early-stop at `max_materials`, and an empty/unresolvable skills config falls back to the built-in knowledge search so retrieval always works. The skill itself is seeded from `_ai_writing_seed_assets/skills/knowledge-base-search/SKILL.md` into `skills/public/` (never overwrites), and a pre-existing researcher `config.yaml` lacking the `skills` key is patched in place with the default (explicit `skills: []` is respected). **PAUSE-1 input append flow**: when the user types natural language into the material-confirm input (`re_search`/legacy `enable_skill_search` + `user_query`), the node matches the most relevant configured skill via `SkillPicker(candidates=…)` (skipped when only one skill is configured), searches with the user's words, and **appends** the new materials to the existing package (URL-dedup, original order preserved, no LLM re-rank/truncation, 100-item hard cap). **Skill-failure fault tolerance**: a configured skill that times out / raises / has all its keyword searches fail (vs. legitimately returning 0 results) no longer just silently moves on — the researcher node emits a `skill_search_step` of type `skill_error` (carrying the error `reason`), and if the configured skills collected **no** new materials at all it runs a `fallback` pass over other available skills (`_fallback_candidate_skills`: the built-in `knowledge-base-search` native path first, then other enabled catalog skills not yet tried) instead of erroring out. The frontend `SkillStepsCard` renders `skill_error` (rose) and `fallback` (amber) so the timeline shows "技能报错 → 改用其他技能搜集". Tests: `tests/test_ai_writing_material_source.py`.
 
 **Skill-call timeout** is hardcoded **300s (5 min)** — `researcher.py` `_SKILL_SEARCH_TIMEOUT_SECONDS` (per-skill wrapper) and `_skill_agent.py` `_REAL_AGENT_TIMEOUT_SECONDS` (whole real-agent skill run). The intranet ES fallback (`IntranetSearchProvider`) gets a longer **600s (10 min)** default (`intranet_stub.py`; overridable via `ai_writing.intranet_search_timeout`) — that interface is the slow one, so our own fallback timeout is generous to avoid being blamed for "检不出".
 

offline-backend-20260512/backend/packages/harness/deerflow/agents/ai_writing/nodes/researcher.py

@@ -1,12 +1,15 @@
 """素材收集专家节点：关键词提取 → 配置技能编排检索（含笔记） → 相关度排序。
 
-检索类型（material_source）只有两种：
+检索类型（material_source）有三种：
   - ``general``（通用检索，默认）：按素材收集专家 agent（ai-writing-researcher）
     config.yaml 配置的技能顺序编排检索。内置「知识库检索」技能
     （knowledge-base-search）走原生 web/intranet 关键词并发检索；其它技能用
     其 description 生成定向 query 后调真实 provider 检索。
+  - ``knowledge_base``（知识库检索）：**直连内网知识库 ES 接口**
+    （``ai_writing.intranet_search_url`` 配的 ``IntranetSearchProvider``），
+    不编排任何技能、也不跑通用 provider。未配置接口地址时本次无素材并给出告警。
   - ``notebook``（我的空间）：仅用笔记 API 素材。
-旧值 ``knowledge_base`` / ``skill`` 一律按 ``general`` 兼容处理。
+旧值 ``skill`` 仍按 ``general`` 兼容处理。
 
 PAUSE-1 素材确认卡的单输入框：用户用自然语言描述想补充检索的内容
 （如「检索 A 的数据」）时，让模型从已配置技能中按相关度匹配，逐个检索，
@@ -414,9 +417,10 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
         if requested_append_count is not None:
             effective_user_query = residual_topic or intent
     # 用户在写作表单选择的检索类型：general（通用检索，按 agent 配置技能编排）/
-    # notebook（我的空间）。旧值 knowledge_base / skill 一律按 general 兼容。
+    # knowledge_base（知识库检索，直连内网 ES 接口）/ notebook（我的空间）。
+    # 旧值 skill 一律按 general 兼容。
     raw_source = (state.get("material_source") or "general").strip()
-    material_source = "notebook" if raw_source == "notebook" else "general"
+    material_source = raw_source if raw_source in ("notebook", "knowledge_base") else "general"
 
     # 用户指定的检索关键词数量（1~10），决定生成多少个检索词、检索多少个方向
     keyword_count = _resolve_keyword_count(state.get("keyword_count"))
@@ -560,6 +564,58 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
         # 用户在表单选了「我的空间」作为检索类型 → 跳过技能编排，
         # 仅使用 Step 1.5 已拉取的 notebook_materials。
         _emit(f"仅使用笔记素材，跳过技能检索（{len(notebook_materials)} 条）")
+    elif material_source == "knowledge_base":
+        # ── 知识库检索：直连内网 ES 接口 ──
+        # 用户在表单选了「知识库检索」→ 跳过技能编排、跳过通用 provider，
+        # 直接调 config.yaml 配的 ai_writing.intranet_search_url 内网知识库 ES 接口
+        # （IntranetSearchProvider）对检索词并发检索。未配置地址 → 无素材 + 告警。
+        intranet_url = (ai_writing_config.get("intranet_search_url") or "").strip()
+        if not intranet_url:
+            _emit("未配置内网知识库检索接口（ai_writing.intranet_search_url），本次未检索到素材")
+            search_errors.append("未配置 ai_writing.intranet_search_url，知识库检索不可用")
+            _emit_skill_step({
+                "type": "skill_error",
+                "message": "「知识库检索」未配置内网接口地址（ai_writing.intranet_search_url），无法检索",
+                "skill_name": KNOWLEDGE_SEARCH_SKILL_NAME,
+                "reason": "intranet_search_url 为空",
+            })
+        else:
+            from ..search.intranet_stub import IntranetSearchProvider
+
+            provider = IntranetSearchProvider(
+                base_url=intranet_url,
+                knowledge_base_name=ai_writing_config.get("intranet_knowledge_base", "samples"),
+                # 内网慢检索超时可在 config.yaml 的 ai_writing.intranet_search_timeout 调大，默认 600s。
+                timeout=ai_writing_config.get("intranet_search_timeout", 600),
+            )
+            # 用户输入框追加（is_user_query_search）时 keywords 已是 [用户原话]，全部检索；
+            # 首次检索按 keyword_count 截断检索词。
+            search_keywords = keywords if is_user_query_search else keywords[:keyword_count]
+            total = len(search_keywords)
+            per_keyword = _per_keyword_limit(max_materials, total)
+            _emit(f"知识库检索：直连内网接口检索 {total} 个方向…")
+            _emit_skill_step({
+                "type": "invoke_skill",
+                "message": f"使用「{_KNOWLEDGE_SKILL_DISPLAY}」直连内网接口检索 {total} 个方向：{('、'.join(search_keywords[:3]))[:60]}",
+                "skill_name": KNOWLEDGE_SEARCH_SKILL_NAME,
+            })
+            gathered = await asyncio.gather(
+                *[provider.search(kw, max_results=per_keyword) for kw in search_keywords],
+                return_exceptions=True,
+            )
+            for results in gathered:
+                if isinstance(results, Exception):
+                    logger.warning("[knowledge-base] search failed: %s", results)
+                    search_errors.append(f"{type(results).__name__}: {results}")
+                    continue
+                new_results.extend(results or [])
+            search_errors.extend(getattr(provider, "errors", []))
+            _emit_skill_step({
+                "type": "skill_result",
+                "message": f"「{_KNOWLEDGE_SKILL_DISPLAY}」（内网接口）检索到 {len(new_results)} 条素材",
+                "skill_name": KNOWLEDGE_SEARCH_SKILL_NAME,
+                "matched_count": len(new_results),
+            })
     elif is_append_only:
         # ── 兼容旧版追加关键词模式 ──
         # 直接对用户给的关键词做原生并发检索（不走技能编排），结果追加到旧素材。

offline-backend-20260512/backend/packages/harness/deerflow/agents/ai_writing/state.py

@@ -141,8 +141,9 @@ class AIWritingState(TypedDict):
     # 是否在成稿里加参考文献：正文引用素材处加角标 [n]，文末按出现顺序生成「参考文献」列表（像论文）。
     add_references: bool
     # 检索类型：general=通用检索（按素材收集专家 agent 配置的技能编排，默认）/
-    # notebook=我的空间（仅笔记检索）。旧值 knowledge_base / skill 由 researcher
-    # 节点按 general 兼容处理。
+    # knowledge_base=知识库检索（直连 ai_writing.intranet_search_url 内网 ES 接口，
+    # 不走技能编排——弱模型识别不了技能时用这条）/ notebook=我的空间（仅笔记检索）。
+    # 旧值 skill 由 researcher 节点按 general 兼容处理。
     material_source: str
     # 跳过素材收集：用户在初始表单勾选「不收集素材，直接用通用知识写作」时为 True。
     # researcher 节点在**首次**进入（无任何 pause 干预）时短路，跳过整个检索流程直接

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

@@ -0,0 +1,105 @@
+"""researcher_node：检索类型 `knowledge_base`（知识库检索）= 直连内网 ES 接口。
+
+弱模型识别不了技能时用这条路：不编排任何技能、不跑通用 provider，
+直接调 ai_writing.intranet_search_url 内网知识库 ES 接口检索。
+"""
+from __future__ import annotations
+
+from types import SimpleNamespace
+
+import pytest
+
+from deerflow.agents.ai_writing.nodes import researcher as R
+from deerflow.agents.ai_writing.search.base import SearchResult
+
+
+class _FakeIntranet:
+    instances: list = []
+
+    def __init__(self, **kw):
+        self.errors = []
+        self.raw_responses = []
+        self.kw = kw
+        _FakeIntranet.instances.append(self)
+
+    async def search(self, query, max_results=5, **_):
+        return [SearchResult(
+            title=f"内网-{query}", content="内网正文", source="ES",
+            published_at="", url=f"http://es/{query}", source_type="intranet",
+        )]
+
+
+class _SkillProviderShouldNotRun:
+    """knowledge_base 模式不应触碰通用 provider —— 调到就让测试爆。"""
+    errors: list = []
+    raw_responses: list = []
+
+    async def search(self, query, max_results=5, **_):  # pragma: no cover - 不应被调用
+        raise AssertionError("knowledge_base 模式不应调用通用 provider / 技能检索")
+
+
+def _patch(monkeypatch, intranet_url="http://es/api"):
+    _FakeIntranet.instances = []
+    cfg = {
+        "max_materials": 20,
+        "intranet_search_url": intranet_url,
+        "intranet_knowledge_base": "samples",
+    }
+    monkeypatch.setattr(R, "get_ai_writing_config", lambda: cfg)
+    monkeypatch.setattr(R, "use_builtin_agent", lambda *_: False)
+    monkeypatch.setattr(R, "safe_get_stream_writer", lambda: (lambda *a, **k: None))
+    monkeypatch.setattr(R, "use_skill_search_agent", lambda: False)
+
+    async def fake_llm_json(*a, **k):
+        return {"keywords": ["特朗普竞选"]}
+
+    monkeypatch.setattr(R, "llm_json", fake_llm_json)
+    # knowledge_base 模式不该加载/编排技能，但仍打桩以防意外路径
+    monkeypatch.setattr(R, "_load_configured_skills",
+                        lambda: [SimpleNamespace(name="knowledge-search-v2", description="内网检索")])
+    monkeypatch.setattr(R, "get_search_provider", lambda _cfg: _SkillProviderShouldNotRun())
+    monkeypatch.setattr(
+        "deerflow.agents.ai_writing.search.intranet_stub.IntranetSearchProvider",
+        _FakeIntranet,
+    )
+
+
+def _state(material_source="knowledge_base"):
+    return {
+        "user_intent": "分析特朗普近期动向",
+        "article_type": "政治分析",
+        "target_audience": "普通读者",
+        "keyword_count": 1,
+        "material_source": material_source,
+    }
+
+
+@pytest.mark.asyncio
+async def test_knowledge_base_hits_intranet_directly(monkeypatch):
+    _patch(monkeypatch)
+    out = await R.researcher_node(_state(), {"configurable": {}})
+    mats = out["material_package"]["materials"]
+    assert mats, "知识库检索应返回内网素材"
+    assert all(m["source_type"] == "intranet" for m in mats)
+    assert mats[0]["title"] == "内网-特朗普竞选"
+    assert _FakeIntranet.instances, "应实例化 IntranetSearchProvider 直连内网接口"
+    # 走素材确认暂停点
+    assert out["status"] == "awaiting_material_confirm"
+
+
+@pytest.mark.asyncio
+async def test_knowledge_base_no_url_empty(monkeypatch):
+    """未配置内网接口地址：无素材，且不实例化 provider。"""
+    _patch(monkeypatch, intranet_url="")
+    out = await R.researcher_node(_state(), {"configurable": {}})
+    assert out["material_package"]["materials"] == []
+    assert _FakeIntranet.instances == [], "无地址时不应实例化内网 provider"
+
+
+@pytest.mark.asyncio
+async def test_knowledge_base_does_not_orchestrate_skills(monkeypatch):
+    """knowledge_base 不编排技能：通用 provider 一旦被调用就会 raise。"""
+    _patch(monkeypatch)
+    # 不抛异常即证明没走 _SkillProviderShouldNotRun
+    out = await R.researcher_node(_state(), {"configurable": {}})
+    assert out["material_package"]["materials"]