feat(ai写作): 左栏智能体 Tab 内嵌问答并优化模块侧栏

AI 写作页默认展示智能体 Tab，在页内复用 ai-writing 模块配置与 AgentChatPage；确认类型后侧栏扁平展示类型名并支持整块收起动画。同步历史回看、流式续跑、改写菜单与 researcher 检索相关改动。

Co-authored-by: Cursor <cursoragent@cursor.com>

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

@@ -42,8 +42,12 @@ _ACTION_INSTRUCTIONS: dict[str, str] = {
     "expand": "请对以下文字进行扩写，补充细节和说明，使内容更丰富饱满。",
     "shorten": "请对以下文字进行缩写，保留核心意思，去除冗余表达，使其更简洁。",
     "tone": "请调整以下文字的语气风格。",
+    "imitate": "请模仿给定范文的写作风格、句式与语气，对以下文字进行仿写改写，在保留原文核心信息的前提下尽量贴近范文的表达方式。",
 }
 
+# 仿写参考范文的最大长度（防止超长 prompt）
+_REFERENCE_MAX_CHARS = 2000
+
 _TONE_SUFFIX: dict[str, str] = {
     "professional": "目标风格：专业、正式。",
     "casual": "目标风格：轻松、口语化。",
@@ -72,11 +76,12 @@ class SurroundingContext(BaseModel):
 class RewriteRequest(BaseModel):
     thread_id: str | None = Field(default=None, alias="threadId")
     selection: SelectionInfo
-    action: str = Field(..., description="polish | expand | shorten | tone")
+    action: str = Field(..., description="polish | expand | shorten | tone | imitate")
     tone: str | None = Field(default=None)
     model_name: str | None = Field(default=None, alias="modelName")
     surrounding_context: SurroundingContext = Field(default_factory=SurroundingContext, alias="surroundingContext")
     custom_instruction: str | None = Field(default=None, alias="customInstruction")
+    reference_text: str | None = Field(default=None, alias="referenceText", description="仿写参考范文（action=imitate）")
     follow_up_instruction: str | None = Field(default=None, alias="followUpInstruction")
     previous_result: str | None = Field(default=None, alias="previousResult")
 
@@ -121,18 +126,15 @@ def _extract_chunk_parts(chunk: object) -> tuple[str, str]:
     return thinking, text
 
 
-# ── Endpoint ──────────────────────────────────────────────────────────────────
+# ── Prompt building (pure, testable) ──────────────────────────────────────────
 
 
-@router.post(
-    "/rewrite",
-    summary="AI Text Rewrite (SSE stream)",
-    response_class=StreamingResponse,
-)
-async def rewrite_selection(
-    body: RewriteRequest,
-    config: AppConfig = Depends(get_config),
-) -> StreamingResponse:
+def build_rewrite_messages(body: RewriteRequest) -> tuple[str, list]:
+    """Build (resolved_action, messages) for the rewrite request.
+
+    Pure function (no model / IO) so the prompt assembly — including 仿写
+    (imitate) reference-sample injection — can be unit-tested directly.
+    """
     action = body.action if body.action in _ACTION_INSTRUCTIONS else "polish"
     instruction = _ACTION_INSTRUCTIONS[action]
     if action == "tone" and body.tone and body.tone in _TONE_SUFFIX:
@@ -153,6 +155,9 @@ async def rewrite_selection(
     base_instruction = instruction
     if body.custom_instruction:
         base_instruction = f"{instruction}\n用户额外要求：{body.custom_instruction}"
+    if action == "imitate" and body.reference_text and body.reference_text.strip():
+        reference = body.reference_text.strip()[:_REFERENCE_MAX_CHARS]
+        base_instruction = f"{base_instruction}\n\n【范文】（请模仿其文风、句式、语气与结构，但不要照抄其具体内容）：\n{reference}"
     user_message = f"{base_instruction}\n\n{context_block}需要改写的文字：\n{body.selection.text}"
 
     if body.follow_up_instruction and body.previous_result:
@@ -165,6 +170,23 @@ async def rewrite_selection(
     else:
         messages = [SystemMessage(content=_SYSTEM_PROMPT), HumanMessage(content=user_message)]
 
+    return action, messages
+
+
+# ── Endpoint ──────────────────────────────────────────────────────────────────
+
+
+@router.post(
+    "/rewrite",
+    summary="AI Text Rewrite (SSE stream)",
+    response_class=StreamingResponse,
+)
+async def rewrite_selection(
+    body: RewriteRequest,
+    config: AppConfig = Depends(get_config),
+) -> StreamingResponse:
+    action, messages = build_rewrite_messages(body)
+
     # Auto-enable thinking when the selected model supports it
     resolved_name = body.model_name or (config.models[0].name if config.models else None)
     model_cfg = config.get_model_config(resolved_name) if resolved_name else None

offline-backend-20260512/backend/config.yaml

@@ -440,6 +440,7 @@ ai_writing:
   # 接口契约：POST {url} 体 {"query","knowledge_base_name","ts":"yes"} → [{"page_content","metadata":{m_title,content,recUuid,m_publish,source1}}]
   intranet_search_url: "" # 例如 http://10.106.57.180:7863/knowledge_base/search_docs_es
   intranet_knowledge_base: "samples" # 兜底检索用的知识库名
+  intranet_search_timeout: 300 # 内网检索超时时间（秒），代码层强制最低 300；需要更长可调大
   # 内网接口诊断开关：开启后即使技能已检索成功，也照样把内网接口跑完并把执行结果
   # （端点/每个检索词命中数/报错/原始响应片段）记到 material_package.intranet_debug 里，
   # 便于在接口返回(devtools)排查内网接口到底通不通。生产建议关掉（会多跑一次内网）。

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

@@ -594,6 +594,7 @@ async def run_skill_search_agent(
     results: list = []
     seen_urls: set[str] = set()
     last_error = ""
+    search_timeout = max(timeout_s, _REAL_AGENT_TIMEOUT_SECONDS)
 
     for _turn in range(_MAX_TURNS):
         try:
@@ -616,10 +617,10 @@ async def run_skill_search_agent(
                 emit_query(query)
             try:
                 found = await asyncio.wait_for(
-                    provider.search(query, max_results=max_results), timeout=timeout_s
+                    provider.search(query, max_results=max_results), timeout=search_timeout
                 )
             except asyncio.TimeoutError:
-                last_error = f"检索超时（>{timeout_s}s）"
+                last_error = f"检索超时（>{search_timeout}s）"
                 messages.append(ToolMessage(content=last_error, tool_call_id=tc_id))
                 continue
             except Exception as e:  # noqa: BLE001

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

@@ -102,6 +102,14 @@ _KNOWLEDGE_SKILL_DISPLAY = "知识库检索"
 _KNOWLEDGE_SKILL_FALLBACK_DESC = "对写作意图提炼的检索词执行 web/intranet 知识库检索，获取相关素材"
 
 
+def _intranet_timeout_seconds(ai_writing_config: dict) -> int:
+    """内网检索超时统一入口：默认 10 分钟，任何配置都至少 5 分钟。"""
+    from ..search.intranet_stub import normalize_intranet_timeout
+
+    raw = (ai_writing_config or {}).get("intranet_search_timeout")
+    return normalize_intranet_timeout(raw)
+
+
 def _fallback_knowledge_skill() -> SimpleNamespace:
     """技能目录里找不到 knowledge-base-search 时的内存兜底（原生路径不依赖 SKILL.md）。"""
     return SimpleNamespace(name=KNOWLEDGE_SEARCH_SKILL_NAME, description=_KNOWLEDGE_SKILL_FALLBACK_DESC)
@@ -581,8 +589,9 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
         provider = IntranetSearchProvider(
             base_url=intranet_url,  # 空串 → provider 内部回退默认内网端点（旧逻辑）
             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),
+            # 内网慢检索超时可在 config.yaml 的 ai_writing.intranet_search_timeout 调大，
+            # 默认 600s，且最低 300s。
+            timeout=_intranet_timeout_seconds(ai_writing_config),
         )
         endpoint_label = intranet_url or getattr(provider, "endpoint", "内置默认内网端点")
         # 用户输入框追加（is_user_query_search）时 keywords 已是 [用户原话]，全部检索；
@@ -833,7 +842,7 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
             "configured": bool(intranet_url),
             "endpoint": intranet_url or "(未配置 ai_writing.intranet_search_url)",
             "knowledge_base": ai_writing_config.get("intranet_knowledge_base", "samples"),
-            "timeout": ai_writing_config.get("intranet_search_timeout", 600),
+            "timeout": _intranet_timeout_seconds(ai_writing_config),
             "debug_force": intranet_debug_force,
             "status": "pending" if intranet_url else "not_configured",
             "queries": [],          # 每个检索词：{query, count, error?}
@@ -853,8 +862,8 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
                     base_url=intranet_url,
                     knowledge_base_name=ai_writing_config.get("intranet_knowledge_base", "samples"),
                     # 兜底接口超时可在 config.yaml 的 ai_writing.intranet_search_timeout 调大，
-                    # 默认 600s（10 分钟），给内网慢检索留足时间。
-                    timeout=ai_writing_config.get("intranet_search_timeout", 600),
+                    # 默认 600s（10 分钟），且最低 300s。
+                    timeout=_intranet_timeout_seconds(ai_writing_config),
                 )
                 intranet_holder["provider"] = intranet
                 fb_keywords = keywords[:keyword_count] or [intent]

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

@@ -1,6 +1,6 @@
 from .base import BaseSearchProvider, SearchResult
 from .internet_search import InternetSearchProvider
-from .intranet_stub import IntranetSearchProvider
+from .intranet_stub import IntranetSearchProvider, normalize_intranet_timeout
 from .demo_search import DemoSearchProvider
 from .duckduckgo_search import DuckDuckGoSearchProvider
 from .skill_search import SkillSearchProvider
@@ -13,6 +13,7 @@ def get_search_provider(ai_writing_config: dict) -> BaseSearchProvider:
       search_provider: internet   # internet(默认,DDG) / intranet / tavily / demo
       intranet_search_url: http://10.106.57.180:7863/knowledge_base/search_docs_es
       intranet_knowledge_base: samples
+      intranet_search_timeout: 300 # 内网检索超时秒数，最少 300 秒
       ddg_region: "cn-zh"         # DuckDuckGo 区域，默认 cn-zh
 
     internet 模式：DuckDuckGo 为主搜索（免 API Key），失败时 fallback 到 demo 演示素材。
@@ -24,6 +25,7 @@ def get_search_provider(ai_writing_config: dict) -> BaseSearchProvider:
         return IntranetSearchProvider(
             base_url=cfg.get("intranet_search_url", ""),
             knowledge_base_name=cfg.get("intranet_knowledge_base", "samples"),
+            timeout=normalize_intranet_timeout(cfg.get("intranet_search_timeout")),
         )
     if provider == "tavily":
         return _FallbackSearchProvider(InternetSearchProvider(), DemoSearchProvider())

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

@@ -11,10 +11,22 @@ logger = logging.getLogger(__name__)
 # 内网知识库默认检索接口与知识库名（可经 config.yaml 的 ai_writing 段覆盖）
 _DEFAULT_ENDPOINT = "http://10.106.57.180:7863/knowledge_base/search_docs_es"
 _DEFAULT_KB = "samples"
+# 内网检索最少等待 5 分钟；该接口经常慢返回，低于这个值会造成素材收集误判为空。
+_MIN_TIMEOUT_SECONDS = 300
+_DEFAULT_TIMEOUT_SECONDS = 600
 # 单条原始响应体最大保留长度，避免把超大响应整段灌进前端时间线
 _RAW_SNIPPET_LIMIT = 2000
 
 
+def normalize_intranet_timeout(timeout: object, default: int = _DEFAULT_TIMEOUT_SECONDS) -> int:
+    """返回内网检索超时秒数，并强制不低于 5 分钟。"""
+    try:
+        value = int(timeout)
+    except (TypeError, ValueError):
+        value = default
+    return max(value, _MIN_TIMEOUT_SECONDS)
+
+
 def _pick(d: dict, keys: tuple) -> str:
     """从结果项里按候选字段名取第一个非空值（两套接口字段名不同，做并集兼容）。"""
     for k in keys:
@@ -38,17 +50,13 @@ class IntranetSearchProvider(BaseSearchProvider):
         self,
         base_url: str = "",
         knowledge_base_name: str = _DEFAULT_KB,
-        timeout: int = 600,
+        timeout: int = _DEFAULT_TIMEOUT_SECONDS,
         **_ignored,
     ):
         self.endpoint = base_url or _DEFAULT_ENDPOINT
         self.knowledge_base_name = knowledge_base_name or _DEFAULT_KB
-        # 内网 ES 接口很慢，兜底检索超时放宽到 10 分钟（不依赖 env 配置）；构造方可传更大值覆盖。
-        # 「咱们先把自己的做好」：我方兜底超时给足，避免因我方过早超时被甩锅成「检不出」。
-        try:
-            self.timeout = max(int(timeout), 1)
-        except (TypeError, ValueError):
-            self.timeout = 600
+        # 内网 ES 接口很慢，兜底检索超时默认 10 分钟，且无论配置如何至少 5 分钟。
+        self.timeout = normalize_intranet_timeout(timeout)
         # 收集本次运行的检索异常，供 researcher 节点取出并提示用户（ES 波动等）
         self.errors: list[str] = []
         # 解析失败时收集的原始响应体，供前端「错误返回结果」折叠展示