feat(ai写作): 内网检索接口执行诊断写入 material_package.intranet_debug(排查用)
- material_package 新增 intranet_debug：是否配置/端点/知识库/超时/每个检索词命中数与 报错/原始响应片段/总命中数/是否被采用为兜底/status(not_configured|pending|ran| cancelled|crashed)。随 values 事件回前端，前端忽略该字段，仅供 devtools 排查。 - 新增 config ai_writing.intranet_search_debug(默认 false)：开启后即使技能成功也跑完 内网并记录完整结果，用于验证内网接口是否真能通。 - 前端不改动。 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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@@ -440,6 +440,10 @@ ai_writing:
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# 接口契约：POST {url} 体 {"query","knowledge_base_name","ts":"yes"} → [{"page_content","metadata":{m_title,content,recUuid,m_publish,source1}}]
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intranet_search_url: "" # 例如 http://10.106.57.180:7863/knowledge_base/search_docs_es
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intranet_knowledge_base: "samples" # 兜底检索用的知识库名
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# 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
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# （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，
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# 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）。
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intranet_search_debug: false
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# 四阶段「内置功能型智能体」开关（按阶段灰度）。true = 该阶段节点改为加载
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# .deer-flow/agents/ai-writing-<stage>/ 的 SOUL.md + config（admin 可在智能体
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# 管理页编辑人设/模型），false / 缺失 = 走旧的硬编码 prompt 路径。
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+57
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@@ -554,6 +554,9 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
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# 本轮新检索到的原始结果（统一在下方合并段转 Material、与旧素材去重）
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new_results: list[SearchResult] = []
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provider = None
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# 内网检索接口执行诊断（排查用，最终挂到 material_package["intranet_debug"]，前端忽略）。
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# 记录：是否配置 / 端点 / 每个检索词命中数与报错 / 原始响应片段 / 是否被采用为兜底。
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intranet_debug: dict | None = None
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if material_source == "notebook" and notebook_search_space_id:
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# ── 纯笔记模式 ──
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@@ -772,12 +775,27 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
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# 预跑好的内网结果兜底（省去"技能全失败后再串行等一遍内网"的时间）。需在 config.yaml
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# 的 ai_writing.intranet_search_url 配地址才启用；技能拿到结果则取消这个任务、不浪费。
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intranet_url = (ai_writing_config.get("intranet_search_url") or "").strip()
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# 诊断开关：开启后即使技能成功也照样跑完内网并记录完整结果（用于验证内网接口是否真能通）。
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intranet_debug_force = bool(ai_writing_config.get("intranet_search_debug", False))
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intranet_task: "asyncio.Task | None" = None
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intranet_holder: dict = {}
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intranet_debug = {
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"configured": bool(intranet_url),
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"endpoint": intranet_url or "(未配置 ai_writing.intranet_search_url)",
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"knowledge_base": ai_writing_config.get("intranet_knowledge_base", "samples"),
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"timeout": ai_writing_config.get("intranet_search_timeout", 600),
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"debug_force": intranet_debug_force,
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"status": "pending" if intranet_url else "not_configured",
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"queries": [], # 每个检索词：{query, count, error?}
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"errors": [], # provider 聚合的报错
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"raw_responses": [], # 解析失败时的原始响应片段
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"result_count": 0, # 内网总命中条数
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"used_as_fallback": False, # 内网结果是否被真正采用为素材
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}
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async def _run_intranet_search() -> list[SearchResult]:
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# 全程吞异常：并行内网任务**绝不**能把主流程带挂（实测内网接口可能瞬间失败）。
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# 失败只记 search_errors + 日志，返回空列表，交由技能结果或空结果文案兜底。
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# 失败只记 search_errors + 诊断 + 日志，返回空列表，交由技能结果或空结果文案兜底。
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try:
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from ..search.intranet_stub import IntranetSearchProvider
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@@ -795,31 +813,56 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
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return_exceptions=True,
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)
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collected: list[SearchResult] = []
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for res in gathered:
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for kw, res in zip(fb_keywords, gathered):
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if isinstance(res, Exception):
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logger.warning("[intranet-parallel] one keyword failed: %s", res)
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logger.warning("[intranet-parallel] keyword=%r failed: %s", kw, res)
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search_errors.append(f"{type(res).__name__}: {res}")
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intranet_debug["queries"].append({"query": kw, "count": 0, "error": f"{type(res).__name__}: {res}"})
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continue
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intranet_debug["queries"].append({"query": kw, "count": len(res or [])})
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collected.extend(res or [])
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intranet_debug["errors"] = list(dict.fromkeys(getattr(intranet, "errors", [])))[:5]
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intranet_debug["raw_responses"] = list(dict.fromkeys(getattr(intranet, "raw_responses", [])))[:3]
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intranet_debug["result_count"] = len(collected)
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intranet_debug["status"] = "ran"
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return collected
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except asyncio.CancelledError:
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intranet_debug["status"] = "cancelled"
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raise # 取消要正常传播（技能已拿到结果时主动取消）
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except Exception as e: # noqa: BLE001
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logger.warning("[intranet-parallel] intranet fallback crashed: %s", e, exc_info=True)
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search_errors.append(f"内网兜底异常：{type(e).__name__}: {e}")
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intranet_debug["status"] = "crashed"
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intranet_debug["errors"].append(f"{type(e).__name__}: {e}")
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return []
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if intranet_url:
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intranet_task = asyncio.create_task(_run_intranet_search())
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async def _cancel_intranet_task() -> None:
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"""技能已拿到素材：取消并行的内网检索任务，避免无谓占用 / 未取回异常告警。"""
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if intranet_task is not None and not intranet_task.done():
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intranet_task.cancel()
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"""技能已拿到素材：默认取消并行的内网检索任务，避免无谓占用。
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诊断模式（intranet_search_debug=true）下**不取消**，而是等它跑完拿到完整诊断，
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这样即使技能成功也能在 material_package.intranet_debug 里看到内网接口的真实返回。
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"""
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if intranet_task is None or intranet_task.done():
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if intranet_task is not None:
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try:
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await intranet_task
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except BaseException: # noqa: BLE001
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pass
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return
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if intranet_debug_force:
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try:
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await intranet_task
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except BaseException: # noqa: BLE001 — CancelledError / 任务内异常都吞掉
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except BaseException: # noqa: BLE001
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pass
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return
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intranet_task.cancel()
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try:
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await intranet_task
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except BaseException: # noqa: BLE001 — CancelledError / 任务内异常都吞掉
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pass
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for sk in ordered:
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added, error_detail = await _execute_skill(sk)
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@@ -871,6 +914,8 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
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fb_results = []
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search_errors.append(f"{type(e).__name__}: {e}")
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fb_added = _collect(fb_results)
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if intranet_debug is not None:
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intranet_debug["used_as_fallback"] = True
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intranet = intranet_holder.get("provider")
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if intranet is not None:
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search_errors.extend(getattr(intranet, "errors", []))
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@@ -1004,6 +1049,11 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
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pkg["skill_search_used"] = existing_pkg.get("skill_search_used", False)
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pkg["skill_search_steps"] = existing_pkg.get("skill_search_steps", [])
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# 内网检索接口执行诊断：挂进 material_package，随 values 事件回前端（前端忽略此字段，
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# 仅供在浏览器 devtools / 接口返回里排查内网接口到底有没有被调、返回/报错是什么）。
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if intranet_debug is not None:
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pkg["intranet_debug"] = intranet_debug
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# 容错告警：检索接口 / 模型调用出错时以非致命 notice 提示用户，不打断写作流程
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notices: list[dict] = []
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if kw_result.get("_llm_error"):
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