fix: 写作管线整批接收对话检索结果，不再自己检索
- 相关性过滤新增位置通道：收割器给每条材料标记来源消息位置，课题 请求之后的检索结果整批入池（与语言/词面无关）——线上中文课题 + 英文检索结果被纯词面匹配误杀 31/35 条，报告只带着 4 条残料在写 - 课题切换保护保留：新请求之前的旧课题检索仍按词面强过滤，不凑 门槛、不入报告；对话里找不到课题消息时退化为原词面过滤 - 删除死代码 HarvestPlusSearchMaterialProvider（retrieve=True 的 自检索补充分支，早已被显式忽略）：写作管线只负责写文件，检索 全部由对话完成成为结构保证 - 测试：锚点规则（澄清回答不充当新课题）、中文课题+英文资料整批 保留、课题切换旧检索过滤等场景，25 个用例入库（此前该测试文件 一直被 tests/* 忽略规则挡在版本库外）
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@@ -132,6 +132,8 @@ offline-backend-20260512/backend/tests/*
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!offline-backend-20260512/backend/tests/test_writing_rewrite_prompt.py
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# LLM call timeout retry classification regression coverage.
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!offline-backend-20260512/backend/tests/test_llm_error_handling_middleware.py
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# Writing-mode pipeline conversation-material regressions.
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!offline-backend-20260512/backend/tests/test_deep_research_report_tool.py
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deploy
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.history
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+18
-9
@@ -72,9 +72,7 @@ async def harvest_thread_materials(
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visible text (its collection小结) and doubles as a fallback material when
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the thread produced no harvestable tool results.
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"""
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checkpoint_tuple = await checkpointer.aget_tuple(
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{"configurable": {"thread_id": runtime_thread_id, "checkpoint_ns": ""}}
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)
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checkpoint_tuple = await checkpointer.aget_tuple({"configurable": {"thread_id": runtime_thread_id, "checkpoint_ns": ""}})
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if checkpoint_tuple is None:
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logger.warning("deep-research harvest: no checkpoint for thread %s", runtime_thread_id)
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return [], ""
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@@ -121,9 +119,14 @@ def harvest_messages_materials(
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def _harvest_tool_messages(messages: list[Any], *, query: str) -> list[ResearchMaterial]:
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"""Convert every harvestable tool result in the thread into materials."""
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"""Convert every harvestable tool result in the thread into materials.
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每条材料在 ``metadata["message_index"]`` 记录其来源工具消息在对话中的
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位置——写作管线据此区分「课题请求之后检索的（属于当前课题）」与
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「课题切换前的旧检索」（后者才做词面相关度过滤）。
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"""
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materials: list[ResearchMaterial] = []
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for message in messages:
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for message_index, message in enumerate(messages):
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if _message_type(message) != "tool":
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continue
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tool_name = str(_message_name(message) or "")
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@@ -140,15 +143,20 @@ def _harvest_tool_messages(messages: list[Any], *, query: str) -> list[ResearchM
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if isinstance(item, dict):
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material = result_to_material(item, query=envelope_query)
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material.metadata["retrieval_channel"] = "chat_web_search"
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material.metadata["message_index"] = message_index
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materials.append(material)
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continue
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materials.extend(
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_harvest_generic_payload(payload, tool_name=tool_name, query=query)
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)
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materials.extend(_harvest_generic_payload(payload, tool_name=tool_name, query=query, message_index=message_index))
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return materials
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def _harvest_generic_payload(payload: Any, *, tool_name: str, query: str) -> list[ResearchMaterial]:
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def _harvest_generic_payload(
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payload: Any,
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*,
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tool_name: str,
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query: str,
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message_index: int | None = None,
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) -> list[ResearchMaterial]:
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"""Normalize a non-web_search tool payload (skill/knowledge/bash/MCP)."""
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source_type = _TOOL_SOURCE_TYPES.get(tool_name, "other")
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materials: list[ResearchMaterial] = []
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@@ -177,6 +185,7 @@ def _harvest_generic_payload(payload: Any, *, tool_name: str, query: str) -> lis
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metadata={
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"retrieval_channel": "chat_tool",
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"tool_name": tool_name,
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**({"message_index": message_index} if message_index is not None else {}),
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},
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)
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)
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+88
-64
@@ -34,7 +34,7 @@ import re
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from typing import Annotated, Any
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from langchain.tools import InjectedToolCallId, ToolRuntime, tool
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from langchain_core.messages import ToolMessage
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from langchain_core.messages import HumanMessage, ToolMessage
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from langgraph.types import Command
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from langgraph.typing import ContextT
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@@ -42,7 +42,6 @@ from deerflow.agents.deep_research.adapters.artifacts import ThreadOutputsArtifa
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from deerflow.agents.deep_research.adapters.context import DeerFlowContextCompressor
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from deerflow.agents.deep_research.adapters.image import build_image_provider
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from deerflow.agents.deep_research.adapters.llm import DeerFlowCompletionBackend
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from deerflow.agents.deep_research.adapters.material_provider import DeerFlowMaterialProvider
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from deerflow.agents.deep_research.collection.thread_harvester import (
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StaticHarvestMaterialProvider,
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harvest_messages_materials,
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@@ -167,53 +166,6 @@ class StreamWriterEventSink:
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logger.debug("deep_research_report: custom stream frame dropped", exc_info=True)
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class HarvestPlusSearchMaterialProvider:
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"""对话收割池优先 + 自检索补充的合并资料源（``retrieve=True`` 分支）。
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与 :class:`StaticHarvestMaterialProvider` 同协议：每个子查询返回合并池，
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交由 runner 的 dedupe / compress 阶段裁剪。自检索单项失败不致命——
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退化为只用对话资料。
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"""
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def __init__(self, harvested: list[ResearchMaterial], search_provider: DeerFlowMaterialProvider) -> None:
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self._harvested = list(harvested)
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self._search = search_provider
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@property
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def material_count(self) -> int:
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return len(self._harvested)
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async def search(
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self,
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query: str,
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*,
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domains: list[str] | None = None,
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limit: int = 8,
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user_id: str = "",
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session_id: str = "",
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cancellation=None,
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) -> list[ResearchMaterial]:
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from deerflow.agents.deep_research.adapters.material import dedupe_materials
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if cancellation is not None:
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cancellation.raise_if_cancelled()
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combined = list(self._harvested)
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try:
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combined.extend(
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await self._search.search(
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query,
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domains=domains,
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limit=limit,
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user_id=user_id,
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session_id=session_id,
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cancellation=cancellation,
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)
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)
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except Exception: # noqa: BLE001 - 自检索失败退化为对话资料
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logger.warning("deep_research_report: supplementary search failed", exc_info=True)
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return dedupe_materials(combined)
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# 写作模式常在「不用搜索 / 对话资料很少」时开写。工作台默认的「材料不足就
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# 写明资料不足」会把报告写成元分析；这里强制用领域知识补全正文。
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_KNOWLEDGE_WRITE_INSTRUCTION = (
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@@ -330,16 +282,91 @@ def _is_topic_relevant(material: ResearchMaterial, strong: frozenset[str], weak:
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def _filter_relevant_materials(
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materials: list[ResearchMaterial], *, topic: str
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materials: list[ResearchMaterial],
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*,
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topic: str,
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anchor_index: int | None = None,
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) -> tuple[list[ResearchMaterial], int]:
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"""按当前主题过滤收割池；返回 ``(相关池, 被剔除条数)``。"""
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"""按当前主题过滤收割池；返回 ``(相关池, 被剔除条数)``。
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两条保留通道（满足其一即入池）：
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1. **位置**：材料来自课题请求消息（``anchor_index``）之后的工具消息——
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那是模型为本课题检索的，与词面/语言无关，一律保留。线上实测中文课题
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+ 英文检索结果会被纯词面匹配整批误杀（35 条只留 4 条）。
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2. **词面**：材料词面命中课题（强词/标题/正文规则）——兜住课题切换前
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的旧检索里仍与当前课题相关的部分。
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``anchor_index`` 之前的旧课题检索（词面不命中）继续被剔除（P1：旧课题
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资料不得凑门槛、不得入报告）。``anchor_index=None`` 或负数（对话里找不
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到课题请求消息，如纯工具消息的旧调用方）退化为纯词面过滤。
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"""
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strong, weak = _topic_terms(topic)
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if not strong and not weak:
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return list(materials), 0
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relevant = [m for m in materials if _is_topic_relevant(m, strong, weak)]
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positional = anchor_index is not None and anchor_index >= 0
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relevant: list[ResearchMaterial] = []
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for material in materials:
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message_index = material.metadata.get("message_index")
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if positional and isinstance(message_index, int) and message_index > anchor_index:
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relevant.append(material)
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continue
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if _is_topic_relevant(material, strong, weak):
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relevant.append(material)
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return relevant, len(materials) - len(relevant)
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def _anchor_visible_human(message: Any) -> bool:
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if not isinstance(message, HumanMessage):
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return False
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kwargs = getattr(message, "additional_kwargs", None) or {}
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return not kwargs.get("hide_from_ui")
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def _anchor_is_clarification_result(message: Any) -> bool:
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return isinstance(message, ToolMessage) and str(getattr(message, "name", "") or "") == "ask_clarification"
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def _anchor_message_text(message: Any) -> str:
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content = getattr(message, "content", "")
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if isinstance(content, list):
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parts: list[str] = []
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for part in content:
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if isinstance(part, str):
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parts.append(part)
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elif isinstance(part, dict) and isinstance(part.get("text"), str):
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parts.append(part["text"])
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return " ".join(parts).strip()
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return str(content or "").strip()
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def topic_anchor_index(messages: list[Any], *, topic: str = "") -> int:
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"""课题请求消息在对话中的位置（写作模式首报轮的资料过滤锚点）。
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规则与 ``writing_mode_report_middleware.extract_report_topic`` 一致：取
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最新的可见人类消息、跳过对 ``ask_clarification`` 的直接回答（紧跟澄清
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ToolMessage 的人类消息不是新课题）。传入 ``topic`` 时优先返回与其文本
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前缀一致的消息位置（模型原样复述课题的常见情形）。找不到返回 -1。
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"""
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normalized_topic = " ".join(str(topic or "").split())
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prefix = normalized_topic[:80]
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fallback = -1
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for index in range(len(messages) - 1, -1, -1):
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message = messages[index]
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if not _anchor_visible_human(message):
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continue
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if index > 0 and _anchor_is_clarification_result(messages[index - 1]):
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continue
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text = " ".join(_anchor_message_text(message).split())
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if not text:
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continue
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if prefix and text.startswith(prefix):
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return index
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if fallback < 0:
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fallback = index
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return fallback
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def _safe_stream_writer() -> Any:
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"""获取当前 run 的 custom 事件写入器；非流式上下文返回 None。"""
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try:
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@@ -408,16 +435,18 @@ async def deep_research_report_tool(
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state = getattr(runtime, "state", None) or {}
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conversation_messages = state.get("messages") or []
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harvested, _agent_summary = harvest_messages_materials(conversation_messages, query=query)
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# 按当前主题强过滤：旧课题的检索结果不计门槛、不入报告（课题切换场景）。
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materials, dropped_count = _filter_relevant_materials(harvested, topic=query)
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# 课题请求之后的检索结果整批保留（那是模型为本课题检索的）；只有课题
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# 切换前的旧检索才按词面强过滤（旧课题资料不得凑门槛、不得入报告）。
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anchor_index = topic_anchor_index(conversation_messages, topic=query)
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materials, dropped_count = _filter_relevant_materials(harvested, topic=query, anchor_index=anchor_index)
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harvested_count = len(materials)
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logger.info(
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"[deep_research_report] thread=%s harvested %d materials from conversation "
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"(topic-relevant %d, off-topic dropped %d)",
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"[deep_research_report] thread=%s harvested %d materials from conversation (topic-relevant %d, off-topic dropped %d, anchor_index=%d)",
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thread_id,
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len(harvested),
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harvested_count,
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dropped_count,
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anchor_index,
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)
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if harvested_count < _MIN_HARVESTED_MATERIALS:
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@@ -430,8 +459,7 @@ async def deep_research_report_tool(
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if retrieve is True:
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logger.info(
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"[deep_research_report] thread=%s ignoring retrieve=true; "
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"writing-mode pipeline writes from conversation materials only",
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"[deep_research_report] thread=%s ignoring retrieve=true; writing-mode pipeline writes from conversation materials only",
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thread_id,
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)
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materials_provider: Any = StaticHarvestMaterialProvider(materials)
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@@ -520,11 +548,7 @@ async def deep_research_report_tool(
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)
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source_count = len(result.source_ids or [])
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summary = (
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f"研究报告已完成：基于 {source_count} 条资料（{material_source}）按「通用分析结构」撰写，"
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f"已写入 {REPORT_VIRTUAL_PATH} 并展示给用户。"
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"请用 2-3 句话向用户简要总结报告的核心结论，不要复述全文。"
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)
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summary = f"研究报告已完成：基于 {source_count} 条资料（{material_source}）按「通用分析结构」撰写，已写入 {REPORT_VIRTUAL_PATH} 并展示给用户。请用 2-3 句话向用户简要总结报告的核心结论，不要复述全文。"
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logger.info(
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"[deep_research_report] thread=%s completed (sources=%d, report_chars=%d)",
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thread_id,
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@@ -540,10 +564,10 @@ async def deep_research_report_tool(
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__all__ = [
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"HarvestPlusSearchMaterialProvider",
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"REPORT_VIRTUAL_PATH",
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"StreamWriterEventSink",
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"build_writing_research_config",
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"deep_research_report_tool",
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"is_writing_mode_report_path",
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"topic_anchor_index",
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]
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@@ -0,0 +1,649 @@
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"""写作模式内联深度研究报告工具的单元测试。
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覆盖：默认配置组装（通用分析结构 / 继承聊天模型）、事件转发（custom 通道帧
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格式/过滤/容错）、工具主体（收割优先——资料足够直接写 / 不足也直接开写 /
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retrieve 参数被忽略、按当前主题的相关度强过滤、线程解析、失败路径、
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artifacts 更新），以及 lead / agent-only 两条 prompt 模板的写作模式首轮合约。
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"""
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from __future__ import annotations
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import asyncio
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import json
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from types import SimpleNamespace
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from typing import Any
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from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
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from deerflow.agents.deep_research.collection.thread_harvester import StaticHarvestMaterialProvider
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from deerflow.agents.deep_research.types import DeepResearchRequest, DeepResearchResult
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from deerflow.persistence.report_structures.defaults import GENERAL_OUTLINE
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from deerflow.tools.builtins import deep_research_report_tool as tool_module
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from deerflow.tools.builtins.deep_research_report_tool import (
|
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_KNOWLEDGE_WRITE_INSTRUCTION,
|
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StreamWriterEventSink,
|
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build_writing_research_config,
|
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deep_research_report_tool,
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)
|
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|
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_DEFAULT_TOPIC = "新能源汽车市场分析"
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_OFF_TOPIC = "养老产业发展研究"
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# ── 配置组装 ─────────────────────────────────────────────────────────────────
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||||
|
||||
|
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def test_writing_research_config_defaults_to_general_outline_basic() -> None:
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cfg = build_writing_research_config()
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|
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assert cfg.mode == "basic"
|
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assert cfg.custom_outline == GENERAL_OUTLINE
|
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assert cfg.structure_mode == "adaptive"
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assert cfg.generate_images is False
|
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assert cfg.report_instruction == _KNOWLEDGE_WRITE_INSTRUCTION
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assert "必须基于模型自身的领域知识" in cfg.report_instruction
|
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|
||||
|
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def test_writing_research_config_maps_focus_to_report_instruction() -> None:
|
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cfg = build_writing_research_config(focus=" 侧重对比分析，3000 字左右 ")
|
||||
|
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assert cfg.report_instruction is not None
|
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assert cfg.report_instruction.startswith("侧重对比分析，3000 字左右")
|
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assert _KNOWLEDGE_WRITE_INSTRUCTION in cfg.report_instruction
|
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# 空白 focus 仍保留知识补全指令，不产生空行前缀
|
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empty_focus = build_writing_research_config(focus=" ")
|
||||
assert empty_focus.report_instruction == _KNOWLEDGE_WRITE_INSTRUCTION
|
||||
|
||||
|
||||
# ── 事件转发 sink ────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def _capture_writer() -> tuple[list[dict], object]:
|
||||
frames: list[dict] = []
|
||||
|
||||
def writer(frame: dict) -> None:
|
||||
frames.append(frame)
|
||||
|
||||
return frames, writer
|
||||
|
||||
|
||||
def test_event_sink_wraps_forwardable_events() -> None:
|
||||
frames, writer = _capture_writer()
|
||||
sink = StreamWriterEventSink(writer, session_id="chat-t1", job_id="call-1")
|
||||
|
||||
asyncio.run(sink.emit("phase_changed", phase="planning", payload={"to": "planning"}))
|
||||
|
||||
assert len(frames) == 1
|
||||
frame = frames[0]
|
||||
assert frame["type"] == "deep_research_report"
|
||||
assert frame["event"]["type"] == "phase_changed"
|
||||
assert frame["event"]["phase"] == "planning"
|
||||
assert frame["event"]["payload"] == {"to": "planning"}
|
||||
assert frame["event"]["jobId"] == "call-1"
|
||||
|
||||
|
||||
def test_event_sink_drops_noise_events() -> None:
|
||||
frames, writer = _capture_writer()
|
||||
sink = StreamWriterEventSink(writer, session_id="s", job_id="j")
|
||||
|
||||
asyncio.run(sink.emit("model_usage", payload={"tokens": 1}))
|
||||
asyncio.run(sink.emit("heartbeat"))
|
||||
|
||||
assert frames == []
|
||||
|
||||
|
||||
def test_event_sink_forwards_live_report_deltas_only() -> None:
|
||||
frames, writer = _capture_writer()
|
||||
sink = StreamWriterEventSink(writer, session_id="s", job_id="j")
|
||||
|
||||
asyncio.run(sink.publish_live("report_delta", phase="writing", payload={"delta": "第一"}))
|
||||
asyncio.run(sink.publish_live("summary_delta", phase="summarizing", payload={"delta": "x"}))
|
||||
|
||||
assert len(frames) == 1
|
||||
assert frames[0]["event"]["type"] == "report_delta"
|
||||
assert frames[0]["event"]["live"] is True
|
||||
|
||||
|
||||
def test_event_sink_survives_writer_failure() -> None:
|
||||
def broken_writer(frame: dict) -> None:
|
||||
raise RuntimeError("stream closed")
|
||||
|
||||
sink = StreamWriterEventSink(broken_writer, session_id="s", job_id="j")
|
||||
|
||||
event = asyncio.run(sink.emit("report_completed", payload={"sourceCount": 3}))
|
||||
|
||||
assert event.type == "report_completed" # 事件本体仍然返回
|
||||
asyncio.run(sink.publish_live("report_delta", payload={"delta": "x"})) # 不抛异常
|
||||
|
||||
|
||||
# ── 工具主体 ─────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def _material_message(index: int, topic: str = _DEFAULT_TOPIC) -> ToolMessage:
|
||||
"""一条可被收割器识别、且与 ``topic`` 词面相关的工具结果消息。"""
|
||||
return ToolMessage(
|
||||
content=json.dumps(
|
||||
{
|
||||
"results": [
|
||||
{
|
||||
"title": f"{topic}：资料{index}",
|
||||
"content": f"第 {index} 条关于{topic}的对话检索资料，包含要点分析。",
|
||||
"url": f"https://example.com/{index}",
|
||||
}
|
||||
]
|
||||
},
|
||||
ensure_ascii=False,
|
||||
),
|
||||
name="weknora_search",
|
||||
tool_call_id=f"call-m{index}",
|
||||
)
|
||||
|
||||
|
||||
def _fake_runtime(
|
||||
thread_id: str = "thread-1",
|
||||
messages: list | None = None,
|
||||
) -> SimpleNamespace:
|
||||
return SimpleNamespace(
|
||||
context={"thread_id": thread_id},
|
||||
config={"configurable": {"thread_id": thread_id}},
|
||||
state={"messages": messages if messages is not None else [_material_message(i) for i in range(1, 4)]},
|
||||
)
|
||||
|
||||
|
||||
class _FakeEngine:
|
||||
"""替代 DeepResearchEngine：记录请求与 adapters 并返回可控结果。"""
|
||||
|
||||
instances: list[_FakeEngine] = []
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.requests: list[DeepResearchRequest] = []
|
||||
self.adapters: Any = None # noqa: RUF012 - 测试桩
|
||||
self.result = DeepResearchResult(
|
||||
report_markdown="# 报告\n正文",
|
||||
source_ids=["s1", "s2", "s3"],
|
||||
)
|
||||
self.error: Exception | None = None
|
||||
_FakeEngine.instances.append(self)
|
||||
|
||||
async def run(self, request, *, adapters, cancellation=None) -> DeepResearchResult:
|
||||
self.requests.append(request)
|
||||
self.adapters = adapters
|
||||
if self.error is not None:
|
||||
raise self.error
|
||||
return self.result
|
||||
|
||||
|
||||
def _patch_tool_dependencies(monkeypatch, *, user_id: str = "u1", engine=None) -> list[dict]:
|
||||
frames, writer = _capture_writer()
|
||||
monkeypatch.setattr(tool_module, "DeepResearchEngine", lambda: engine or _FakeEngine())
|
||||
monkeypatch.setattr(tool_module, "resolve_path_user_id", lambda thread_id: user_id)
|
||||
monkeypatch.setattr(tool_module, "_safe_stream_writer", lambda: writer)
|
||||
monkeypatch.setattr(
|
||||
tool_module,
|
||||
"DeerFlowCompletionBackend",
|
||||
lambda *args, **kwargs: SimpleNamespace(usage_totals={}),
|
||||
)
|
||||
monkeypatch.setattr(
|
||||
tool_module,
|
||||
"DeerFlowContextCompressor",
|
||||
lambda **kwargs: SimpleNamespace(captured=kwargs),
|
||||
)
|
||||
monkeypatch.setattr(tool_module, "ThreadOutputsArtifactWriter", lambda *args, **kwargs: SimpleNamespace(captured=(args, kwargs)))
|
||||
monkeypatch.setattr(tool_module, "build_image_provider", lambda: None)
|
||||
return frames
|
||||
|
||||
|
||||
def test_tool_writes_with_harvested_materials_when_sufficient(monkeypatch) -> None:
|
||||
frames = _patch_tool_dependencies(monkeypatch)
|
||||
|
||||
command = asyncio.run(
|
||||
deep_research_report_tool.coroutine(
|
||||
_fake_runtime("thread-9", messages=[_material_message(i) for i in range(1, 5)]),
|
||||
"call-1",
|
||||
_DEFAULT_TOPIC,
|
||||
focus="侧重销量对比",
|
||||
)
|
||||
)
|
||||
|
||||
engine = _FakeEngine.instances[-1]
|
||||
assert len(engine.requests) == 1
|
||||
request = engine.requests[0]
|
||||
assert request.query == "新能源汽车市场分析"
|
||||
assert request.config.custom_outline == GENERAL_OUTLINE
|
||||
assert request.config.report_instruction is not None
|
||||
assert request.config.report_instruction.startswith("侧重销量对比")
|
||||
assert _KNOWLEDGE_WRITE_INSTRUCTION in request.config.report_instruction
|
||||
assert request.session_id == "chat-thread-9"
|
||||
|
||||
# 资料足够 → 只用收割池，不触发自检索
|
||||
assert isinstance(engine.adapters.materials, StaticHarvestMaterialProvider)
|
||||
assert engine.adapters.materials.material_count == 4
|
||||
|
||||
update = command.update
|
||||
assert update["artifacts"] == ["/mnt/user-data/outputs/report.md"]
|
||||
tool_message = update["messages"][0]
|
||||
assert tool_message.tool_call_id == "call-1"
|
||||
assert "对话资料" in tool_message.content
|
||||
assert "3" in tool_message.content # 来源条数写入总结
|
||||
|
||||
# 终态帧已发出（success）
|
||||
assert any(f.get("event", {}).get("type") == "tool_finished" and f["event"]["payload"]["status"] == "success" for f in frames)
|
||||
|
||||
|
||||
def test_tool_writes_immediately_when_harvested_materials_insufficient(monkeypatch) -> None:
|
||||
"""资料不足也不再询问用户，直接用现有对话资料开写。"""
|
||||
frames = _patch_tool_dependencies(monkeypatch)
|
||||
|
||||
command = asyncio.run(
|
||||
deep_research_report_tool.coroutine(
|
||||
_fake_runtime("thread-a", messages=[_material_message(1, topic="课题")]),
|
||||
"call-2",
|
||||
"课题",
|
||||
)
|
||||
)
|
||||
|
||||
engine = _FakeEngine.instances[-1]
|
||||
assert isinstance(engine.adapters.materials, StaticHarvestMaterialProvider)
|
||||
assert engine.adapters.materials.material_count == 1
|
||||
|
||||
update = command.update
|
||||
assert update["artifacts"] == ["/mnt/user-data/outputs/report.md"]
|
||||
tool_message = update["messages"][0]
|
||||
assert tool_message.tool_call_id == "call-2"
|
||||
assert "对话资料" in tool_message.content
|
||||
assert "ask_clarification" not in tool_message.content
|
||||
assert any(f.get("event", {}).get("type") == "tool_finished" and f["event"]["payload"]["status"] == "success" for f in frames)
|
||||
|
||||
|
||||
def test_tool_retrieve_true_still_writes_from_conversation_only(monkeypatch) -> None:
|
||||
"""写作管线忽略 retrieve=true：检索留给对话工具，这里只写文件。"""
|
||||
_patch_tool_dependencies(monkeypatch)
|
||||
|
||||
command = asyncio.run(
|
||||
deep_research_report_tool.coroutine(
|
||||
_fake_runtime("thread-b", messages=[_material_message(1, topic="课题")]),
|
||||
"call-3",
|
||||
"课题",
|
||||
retrieve=True,
|
||||
)
|
||||
)
|
||||
|
||||
engine = _FakeEngine.instances[-1]
|
||||
assert isinstance(engine.adapters.materials, StaticHarvestMaterialProvider)
|
||||
assert engine.adapters.materials.material_count == 1
|
||||
|
||||
update = command.update
|
||||
assert update["artifacts"] == ["/mnt/user-data/outputs/report.md"]
|
||||
assert "对话资料" in update["messages"][0].content
|
||||
assert "补充检索" not in update["messages"][0].content
|
||||
|
||||
|
||||
def test_tool_drops_off_topic_materials_from_pool_and_threshold(monkeypatch) -> None:
|
||||
"""课题切换：旧课题检索结果既不计入门槛，也不进报告资料池。"""
|
||||
_patch_tool_dependencies(monkeypatch)
|
||||
messages = [
|
||||
*[_material_message(i) for i in range(1, 6)], # 旧课题（新能源）5 条
|
||||
*[_material_message(i, topic=_OFF_TOPIC) for i in range(11, 14)], # 当前课题 3 条
|
||||
]
|
||||
|
||||
command = asyncio.run(
|
||||
deep_research_report_tool.coroutine(
|
||||
_fake_runtime("thread-t", messages=messages),
|
||||
"call-t",
|
||||
_OFF_TOPIC,
|
||||
)
|
||||
)
|
||||
|
||||
engine = _FakeEngine.instances[-1]
|
||||
assert isinstance(engine.adapters.materials, StaticHarvestMaterialProvider)
|
||||
# 只有当前课题的 3 条入池；旧课题 5 条被强过滤剔除
|
||||
assert engine.adapters.materials.material_count == 3
|
||||
assert command.update["artifacts"] == ["/mnt/user-data/outputs/report.md"]
|
||||
|
||||
|
||||
def test_tool_writes_immediately_when_all_materials_off_topic(monkeypatch) -> None:
|
||||
"""收割池非空但全部偏题 → 相关数 0，仍直接开写，不再问用户。"""
|
||||
_patch_tool_dependencies(monkeypatch)
|
||||
|
||||
command = asyncio.run(
|
||||
deep_research_report_tool.coroutine(
|
||||
_fake_runtime("thread-o", messages=[_material_message(i) for i in range(1, 6)]),
|
||||
"call-o",
|
||||
_OFF_TOPIC,
|
||||
)
|
||||
)
|
||||
|
||||
engine = _FakeEngine.instances[-1]
|
||||
assert isinstance(engine.adapters.materials, StaticHarvestMaterialProvider)
|
||||
assert engine.adapters.materials.material_count == 0
|
||||
assert command.update["artifacts"] == ["/mnt/user-data/outputs/report.md"]
|
||||
assert "ask_clarification" not in command.update["messages"][0].content
|
||||
|
||||
|
||||
def test_relevance_filter_rules() -> None:
|
||||
"""词面相关度判定的最小规则集（强词 / 标题 / 正文 / 短主题兜底）。"""
|
||||
from deerflow.agents.deep_research.types import ResearchMaterial
|
||||
|
||||
strong, weak = tool_module._topic_terms(_DEFAULT_TOPIC)
|
||||
assert _DEFAULT_TOPIC in strong # 整段短语是强词
|
||||
assert "新能" in weak and "分析" in weak
|
||||
|
||||
def _mat(title: str, content: str) -> ResearchMaterial:
|
||||
return ResearchMaterial(
|
||||
id=title,
|
||||
query="q",
|
||||
title=title,
|
||||
url=None,
|
||||
raw_content=content,
|
||||
snippet=None,
|
||||
source="t",
|
||||
source_type="other",
|
||||
content_hash=title,
|
||||
)
|
||||
|
||||
# 强词命中标题
|
||||
assert tool_module._is_topic_relevant(_mat(f"{_DEFAULT_TOPIC}：综述", "无关键词"), strong, weak) is True
|
||||
# 标题两个不同 bigram
|
||||
assert tool_module._is_topic_relevant(_mat("新能源汽车销量榜", "……"), strong, weak) is True
|
||||
# 完全偏题
|
||||
assert tool_module._is_topic_relevant(_mat("养老产业白皮书", "养老服务体系建设"), strong, weak) is False
|
||||
# 正文密集命中（≥4 个不同 bigram）
|
||||
assert tool_module._is_topic_relevant(_mat("行业观察", "新能源汽车市场分析"), strong, weak) is True
|
||||
# 短主题兜底：唯一词命中即相关
|
||||
s2, w2 = tool_module._topic_terms("课题")
|
||||
assert tool_module._is_topic_relevant(_mat("课题：资料1", "关于课题的要点"), s2, w2) is True
|
||||
|
||||
|
||||
# ── 课题锚点：请求之后的对话检索整批保留（语言/词面无关） ────────────────────
|
||||
|
||||
|
||||
def _search_envelope_message(index: int, *, query: str, titles: list[str]) -> ToolMessage:
|
||||
"""一条 web_search 工具结果信封（收割器走最高保真映射分支）。"""
|
||||
return ToolMessage(
|
||||
content=json.dumps(
|
||||
{
|
||||
"query": query,
|
||||
"results": [
|
||||
{
|
||||
"title": title,
|
||||
"content": f"{title} — key findings and technical details.",
|
||||
"url": f"https://example.com/{index}-{i}",
|
||||
}
|
||||
for i, title in enumerate(titles)
|
||||
],
|
||||
},
|
||||
ensure_ascii=False,
|
||||
),
|
||||
name="web_search",
|
||||
tool_call_id=f"call-s{index}",
|
||||
)
|
||||
|
||||
|
||||
def test_topic_anchor_index_rules() -> None:
|
||||
"""锚点 = 最新的非澄清回答人类消息；澄清回答不充当新课题。"""
|
||||
messages = [
|
||||
HumanMessage(content="写人工智能领域的论文md"),
|
||||
AIMessage(
|
||||
content="",
|
||||
tool_calls=[{"id": "q1", "name": "ask_clarification", "args": {"question": "方向？"}, "type": "tool_call"}],
|
||||
),
|
||||
ToolMessage(content="需要澄清", tool_call_id="q1", name="ask_clarification"),
|
||||
HumanMessage(content="方向：AIGC；篇幅：3000字"),
|
||||
HumanMessage(content="写人工智能领域的论文md"),
|
||||
]
|
||||
# 文本前缀命中课题原文
|
||||
assert tool_module.topic_anchor_index(messages, topic="写人工智能领域的论文md") == 4
|
||||
# 无 topic 匹配时回落「最新的非回答人类消息」——回答被跳过
|
||||
messages[-1] = HumanMessage(content="补充：要英文文献")
|
||||
assert tool_module.topic_anchor_index(messages) == 4
|
||||
assert tool_module.topic_anchor_index([]) == -1
|
||||
|
||||
|
||||
def test_tool_keeps_conversation_searches_after_topic_request(monkeypatch) -> None:
|
||||
"""课题请求之后的对话检索结果整批入池——中文课题 + 英文检索结果不再被
|
||||
纯词面匹配误杀（线上 35 条只留 4 条的根因）。"""
|
||||
_patch_tool_dependencies(monkeypatch)
|
||||
messages = [
|
||||
HumanMessage(content="写一篇人工智能领域的论文md"),
|
||||
_search_envelope_message(
|
||||
1,
|
||||
query="AIGC generative AI survey 2024",
|
||||
titles=[
|
||||
"Sora: A Review of Text-to-Video Generation Models",
|
||||
"Diffusion Transformers for Image Synthesis",
|
||||
"Challenges and Ethics of AI-Generated Content",
|
||||
],
|
||||
),
|
||||
_search_envelope_message(
|
||||
2,
|
||||
query="LLM alignment RLHF DPO",
|
||||
titles=["Direct Preference Optimization: A Survey"],
|
||||
),
|
||||
]
|
||||
|
||||
command = asyncio.run(
|
||||
deep_research_report_tool.coroutine(
|
||||
_fake_runtime("thread-keep", messages=messages),
|
||||
"call-keep",
|
||||
"写一篇人工智能领域的论文md",
|
||||
)
|
||||
)
|
||||
|
||||
engine = _FakeEngine.instances[-1]
|
||||
assert isinstance(engine.adapters.materials, StaticHarvestMaterialProvider)
|
||||
# 4 条英文资料全部进入写作池（锚点=课题请求，之后的检索一律保留）
|
||||
assert engine.adapters.materials.material_count == 4
|
||||
assert command.update["artifacts"] == ["/mnt/user-data/outputs/report.md"]
|
||||
|
||||
|
||||
def test_tool_still_drops_old_topic_searches_before_new_request(monkeypatch) -> None:
|
||||
"""课题切换：新请求之前的旧课题检索仍被词面强过滤，不凑门槛不入报告。"""
|
||||
_patch_tool_dependencies(monkeypatch)
|
||||
messages = [
|
||||
HumanMessage(content="写养老产业发展研究报告"),
|
||||
*[_material_message(i, topic=_OFF_TOPIC) for i in range(1, 4)], # 旧课题 3 条
|
||||
HumanMessage(content="改成写新能源汽车市场分析"),
|
||||
*[_material_message(i, topic=_DEFAULT_TOPIC) for i in range(11, 14)], # 当前课题 3 条
|
||||
]
|
||||
|
||||
asyncio.run(
|
||||
deep_research_report_tool.coroutine(
|
||||
_fake_runtime("thread-switch", messages=messages),
|
||||
"call-switch",
|
||||
_DEFAULT_TOPIC,
|
||||
)
|
||||
)
|
||||
|
||||
engine = _FakeEngine.instances[-1]
|
||||
assert isinstance(engine.adapters.materials, StaticHarvestMaterialProvider)
|
||||
# 旧课题 3 条（锚点之前、词面不命中）被剔除；当前课题 3 条保留
|
||||
assert engine.adapters.materials.material_count == 3
|
||||
|
||||
|
||||
def test_tool_retrieve_false_writes_with_existing_materials(monkeypatch) -> None:
|
||||
frames = _patch_tool_dependencies(monkeypatch)
|
||||
engines_before = len(_FakeEngine.instances)
|
||||
|
||||
command = asyncio.run(
|
||||
deep_research_report_tool.coroutine(
|
||||
_fake_runtime("thread-c", messages=[]), # 极端：一条资料也没有
|
||||
"call-4",
|
||||
"课题",
|
||||
retrieve=False,
|
||||
)
|
||||
)
|
||||
|
||||
# 用户明确拒绝检索 → 即使收割为空也直接进入管线（runner 有空资料兜底）
|
||||
engine = _FakeEngine.instances[-1]
|
||||
assert len(_FakeEngine.instances) == engines_before + 1
|
||||
assert isinstance(engine.adapters.materials, StaticHarvestMaterialProvider)
|
||||
assert engine.adapters.materials.material_count == 0
|
||||
assert "对话资料" in command.update["messages"][0].content
|
||||
assert any(f.get("event", {}).get("type") == "tool_finished" and f["event"]["payload"]["status"] == "success" for f in frames)
|
||||
|
||||
|
||||
def test_tool_reports_engine_failure_without_artifacts(monkeypatch) -> None:
|
||||
failing = _FakeEngine()
|
||||
failing.error = RuntimeError("检索服务不可用")
|
||||
frames = _patch_tool_dependencies(monkeypatch, engine=failing)
|
||||
|
||||
command = asyncio.run(deep_research_report_tool.coroutine(_fake_runtime("thread-x"), "call-5", _DEFAULT_TOPIC))
|
||||
|
||||
update = command.update
|
||||
assert "artifacts" not in update
|
||||
assert "检索服务不可用" in update["messages"][0].content
|
||||
assert any(f.get("event", {}).get("type") == "tool_finished" and f["event"]["payload"]["status"] == "error" for f in frames)
|
||||
|
||||
|
||||
def test_tool_errors_cleanly_without_thread_id(monkeypatch) -> None:
|
||||
_patch_tool_dependencies(monkeypatch)
|
||||
runtime = SimpleNamespace(context={}, config={}, state={"messages": []})
|
||||
engines_before = len(_FakeEngine.instances)
|
||||
|
||||
command = asyncio.run(deep_research_report_tool.coroutine(runtime, "call-6", "课题"))
|
||||
|
||||
update = command.update
|
||||
assert "artifacts" not in update
|
||||
assert "thread id" in update["messages"][0].content
|
||||
# 未触达引擎
|
||||
assert len(_FakeEngine.instances) == engines_before
|
||||
|
||||
|
||||
# ── 模型继承（P2：管线用聊天选的模型与思考开关） ─────────────────────────────
|
||||
|
||||
|
||||
def test_writing_research_config_inherits_chat_model() -> None:
|
||||
cfg = build_writing_research_config(thinking_enabled=True, model_name="qwen3.6")
|
||||
|
||||
assert cfg.fast_model == "qwen3.6"
|
||||
assert cfg.smart_model == "qwen3.6"
|
||||
assert cfg.strategic_model == "qwen3.6"
|
||||
assert cfg.thinking_enabled is True
|
||||
# 缺省回落系统默认模型 + 关思考
|
||||
defaults = build_writing_research_config()
|
||||
assert defaults.fast_model is None and defaults.smart_model is None and defaults.strategic_model is None
|
||||
assert defaults.thinking_enabled is False
|
||||
|
||||
|
||||
def _fake_app_config(monkeypatch, *, known: str = "qwen3.6", supports_thinking: bool = True) -> None:
|
||||
import deerflow.config.app_config as app_config_module
|
||||
|
||||
def get_model_config(name: str):
|
||||
if name != known:
|
||||
return None
|
||||
return SimpleNamespace(supports_thinking=supports_thinking)
|
||||
|
||||
monkeypatch.setattr(
|
||||
app_config_module,
|
||||
"get_app_config",
|
||||
lambda: SimpleNamespace(get_model_config=get_model_config),
|
||||
)
|
||||
|
||||
|
||||
def test_resolve_chat_model_passes_through_known_model(monkeypatch) -> None:
|
||||
_fake_app_config(monkeypatch, known="qwen3.6", supports_thinking=True)
|
||||
|
||||
name, thinking, force = tool_module._resolve_chat_model({"model_name": "qwen3.6", "thinking_enabled": True})
|
||||
|
||||
assert (name, thinking, force) == ("qwen3.6", True, False)
|
||||
|
||||
|
||||
def test_resolve_chat_model_falls_back_on_unknown_or_unsupported(monkeypatch) -> None:
|
||||
_fake_app_config(monkeypatch, known="qwen3.6", supports_thinking=False)
|
||||
|
||||
# 不在 config.yaml 的模型 → 回落系统默认
|
||||
assert tool_module._resolve_chat_model({"model_name": "no-such-model"})[0] is None
|
||||
# 模型不支持思考 → 关思考
|
||||
name, thinking, _ = tool_module._resolve_chat_model({"model_name": "qwen3.6", "thinking_enabled": True})
|
||||
assert (name, thinking) == ("qwen3.6", False)
|
||||
# 未选模型 → None + 透传 force 开关
|
||||
assert tool_module._resolve_chat_model({"thinking_force_disabled": True}) == (None, True, True)
|
||||
|
||||
|
||||
def test_tool_threads_chat_model_into_research_config(monkeypatch) -> None:
|
||||
_patch_tool_dependencies(monkeypatch)
|
||||
_fake_app_config(monkeypatch, known="qwen3.6", supports_thinking=True)
|
||||
runtime = SimpleNamespace(
|
||||
context={
|
||||
"thread_id": "thread-m",
|
||||
"model_name": "qwen3.6",
|
||||
"thinking_enabled": True,
|
||||
"thinking_force_disabled": False,
|
||||
},
|
||||
config={},
|
||||
state={"messages": [_material_message(i) for i in range(1, 4)]},
|
||||
)
|
||||
|
||||
asyncio.run(deep_research_report_tool.coroutine(runtime, "call-m", _DEFAULT_TOPIC))
|
||||
|
||||
config = _FakeEngine.instances[-1].requests[0].config
|
||||
assert config.smart_model == "qwen3.6"
|
||||
assert config.strategic_model == "qwen3.6"
|
||||
assert config.thinking_enabled is True
|
||||
|
||||
|
||||
# ── prompt 合约 ──────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def _patch_prompt_sections(monkeypatch, module) -> None:
|
||||
monkeypatch.setattr(module, "_get_memory_context", lambda *a, **k: "")
|
||||
monkeypatch.setattr(module, "get_skills_prompt_section", lambda *a, **k: "")
|
||||
monkeypatch.setattr(module, "get_deferred_tools_prompt_section", lambda *a, **k: "")
|
||||
monkeypatch.setattr(module, "_build_acp_section", lambda *a, **k: "")
|
||||
monkeypatch.setattr(module, "_build_custom_mounts_section", lambda *a, **k: "")
|
||||
monkeypatch.setattr(module, "_current_time_context", lambda: "")
|
||||
|
||||
|
||||
def test_lead_prompt_first_turn_uses_research_tool_contract(monkeypatch) -> None:
|
||||
from deerflow.agents.lead_agent import prompt as prompt_module
|
||||
|
||||
_patch_prompt_sections(monkeypatch, prompt_module)
|
||||
|
||||
prompt = prompt_module.apply_prompt_template(writing_mode=True)
|
||||
|
||||
assert "deep_research_report" in prompt
|
||||
assert "Work normally until delivery time" in prompt
|
||||
assert "Do NOT ask the user whether to search for more" in prompt
|
||||
assert "first report turn" in prompt
|
||||
assert "do NOT call `read_file`" in prompt
|
||||
assert "Current Markdown Artifact" not in prompt
|
||||
|
||||
|
||||
def test_lead_prompt_follow_up_keeps_manual_edit_contract(monkeypatch) -> None:
|
||||
from deerflow.agents.lead_agent import prompt as prompt_module
|
||||
|
||||
_patch_prompt_sections(monkeypatch, prompt_module)
|
||||
|
||||
prompt = prompt_module.apply_prompt_template(
|
||||
writing_mode=True,
|
||||
writing_artifact_path="/mnt/user-data/outputs/report.md",
|
||||
)
|
||||
|
||||
# 续写轮保持原合约：编辑现有文件 + present_files，不再要求调用管线工具
|
||||
assert "Current Markdown Artifact" in prompt
|
||||
assert "present_files" in prompt
|
||||
assert "deep_research_report" not in prompt
|
||||
|
||||
|
||||
def test_agent_only_prompt_mirrors_both_contracts(monkeypatch) -> None:
|
||||
from deerflow.agents.lead_agent import prompt as prompt_module
|
||||
|
||||
monkeypatch.setattr(prompt_module, "get_agent_soul", lambda *a, **k: "SOUL")
|
||||
monkeypatch.setattr(prompt_module, "get_skills_prompt_section", lambda *a, **k: "")
|
||||
monkeypatch.setattr(prompt_module, "get_deferred_tools_prompt_section", lambda *a, **k: "")
|
||||
monkeypatch.setattr(prompt_module, "_build_acp_section", lambda *a, **k: "")
|
||||
monkeypatch.setattr(prompt_module, "_build_custom_mounts_section", lambda *a, **k: "")
|
||||
|
||||
first_turn = prompt_module.apply_agent_only_prompt_template(agent_id="a1", agent_name="A1", writing_mode=True)
|
||||
follow_up = prompt_module.apply_agent_only_prompt_template(
|
||||
agent_id="a1",
|
||||
agent_name="A1",
|
||||
writing_mode=True,
|
||||
writing_artifact_path="/mnt/user-data/outputs/report.md",
|
||||
)
|
||||
|
||||
assert "deep_research_report" in first_turn
|
||||
assert "Work normally (chat, search, clarify) until the report is due" in first_turn
|
||||
assert "Do NOT ask the user whether to search for more" in first_turn
|
||||
assert "do NOT call `read_file`" in first_turn
|
||||
assert "Current Markdown Artifact" in follow_up
|
||||
assert "deep_research_report" not in follow_up
|
||||
Reference in new issue
Block a user