分段分域组合

sug_v6_1_2_121.py

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+import asyncio
+import json
+import os
+import sys
+import argparse
+from datetime import datetime
+from typing import Literal
+
+from agents import Agent, Runner, ModelSettings
+from lib.my_trace import set_trace
+from pydantic import BaseModel, Field
+
+from lib.utils import read_file_as_string
+from lib.client import get_model
+MODEL_NAME = "google/gemini-2.5-flash"
+# 得分提升阈值：sug或组合词必须比来源query提升至少此幅度才能进入下一轮
+REQUIRED_SCORE_GAIN = 0.05
+from script.search_recommendations.xiaohongshu_search_recommendations import XiaohongshuSearchRecommendations
+from script.search.xiaohongshu_search import XiaohongshuSearch
+
+
+# ============================================================================
+# 日志工具类
+# ============================================================================
+
+class TeeLogger:
+    """同时输出到控制台和日志文件的工具类"""
+    def __init__(self, stdout, log_file):
+        self.stdout = stdout
+        self.log_file = log_file
+
+    def write(self, message):
+        self.stdout.write(message)
+        self.log_file.write(message)
+        self.log_file.flush()  # 实时写入，避免丢失日志
+
+    def flush(self):
+        self.stdout.flush()
+        self.log_file.flush()
+
+
+# ============================================================================
+# 数据模型
+# ============================================================================
+
+class Seg(BaseModel):
+    """分词（旧版）- v120使用"""
+    text: str
+    score_with_o: float = 0.0  # 与原始问题的评分
+    reason: str = ""  # 评分理由
+    from_o: str = ""  # 原始问题
+
+
+# ============================================================================
+# 新架构数据模型 (v121)
+# ============================================================================
+
+class Segment(BaseModel):
+    """语义片段（Round 0语义分段结果）"""
+    text: str  # 片段文本
+    type: str  # 语义类型: 疑问标记/核心动作/修饰短语/中心名词/逻辑连接
+    score_with_o: float = 0.0  # 与原始问题的评分
+    reason: str = ""  # 评分理由
+    from_o: str = ""  # 原始问题
+    words: list[str] = Field(default_factory=list)  # 该片段拆分出的词列表（Round 0拆词结果）
+    word_scores: dict[str, float] = Field(default_factory=dict)  # 词的评分 {word: score}
+    word_reasons: dict[str, str] = Field(default_factory=dict)  # 词的评分理由 {word: reason}
+
+
+class DomainCombination(BaseModel):
+    """域组合（Round N的N域组合结果）"""
+    text: str  # 组合后的文本
+    domains: list[int] = Field(default_factory=list)  # 参与组合的域索引列表（对应segments的索引）
+    type_label: str = ""  # 类型标签，如 [疑问标记+核心动作+中心名词]
+    source_words: list[list[str]] = Field(default_factory=list)  # 来源词列表，每个元素是一个域的词列表，如 [["猫咪"], ["梗图"]]
+    score_with_o: float = 0.0  # 与原始问题的评分
+    reason: str = ""  # 评分理由
+    from_segments: list[str] = Field(default_factory=list)  # 来源segment的文本列表
+
+
+# ============================================================================
+# 旧架构数据模型（保留但不使用）
+# ============================================================================
+
+# class Word(BaseModel):
+#     """词（旧版）- v120使用，v121不再使用"""
+#     text: str
+#     score_with_o: float = 0.0  # 与原始问题的评分
+#     from_o: str = ""  # 原始问题
+
+
+class Word(BaseModel):
+    """词"""
+    text: str
+    score_with_o: float = 0.0  # 与原始问题的评分
+    from_o: str = ""  # 原始问题
+
+
+class QFromQ(BaseModel):
+    """Q来源信息（用于Sug中记录）"""
+    text: str
+    score_with_o: float = 0.0
+
+
+class Q(BaseModel):
+    """查询"""
+    text: str
+    score_with_o: float = 0.0  # 与原始问题的评分
+    reason: str = ""  # 评分理由
+    from_source: str = ""  # v120: seg/sug/add; v121新增: segment/domain_comb/sug
+    type_label: str = ""  # v121新增：域类型标签（仅用于domain_comb来源）
+
+
+class Sug(BaseModel):
+    """建议词"""
+    text: str
+    score_with_o: float = 0.0  # 与原始问题的评分
+    reason: str = ""  # 评分理由
+    from_q: QFromQ | None = None  # 来自的q
+
+
+class Seed(BaseModel):
+    """种子（旧版）- v120使用，v121不再使用"""
+    text: str
+    added_words: list[str] = Field(default_factory=list)  # 已经增加的words
+    from_type: str = ""  # seg/sug/add
+    score_with_o: float = 0.0  # 与原始问题的评分
+
+
+class Post(BaseModel):
+    """帖子"""
+    title: str = ""
+    body_text: str = ""
+    type: str = "normal"  # video/normal
+    images: list[str] = Field(default_factory=list)  # 图片url列表，第一张为封面
+    video: str = ""  # 视频url
+    interact_info: dict = Field(default_factory=dict)  # 互动信息
+    note_id: str = ""
+    note_url: str = ""
+
+
+class Search(Sug):
+    """搜索结果（继承Sug）"""
+    post_list: list[Post] = Field(default_factory=list)  # 搜索得到的帖子列表
+
+
+class RunContext(BaseModel):
+    """运行上下文"""
+    version: str
+    input_files: dict[str, str]
+    c: str  # 原始需求
+    o: str  # 原始问题
+    log_url: str
+    log_dir: str
+
+    # v121新增：语义分段结果
+    segments: list[dict] = Field(default_factory=list)  # Round 0的语义分段结果
+
+    # 每轮的数据
+    rounds: list[dict] = Field(default_factory=list)  # 每轮的详细数据
+
+    # 最终结果
+    final_output: str | None = None
+
+    # 评估缓存：避免重复评估相同文本
+    evaluation_cache: dict[str, tuple[float, str]] = Field(default_factory=dict)
+    # key: 文本, value: (score, reason)
+
+
+# ============================================================================
+# Agent 定义
+# ============================================================================
+
+# ============================================================================
+# v121 新增 Agent
+# ============================================================================
+
+# Agent: 语义分段专家 (Prompt1)
+class SemanticSegment(BaseModel):
+    """单个语义片段"""
+    segment_text: str = Field(..., description="片段文本")
+    segment_type: str = Field(..., description="语义类型（疑问标记/核心动作/修饰短语/中心名词/逻辑连接）")
+    reasoning: str = Field(..., description="分段理由")
+
+
+class SemanticSegmentation(BaseModel):
+    """语义分段结果"""
+    segments: list[SemanticSegment] = Field(..., description="语义片段列表")
+    overall_reasoning: str = Field(..., description="整体分段思路")
+
+
+semantic_segmentation_instructions = """
+你是语义分段专家。给定一个搜索query，将其拆分成不同语义类型的片段。
+
+## 语义类型定义（5种）
+1. **疑问标记**：如何、怎么、什么、哪里等疑问词
+2. **核心动作**：关键动词，如获取、制作、拍摄、寻找等
+3. **修饰短语**：形容词、副词等修饰成分，如高质量、能体现...特色等
+4. **中心名词**：核心名词，如素材、梗图、攻略等
+5. **逻辑连接**：并且、或者、以及等连接词（较少出现）
+
+## 分段原则
+1. **语义完整性**：每个片段应该是一个完整的语义单元
+2. **类型互斥**：每个片段只能属于一种类型
+3. **保留原文**：片段文本必须保留原query中的字符，不得改写
+4. **顺序保持**：片段顺序应与原query一致
+
+## 示例
+**Query**: "如何获取能体现川西秋季特色的高质量风光摄影素材？"
+
+**分段结果**:
+1. "如何" - 疑问标记
+2. "获取" - 核心动作
+3. "能体现川西秋季特色的" - 修饰短语
+4. "高质量" - 修饰短语
+5. "风光摄影素材" - 中心名词
+
+## 输出要求
+- segments: 片段列表
+  - segment_text: 片段文本（必须来自原query）
+  - segment_type: 语义类型（从5种类型中选择）
+  - reasoning: 为什么这样分段
+- overall_reasoning: 整体分段思路
+
+## JSON输出规范
+1. **格式要求**：必须输出标准JSON格式
+2. **引号规范**：字符串中如需表达引用，使用书名号《》或「」，不要使用英文引号或中文引号""
+""".strip()
+
+semantic_segmenter = Agent[None](
+    name="语义分段专家",
+    instructions=semantic_segmentation_instructions,
+    model=get_model(MODEL_NAME),
+    output_type=SemanticSegmentation,
+)
+
+
+# ============================================================================
+# v120 保留 Agent
+# ============================================================================
+
+# Agent 1: 分词专家（v121用于Round 0拆词）
+class WordSegmentation(BaseModel):
+    """分词结果"""
+    words: list[str] = Field(..., description="分词结果列表")
+    reasoning: str = Field(..., description="分词理由")
+
+word_segmentation_instructions = """
+你是分词专家。给定一个query，将其拆分成有意义的最小单元。
+
+## 分词原则
+1. 保留有搜索意义的词汇
+2. 拆分成独立的概念
+3. 保留专业术语的完整性
+4. 去除虚词（的、吗、呢等）
+
+## 输出要求
+返回分词列表和分词理由。
+""".strip()
+
+word_segmenter = Agent[None](
+    name="分词专家",
+    instructions=word_segmentation_instructions,
+    model=get_model(MODEL_NAME),
+    output_type=WordSegmentation,
+)
+
+
+# Agent 2: 动机维度评估专家 + 品类维度评估专家（两阶段评估）
+
+# 动机评估的嵌套模型
+class CoreMotivationExtraction(BaseModel):
+    """核心动机提取"""
+    简要说明核心动机: str = Field(..., description="核心动机说明")
+
+class MotivationEvaluation(BaseModel):
+    """动机维度评估"""
+    原始问题核心动机提取: CoreMotivationExtraction = Field(..., description="原始问题核心动机提取")
+    动机维度得分: float = Field(..., description="动机维度得分 -1~1")
+    简要说明动机维度相关度理由: str = Field(..., description="动机维度相关度理由")
+
+class CategoryEvaluation(BaseModel):
+    """品类维度评估"""
+    品类维度得分: float = Field(..., description="品类维度得分 -1~1")
+    简要说明品类维度相关度理由: str = Field(..., description="品类维度相关度理由")
+
+# 动机评估 prompt - 第一轮版本（来自 sug_v6_1_2_115.py）
+motivation_evaluation_instructions_round1 = """
+#角色
+你是一个 **专业的语言专家和语义相关性评判专家**。你的任务是：判断我给你的 <平台sug词条> 与 <原始问题> 的需求动机匹配度，给出 **-1 到 1 之间** 的数值评分。
+
+---
+
+# 核心概念与方法论
+
+## 评估维度
+本评估系统围绕 **动机维度** 进行：
+
+### 1. 动机维度
+**定义：** 用户"想要做什么"，即原始问题的行为意图和目的
+- 核心是 **动词**：获取、学习、拍摄、制作、寻找等
+- 包括：核心动作 + 使用场景 + 最终目的
+
+---
+
+## 如何识别原始问题的核心动机
+
+**核心动机必须是动词**，识别方法如下：
+
+### 方法1: 显性动词直接提取
+
+当原始问题明确包含动词时，直接提取
+示例：
+"如何获取素材" → 核心动机 = "获取"
+"寻找拍摄技巧" → 核心动机 = "寻找"（或"学习"）
+"制作视频教程" → 核心动机 = "制作"
+
+### 方法2: 隐性动词语义推理
+
+当原始问题没有显性动词时，需要结合上下文推理
+示例：
+例: "川西秋天风光摄影" → 隐含动作="拍摄"
+→ 需结合上下文判断
+
+如果原始问题是纯名词短语，无任何动作线索：
+→ 核心动机 = 无法识别
+→ 在此情况下，动机维度得分应为 0。
+示例：
+"摄影" → 无法识别动机，动机维度得分 = 0
+"川西风光" → 无法识别动机，动机维度得分 = 0
+
+---
+
+# 输入信息
+你将接收到以下输入：
+- **<原始问题>**：用户的初始查询问题，代表用户的真实需求意图。
+- **<平台sug词条>**：平台推荐的词条列表，每个词条需要单独评估。
+
+
+#判定流程
+#评估架构
+
+输入: <原始问题> + <平台sug词条>
+         ↓
+【动机维度相关性判定】
+    ├→ 步骤1: 评估<sug词条>与<原始问题>的需求动机匹配度
+    └→ 输出: -1到1之间的数值 + 判定依据
+
+
+相关度评估维度详解
+维度1: 动机维度评估
+评估对象： <平台sug词条> 与 <原始问题> 的需求动机匹配度
+说明： 核心动作是用户需求的第一优先级，决定了推荐的基本有效性
+
+
+评分标准:
+
+【正向匹配】
++0.95~1.0: 核心动作完全一致
+  - 例: 原始问题"如何获取素材" vs sug词"素材获取方法"
+  - 特殊规则: 如果sug词的核心动作是原始问题动作的**具体化子集**，也判定为完全一致
+    · 例: 原始问题"扣除猫咪主体的方法" vs sug词"扣除猫咪眼睛的方法"（子集但目的一致）
+
++0.75~0.95: 核心动作语义相近或为同义表达
+  - 例: 原始问题"如何获取素材" vs sug词"如何下载素材"
+  - 同义词对: 获取≈下载≈寻找, 技巧≈方法≈教程≈攻略
+
++0.5~0.75: 核心动作相关但非直接对应（相关实现路径）
+  - 例: 原始问题"如何获取素材" vs sug词"素材管理整理"
+
++0.2~0.45: 核心动作弱相关（同领域不同动作）
+  - 例: 原始问题"如何拍摄风光" vs sug词"风光摄影欣赏"
+
+【中性/无关】
+0: 没有明确目的，动作意图无明确关联
+  - 例: 原始问题"如何获取素材" vs sug词"摄影器材推荐"
+  - 例: 原始问题无法识别动机 且 sug词也无明确动作 → 0
+  - 如果原始问题无法识别动机，则动机维度得分为0。
+
+【负向偏离】
+-0.2~-0.05: 动作意图轻度冲突或误导
+  - 例: 原始问题"如何获取素材" vs sug词"素材版权保护须知"
+
+-0.5~-0.25: 动作意图明显对立
+  - 例: 原始问题"如何获取免费素材" vs sug词"如何售卖素材"
+
+-1.0~-0.55: 动作意图完全相反或产生严重负面引导
+  - 例: 原始问题"免费素材获取" vs sug词"付费素材强制推销"
+
+---
+
+# 输出要求
+
+输出结果必须为一个 **JSON 格式**，包含以下内容：
+```json
+{
+  "原始问题核心动机提取": {
+    "简要说明核心动机": ""
+  },
+  "动机维度得分": "-1到1之间的小数",
+  "简要说明动机维度相关度理由": "评估该sug词条与原始问题动机匹配程度的理由"
+}
+
+**输出约束（非常重要）**：
+1. **字符串长度限制**：\"简要说明动机维度相关度理由\"字段必须控制在**150字以内**
+2. **JSON格式规范**：必须生成完整的JSON格式，确保字符串用双引号包裹且正确闭合
+3. **引号使用**：字符串中如需表达引用，请使用《》或「」代替单引号或双引号
+
+#注意事项：
+始终围绕动机维度：所有评估都基于"动机"维度，不偏离
+核心动机必须是动词：在评估前，必须先提取原始问题的核心动机（动词），这是整个评估的基础
+严格标准一致性：对所有用例使用相同的评估标准，避免评分飘移
+负分使用原则：仅当sug词条对原始问题动机产生误导、冲突或有害引导时给予负分
+零分使用原则：当sug词条与原始问题动机无明确关联，既不相关也不冲突时给予零分，或原始问题无法识别动机时。
+""".strip()
+
+# 动机评估 prompt - 后续轮次版本（当前 116 版本）
+motivation_evaluation_instructions = """
+#角色
+你是一个 **专业的语言专家和语义相关性评判专家**。你的任务是：判断我给你的 <平台sug词条> 与 <原始问题> 的需求动机匹配度，给出 **-1 到 1 之间** 的数值评分。
+
+---
+# 动机评估核心原则（必读）
+
+### 动机 = 动作 + 对象 + 场景
+评估时必须同时考虑三要素，不能只看动词：
+- **动作**：制定、规划、获取、拍摄等
+- **对象**：旅行行程 vs 每日计划、风光照片 vs 证件照
+- **场景**：旅游 vs 日常、摄影 vs 办公
+
+### 关键判断：动词相同 ≠ 动机匹配
+
+错误：只看动词相同就给高分
+- "制定旅行行程" vs "制定每日计划" → 给0.95 错误
+- "拍摄风光" vs "拍摄证件照" → 给0.95 错误
+
+正确：检查对象和场景是否匹配
+- 对象不同领域 → 降至0.3左右
+- 场景不同 → 降至0.3左右
+
+
+# 核心概念与方法论
+
+## 评估维度
+本评估系统围绕 **动机维度** 进行：
+
+# 维度独立性警告
+【严格约束】本评估**只评估动机维度**：
+**禁止使用"主题相关"作为评分依据**：评分理由中不得出现"主题"、"内容"、"话题"等词
+
+### 1. 动机维度
+**定义：** 用户"想要做什么"，即原始问题的行为意图和目的
+- 核心是 **动词**：获取、学习、拍摄、制作、寻找等
+- 包括：核心动作 + 使用场景 + 最终目的
+
+---
+
+如果原始问题是纯名词短语，无任何动作线索：
+→ 核心动机 = 无法识别
+→ 在此情况下，动机维度得分应为 0。
+示例：
+"摄影" → 无法识别动机，动机维度得分 = 0
+"川西风光" → 无法识别动机，动机维度得分 = 0
+
+---
+
+# 输入信息
+你将接收到以下输入：
+- **<原始问题>**：用户的初始查询问题，代表用户的真实需求意图。
+- **<平台sug词条>**：平台推荐的词条列表，每个词条需要单独评估。
+
+
+#判定流程
+#评估架构
+
+输入: <原始问题> + <平台sug词条>
+         ↓
+【动机维度相关性判定】
+    ├→ 步骤1: 评估<sug词条>与<原始问题>的需求动机匹配度
+    └→ 输出: -1到1之间的数值 + 判定依据
+
+
+相关度评估维度详解
+维度1: 动机维度评估
+评估对象： <平台sug词条> 与 <原始问题> 的需求动机匹配度
+说明： 核心动作是用户需求的第一优先级，决定了推荐的基本有效性
+
+
+评分标准:
+
+【正向匹配】
++0.95~1.0: 动作+对象+场景完全一致
+  - 要求：动词、对象、场景都必须匹配，不能只看动词
+  - "制定旅行行程" vs "制定每日计划"
+     虽然动词相同，但对象和场景完全不同，不属于高分
+  - 特殊规则: 如果sug词的核心动作是原始问题动作在动作+对象+场景一致下的**具体化子集**，也判定为完全一致
+
+
++0.75~0.95: 核心动作语义相近或为同义表达
+  - 例: 原始问题"如何获取素材" vs sug词"如何下载素材"
+  - 同义词对: 获取≈下载≈寻找, 技巧≈方法≈教程≈攻略
+
++0.5~0.75: 核心动作相关但非直接对应（相关实现路径）
+  - 例: 原始问题"如何获取素材" vs sug词"素材管理整理"
+
++0.25~0.4: 动词相同但对象或场景明显不同（弱相关）
+  - 判断要点：动词一致，但对象不同领域或场景不同
+  - 关键：不要因为动词相同就给0.95，必须检查对象！
+
+
+【中性/无关】
+0: 没有明确目的，动作意图无明确关联
+  - 例: 原始问题"如何获取素材" vs sug词"摄影器材推荐"
+  - 例: 原始问题无法识别动机 且 sug词也无明确动作 → 0
+  - 如果原始问题无法识别动机，则动机维度得分为0
+
+ 特别注意 - 禁止的错误理由：
+  - 禁止: "虽然没有动作，但主题相关，所以给0.2"
+  - 禁止:"内容有参考价值，所以给0.15"
+  - 禁止: "都提到了XX（名词），所以不是完全无关"
+  - 正确理由："sug词条无动作意图，与原始问题的'XX'动机完全无关"
+
+【负向偏离】
+-0.2~-0.05: 动作意图轻度冲突或误导
+  - 例: 原始问题"如何获取素材" vs sug词"素材版权保护须知"
+
+-0.5~-0.25: 动作意图明显对立
+  - 例: 原始问题"如何获取免费素材" vs sug词"如何售卖素材"
+
+-1.0~-0.55: 动作意图完全相反或产生严重负面引导
+  - 例: 原始问题"免费素材获取" vs sug词"付费素材强制推销"
+
+---
+
+# 输出要求
+
+输出结果必须为一个 **JSON 格式**，包含以下内容：
+```json
+{
+  "原始问题核心动机提取": {
+    "简要说明核心动机": ""
+  },
+  "动机维度得分": "-1到1之间的小数",
+  "简要说明动机维度相关度理由": "评估该sug词条与原始问题动机匹配程度的理由"
+}
+
+**输出约束（非常重要）**：
+1. **字符串长度限制**：\"简要说明动机维度相关度理由\"字段必须控制在**150字以内**
+2. **JSON格式规范**：必须生成完整的JSON格式，确保字符串用双引号包裹且正确闭合
+3. **引号使用**：字符串中如需表达引用，请使用《》或「」代替单引号或双引号
+
+#注意事项：
+始终围绕动机维度：所有评估都基于"动机"维度，不偏离
+核心动机必须是动词：在评估前，必须先提取原始问题的核心动机（动词），这是整个评估的基础
+严格标准一致性：对所有用例使用相同的评估标准，避免评分飘移
+负分使用原则：仅当sug词条对原始问题动机产生误导、冲突或有害引导时给予负分
+零分使用原则：当sug词条与原始问题动机无明确关联，既不相关也不冲突时给予零分，或原始问题无法识别动机时。
+""".strip()
+
+# 品类评估 prompt
+category_evaluation_instructions = """
+#角色
+你是一个 **专业的语言专家和语义相关性评判专家**。你的任务是：判断我给你的 <平台sug词条> 与 <原始问题> 的内容主体和限定词匹配度，给出 **-1 到 1 之间** 的数值评分。
+
+---
+# 核心概念与方法论
+
+## 评估维度
+本评估系统围绕 **品类维度** 进行：
+
+#  维度独立性警告
+【严格约束】本评估**只评估品类维度**,，必须遵守以下规则：
+1. **只看名词和限定词**：评估时只考虑主体、限定词的匹配度
+2. **完全忽略动词**：动作意图、目的等动机信息对本维度评分无影响
+
+### 品类维度
+**定义：** 用户"关于什么内容"，即原始问题的主题对象和限定词
+- 核心是 **名词+限定词**：川西秋季风光摄影素材
+- 包括：核心主体 + 地域限定 + 时间限定 + 质量限定等
+
+## ⚠️ 品类评估核心原则（必读）
+
+### 原则1：只看词条表面，禁止联想推演
+- 只能基于sug词实际包含的词汇评分
+- 禁止推测"可能包含"、"可以理解为"
+
+**错误示例：**
+原始问题："川西旅行行程" vs sug词："每日计划"
+- 错误 "每日计划可以包含旅行规划，所以有关联" → 这是不允许的联想
+- 正确： "sug词只有'每日计划'，无'旅行'字眼，品类不匹配" → 正确判断
+
+### 原则2：通用概念 ≠ 特定概念
+- **通用**：计划、方法、技巧、素材（无领域限定）
+- **特定**：旅行行程、摄影技巧、烘焙方法（有明确领域）
+
+IF sug词是通用 且 原始问题是特定：
+   → 品类不匹配 → 评分0.05~0.1
+关键：通用概念不等于特定概念，不能因为"抽象上都是规划"就给分
+
+---
+
+# 输入信息
+你将接收到以下输入：
+- **<原始问题>**：用户的初始查询问题，代表用户的真实需求意图。
+- **<平台sug词条>**：平台推荐的词条列表，每个词条需要单独评估。
+
+
+#判定流程
+#评估架构
+
+输入: <原始问题> + <平台sug词条>
+         ↓
+【品类维度相关性判定】
+    ├→ 步骤1: 评估<sug词条>与<原始问题>的内容主体和限定词匹配度
+    └→ 输出: -1到1之间的数值 + 判定依据
+
+
+相关度评估维度详解
+维度2: 品类维度评估
+评估对象： <平台sug词条> 与 <原始问题> 的内容主体和限定词匹配度
+
+评分标准:
+
+【正向匹配】
++0.95~1.0: 核心主体+所有关键限定词完全匹配
+  - 例: 原始问题"川西秋季风光摄影素材" vs sug词"川西秋季风光摄影作品"
+
++0.75~0.95: 核心主体匹配，存在限定词匹配
+  - 例: 原始问题"川西秋季风光摄影素材" vs sug词"川西风光摄影素材"（缺失"秋季"）
+
++0.5~0.75: 核心主体匹配，无限定词匹配或合理泛化
+  - 例: 原始问题"川西秋季风光摄影素材" vs sug词"四川风光摄影"
+
++0.3~0.5: 核心主体匹配，但限定词缺失或存在语义错位
+  - 特别注意"语义身份"差异，主体词出现但上下文语义不同
+  - 例:
+    · "猫咪的XX行为"（猫咪是行为者）
+    · vs "用猫咪表达XX的梗图"（猫咪是媒介）
+    · 虽都含"猫咪+XX"，但语义角色不同
+
++0.2~0.3: 主体词不匹配，限定词缺失或错位
+  - 例: 原始问题"川西秋季风光摄影素材" vs sug词"风光摄影入门"
+
++0.05~0.2: 主体词过度泛化或仅抽象相似
+  - 例: sug词是通用概念，原始问题是特定概念
+    sug词"每日计划"（通用）vs 原始问题 "川西旅行行程"（特定）
+      → 评分：0.08
+
+【中性/无关】
+0: 类别明显不同，没有明确目的，无明确关联
+  - 例: 原始问题"川西秋季风光摄影素材" vs sug词"人像摄影素材"
+  - 例: 原始问题无法识别动机 且 sug词也无明确动作 → 0
+
+【负向偏离】
+-0.2~-0.05: 主体词或限定词存在误导性
+  - 例: 原始问题"免费摄影素材" vs sug词"付费摄影素材库"
+
+-0.5~-0.25: 主体词明显错位或品类冲突
+  - 例: 原始问题"风光摄影素材" vs sug词"人像修图教程"
+
+-1.0~-0.55: 完全错误的品类或有害引导
+  - 例: 原始问题"正版素材获取" vs sug词"盗版素材下载"
+
+---
+
+# 输出要求
+
+输出结果必须为一个 **JSON 格式**，包含以下内容：
+```json
+{
+  "品类维度得分": "-1到1之间的小数",
+  "简要说明品类维度相关度理由": "评估该sug词条与原始问题品类匹配程度的理由"
+}
+---
+
+**输出约束（非常重要）**：
+1. **字符串长度限制**：\"简要说明品类维度相关度理由\"字段必须控制在**150字以内**
+2. **JSON格式规范**：必须生成完整的JSON格式，确保字符串用双引号包裹且正确闭合
+3. **引号使用**：字符串中如需表达引用，请使用《》或「」代替单引号或双引号
+
+---
+
+#注意事项：
+始终围绕品类维度：所有评估都基于"品类"维度，不偏离
+严格标准一致性：对所有用例使用相同的评估标准，避免评分飘移
+负分使用原则：仅当sug词条对原始问题品类产生误导、冲突或有害引导时给予负分
+零分使用原则：当sug词条与原始问题品类无明确关联，既不相关也不冲突时给予零分
+""".strip()
+
+# 创建评估 Agent
+motivation_evaluator = Agent[None](
+    name="动机维度评估专家(后续轮次)",
+    instructions=motivation_evaluation_instructions,
+    model=get_model(MODEL_NAME),
+    output_type=MotivationEvaluation)
+
+category_evaluator = Agent[None](
+    name="品类维度评估专家",
+    instructions=category_evaluation_instructions,
+    model=get_model(MODEL_NAME),
+    output_type=CategoryEvaluation
+)
+
+
+# ============================================================================
+# v120 保留但不使用的 Agent（v121不再使用）
+# ============================================================================
+
+# # Agent 3: 加词选择专家（旧版 - v120使用，v121不再使用）
+# class WordCombination(BaseModel):
+#     """单个词组合"""
+#     selected_word: str = Field(..., description="选择的词")
+#     combined_query: str = Field(..., description="组合后的新query")
+#     reasoning: str = Field(..., description="选择理由")
+
+# class WordSelectionTop5(BaseModel):
+#     """加词选择结果（Top 5）"""
+#     combinations: list[WordCombination] = Field(
+#         ...,
+#         description="选择的Top 5组合（不足5个则返回所有）",
+#         min_items=1,
+#         max_items=5
+#     )
+#     overall_reasoning: str = Field(..., description="整体选择思路")
+
+# word_selection_instructions 已删除 (v121不再使用)
+
+# word_selector = Agent[None](
+#     name="加词组合专家",
+#     instructions=word_selection_instructions,
+#     model=get_model(MODEL_NAME),
+#     output_type=WordSelectionTop5,
+#     model_settings=ModelSettings(temperature=0.2),
+# )
+
+
+# ============================================================================
+# 辅助函数
+# ============================================================================
+
+# ============================================================================
+# v121 新增辅助函数
+# ============================================================================
+
+def get_ordered_subsets(words: list[str], min_len: int = 1) -> list[list[str]]:
+    """
+    生成words的所有有序子集（可跳过但不可重排）
+
+    使用 itertools.combinations 生成索引组合，保持原始顺序
+
+    Args:
+        words: 词列表
+        min_len: 子集最小长度
+
+    Returns:
+        所有可能的有序子集列表
+
+    Example:
+        words = ["川西", "秋季", "风光"]
+        结果:
+        - 长度1: ["川西"], ["秋季"], ["风光"]
+        - 长度2: ["川西", "秋季"], ["川西", "风光"], ["秋季", "风光"]
+        - 长度3: ["川西", "秋季", "风光"]
+        共 C(3,1) + C(3,2) + C(3,3) = 3 + 3 + 1 = 7种
+    """
+    from itertools import combinations
+
+    subsets = []
+    n = len(words)
+
+    # 遍历所有可能的长度（从min_len到n）
+    for r in range(min_len, n + 1):
+        # 生成长度为r的所有索引组合
+        for indices in combinations(range(n), r):
+            # 按照原始顺序提取词
+            subset = [words[i] for i in indices]
+            subsets.append(subset)
+
+    return subsets
+
+
+def generate_domain_combinations(segments: list[Segment], n_domains: int) -> list[DomainCombination]:
+    """
+    生成N域组合
+
+    步骤:
+    1. 从len(segments)个域中选择n_domains个域（组合，保持顺序）
+    2. 对每个选中的域，生成其words的所有有序子集
+    3. 计算笛卡尔积，生成所有可能的组合
+
+    Args:
+        segments: 语义片段列表
+        n_domains: 参与组合的域数量
+
+    Returns:
+        所有可能的N域组合列表
+
+    Example:
+        有4个域: [疑问标记, 核心动作, 修饰短语, 中心名词]
+        n_domains=2时，选择域的方式: C(4,2) = 6种
+
+        假设选中[核心动作, 中心名词]:
+        - 核心动作的words: ["获取"], 子集: ["获取"]
+        - 中心名词的words: ["风光", "摄影", "素材"], 子集: 7种
+        则该域选择下的组合数: 1 * 7 = 7种
+    """
+    from itertools import combinations, product
+
+    all_combinations = []
+    n = len(segments)
+
+    # 检查参数有效性
+    if n_domains > n or n_domains < 1:
+        return []
+
+    # 1. 选择n_domains个域（保持原始顺序）
+    for domain_indices in combinations(range(n), n_domains):
+        selected_segments = [segments[i] for i in domain_indices]
+
+        # 2. 为每个选中的域生成其words的所有有序子集
+        domain_subsets = []
+        for seg in selected_segments:
+            if len(seg.words) == 0:
+                # 如果某个域没有词，跳过该域组合
+                domain_subsets = []
+                break
+            subsets = get_ordered_subsets(seg.words, min_len=1)
+            domain_subsets.append(subsets)
+
+        # 如果某个域没有词，跳过
+        if len(domain_subsets) != n_domains:
+            continue
+
+        # 3. 计算笛卡尔积
+        for word_combination in product(*domain_subsets):
+            # word_combination 是一个tuple，每个元素是一个词列表
+            # 例如: (["获取"], ["风光", "摄影"])
+
+            # 将所有词连接成一个字符串
+            combined_text = "".join(["".join(words) for words in word_combination])
+
+            # 生成类型标签
+            type_labels = [selected_segments[i].type for i in range(n_domains)]
+            type_label = "[" + "+".join(type_labels) + "]"
+
+            # 创建DomainCombination对象
+            comb = DomainCombination(
+                text=combined_text,
+                domains=list(domain_indices),
+                type_label=type_label,
+                source_words=[list(words) for words in word_combination],  # 保存来源词
+                from_segments=[seg.text for seg in selected_segments]
+            )
+            all_combinations.append(comb)
+
+    return all_combinations
+
+
+def extract_words_from_segments(segments: list[Segment]) -> list[Q]:
+    """
+    从 segments 中提取所有 words，转换为 Q 对象列表
+
+    用于 Round 1 的输入：将 Round 0 的 words 转换为可用于请求SUG的 query 列表
+
+    Args:
+        segments: Round 0 的语义片段列表
+
+    Returns:
+        list[Q]: word 列表，每个 word 作为一个 Q 对象
+    """
+    q_list = []
+
+    for seg_idx, segment in enumerate(segments):
+        for word in segment.words:
+            # 从 segment.word_scores 获取该 word 的评分
+            word_score = segment.word_scores.get(word, 0.0)
+            word_reason = segment.word_reasons.get(word, "")
+
+            # 创建 Q 对象
+            q = Q(
+                text=word,
+                score_with_o=word_score,
+                reason=word_reason,
+                from_source="word",  # 标记来源为 word
+                type_label=f"[{segment.type}]"  # 保留域信息
+            )
+            q_list.append(q)
+
+    return q_list
+
+
+# ============================================================================
+# v120 保留辅助函数
+# ============================================================================
+
+def calculate_final_score(motivation_score: float, category_score: float) -> float:
+    """
+    应用依存性规则计算最终得分
+
+    步骤1: 基础加权计算
+    base_score = motivation_score * 0.7 + category_score * 0.3
+
+    步骤2: 极值保护规则
+
+    Args:
+        motivation_score: 动机维度得分 -1~1
+        category_score: 品类维度得分 -1~1
+
+    Returns:
+        最终得分 -1~1
+    """
+    # 基础加权得分
+    base_score = motivation_score * 0.7 + category_score * 0.3
+
+    # 规则C: 动机负向决定机制（最高优先级）
+    if motivation_score < 0:
+        return 0.0
+
+    # 规则A: 动机高分保护机制
+    if motivation_score >= 0.8:
+        # 当目的高度一致时，品类的泛化不应导致"弱相关"
+        return max(base_score, 0.7)
+
+    # 规则B: 动机低分限制机制
+    if motivation_score <= 0.2:
+        # 目的不符时，品类匹配的价值有限
+        return min(base_score, 0.5)
+
+    # 无规则调整，返回基础得分
+    return base_score
+
+
+def clean_json_string(text: str) -> str:
+    """清理JSON中的非法控制字符（保留 \t \n \r）"""
+    import re
+    # 移除除了 \t(09) \n(0A) \r(0D) 之外的所有控制字符
+    return re.sub(r'[\x00-\x08\x0B\x0C\x0E-\x1F]', '', text)
+
+
+def process_note_data(note: dict) -> Post:
+    """处理搜索接口返回的帖子数据"""
+    note_card = note.get("note_card", {})
+    image_list = note_card.get("image_list", [])
+    interact_info = note_card.get("interact_info", {})
+    user_info = note_card.get("user", {})
+
+    # ========== 调试日志 START ==========
+    note_id = note.get("id", "")
+    raw_title = note_card.get("display_title")  # 不提供默认值
+    raw_body = note_card.get("desc")
+    raw_type = note_card.get("type")
+
+    # 打印原始值类型和内容
+    print(f"\n[DEBUG] 处理帖子 {note_id}:")
+    print(f"  raw_title 类型: {type(raw_title).__name__}, 值: {repr(raw_title)}")
+    print(f"  raw_body 类型: {type(raw_body).__name__}, 值: {repr(raw_body)[:100] if raw_body else repr(raw_body)}")
+    print(f"  raw_type 类型: {type(raw_type).__name__}, 值: {repr(raw_type)}")
+
+    # 检查是否为 None
+    if raw_title is None:
+        print(f"  ⚠️  WARNING: display_title 是 None!")
+    if raw_body is None:
+        print(f"  ⚠️  WARNING: desc 是 None!")
+    if raw_type is None:
+        print(f"  ⚠️  WARNING: type 是 None!")
+    # ========== 调试日志 END ==========
+
+    # 提取图片URL - 使用新的字段名 image_url
+    images = []
+    for img in image_list:
+        if isinstance(img, dict):
+            # 尝试新字段名 image_url，如果不存在则尝试旧字段名 url_default
+            img_url = img.get("image_url") or img.get("url_default")
+            if img_url:
+                images.append(img_url)
+
+    # 判断类型
+    note_type = note_card.get("type", "normal")
+    video_url = ""
+    if note_type == "video":
+        video_info = note_card.get("video", {})
+        if isinstance(video_info, dict):
+            # 尝试获取视频URL
+            video_url = video_info.get("media", {}).get("stream", {}).get("h264", [{}])[0].get("master_url", "")
+
+    return Post(
+        note_id=note.get("id") or "",
+        title=note_card.get("display_title") or "",
+        body_text=note_card.get("desc") or "",
+        type=note_type,
+        images=images,
+        video=video_url,
+        interact_info={
+            "liked_count": interact_info.get("liked_count", 0),
+            "collected_count": interact_info.get("collected_count", 0),
+            "comment_count": interact_info.get("comment_count", 0),
+            "shared_count": interact_info.get("shared_count", 0)
+        },
+        note_url=f"https://www.xiaohongshu.com/explore/{note.get('id', '')}"
+    )
+
+
+async def evaluate_with_o(text: str, o: str, cache: dict[str, tuple[float, str]] | None = None) -> tuple[float, str]:
+    """评估文本与原始问题o的相关度
+
+    采用两阶段评估 + 代码计算规则：
+    1. 动机维度评估（权重70%）
+    2. 品类维度评估（权重30%）
+    3. 应用规则A/B/C调整得分
+
+    Args:
+        text: 待评估的文本
+        o: 原始问题
+        cache: 评估缓存（可选），用于避免重复评估
+
+    Returns:
+        tuple[float, str]: (最终相关度分数, 综合评估理由)
+    """
+    # 检查缓存
+    if cache is not None and text in cache:
+        cached_score, cached_reason = cache[text]
+        print(f"  ⚡ 缓存命中: {text} -> {cached_score:.2f}")
+        return cached_score, cached_reason
+
+    # 准备输入
+    eval_input = f"""
+<原始问题>
+{o}
+</原始问题>
+
+<平台sug词条>
+{text}
+</平台sug词条>
+
+请评估平台sug词条与原始问题的匹配度。
+"""
+
+    # 添加重试机制
+    max_retries = 2
+    last_error = None
+
+    for attempt in range(max_retries):
+        try:
+            # 并发调用两个评估器（统一使用标准评估策略）
+            motivation_task = Runner.run(motivation_evaluator, eval_input)
+            category_task = Runner.run(category_evaluator, eval_input)
+
+            motivation_result, category_result = await asyncio.gather(
+                motivation_task,
+                category_task
+            )
+
+            # 获取评估结果
+            motivation_eval: MotivationEvaluation = motivation_result.final_output
+            category_eval: CategoryEvaluation = category_result.final_output
+
+            # 提取得分
+            motivation_score = motivation_eval.动机维度得分
+            category_score = category_eval.品类维度得分
+
+            # 计算基础得分
+            base_score = motivation_score * 0.7 + category_score * 0.3
+
+            # 应用规则计算最终得分
+            final_score = calculate_final_score(motivation_score, category_score)
+
+            # 组合评估理由
+            core_motivation = motivation_eval.原始问题核心动机提取.简要说明核心动机
+            motivation_reason = motivation_eval.简要说明动机维度相关度理由
+            category_reason = category_eval.简要说明品类维度相关度理由
+
+            combined_reason = (
+                f"【核心动机】{core_motivation}\n"
+                f"【动机维度 {motivation_score:.2f}】{motivation_reason}\n"
+                f"【品类维度 {category_score:.2f}】{category_reason}\n"
+                f"【基础得分 {base_score:.2f}】= 动机({motivation_score:.2f})*0.7 + 品类({category_score:.2f})*0.3\n"
+                f"【最终得分 {final_score:.2f}】"
+            )
+
+            # 如果应用了规则，添加规则说明
+            if final_score != base_score:
+                if motivation_score < 0:
+                    combined_reason += "（应用规则C：动机负向决定机制）"
+                elif motivation_score >= 0.8:
+                    combined_reason += "（应用规则A：动机高分保护机制）"
+                elif motivation_score <= 0.2:
+                    combined_reason += "（应用规则B：动机低分限制机制）"
+
+            # 存入缓存
+            if cache is not None:
+                cache[text] = (final_score, combined_reason)
+
+            return final_score, combined_reason
+
+        except Exception as e:
+            last_error = e
+            error_msg = str(e)
+
+            if attempt < max_retries - 1:
+                print(f"  ⚠️  评估失败 (尝试 {attempt+1}/{max_retries}): {error_msg[:150]}")
+                print(f"  正在重试...")
+                await asyncio.sleep(1)  # 等待1秒后重试
+            else:
+                print(f"  ❌ 评估失败 (已达最大重试次数): {error_msg[:150]}")
+
+    # 所有重试失败后，返回默认值
+    fallback_reason = f"评估失败(重试{max_retries}次): {str(last_error)[:200]}"
+    print(f"  使用默认值: score=0.0, reason={fallback_reason[:100]}...")
+    return 0.0, fallback_reason
+
+
+# ============================================================================
+# 核心流程函数
+# ============================================================================
+
+async def initialize(o: str, context: RunContext) -> tuple[list[Seg], list[Word], list[Q], list[Seed]]:
+    """
+    初始化阶段
+
+    Returns:
+        (seg_list, word_list_1, q_list_1, seed_list)
+    """
+    print(f"\n{'='*60}")
+    print(f"初始化阶段")
+    print(f"{'='*60}")
+
+    # 1. 分词：原始问题(o) ->分词-> seg_list
+    print(f"\n[步骤1] 分词...")
+    result = await Runner.run(word_segmenter, o)
+    segmentation: WordSegmentation = result.final_output
+
+    seg_list = []
+    for word in segmentation.words:
+        seg_list.append(Seg(text=word, from_o=o))
+
+    print(f"分词结果: {[s.text for s in seg_list]}")
+    print(f"分词理由: {segmentation.reasoning}")
+
+    # 2. 分词评估：seg_list -> 每个seg与o进行评分（使用信号量限制并发数）
+    print(f"\n[步骤2] 评估每个分词与原始问题的相关度...")
+
+    MAX_CONCURRENT_SEG_EVALUATIONS = 5
+    seg_semaphore = asyncio.Semaphore(MAX_CONCURRENT_SEG_EVALUATIONS)
+
+    async def evaluate_seg(seg: Seg) -> Seg:
+        async with seg_semaphore:
+            # 初始化阶段的分词评估使用第一轮 prompt (round_num=1)
+            seg.score_with_o, seg.reason = await evaluate_with_o(seg.text, o, context.evaluation_cache, round_num=1)
+            return seg
+
+    if seg_list:
+        print(f"  开始评估 {len(seg_list)} 个分词（并发限制: {MAX_CONCURRENT_SEG_EVALUATIONS}）...")
+        eval_tasks = [evaluate_seg(seg) for seg in seg_list]
+        await asyncio.gather(*eval_tasks)
+
+    for seg in seg_list:
+        print(f"  {seg.text}: {seg.score_with_o:.2f}")
+
+    # 3. 构建word_list_1: seg_list -> word_list_1（固定词库）
+    print(f"\n[步骤3] 构建word_list_1（固定词库）...")
+    word_list_1 = []
+    for seg in seg_list:
+        word_list_1.append(Word(
+            text=seg.text,
+            score_with_o=seg.score_with_o,
+            from_o=o
+        ))
+    print(f"word_list_1（固定）: {[w.text for w in word_list_1]}")
+
+    # 4. 构建q_list_1：seg_list 作为 q_list_1
+    print(f"\n[步骤4] 构建q_list_1...")
+    q_list_1 = []
+    for seg in seg_list:
+        q_list_1.append(Q(
+            text=seg.text,
+            score_with_o=seg.score_with_o,
+            reason=seg.reason,
+            from_source="seg"
+        ))
+    print(f"q_list_1: {[q.text for q in q_list_1]}")
+
+    # 5. 构建seed_list: seg_list -> seed_list
+    print(f"\n[步骤5] 构建seed_list...")
+    seed_list = []
+    for seg in seg_list:
+        seed_list.append(Seed(
+            text=seg.text,
+            added_words=[],
+            from_type="seg",
+            score_with_o=seg.score_with_o
+        ))
+    print(f"seed_list: {[s.text for s in seed_list]}")
+
+    return seg_list, word_list_1, q_list_1, seed_list
+
+
+async def run_round(
+    round_num: int,
+    q_list: list[Q],
+    word_list_1: list[Word],
+    seed_list: list[Seed],
+    o: str,
+    context: RunContext,
+    xiaohongshu_api: XiaohongshuSearchRecommendations,
+    xiaohongshu_search: XiaohongshuSearch,
+    sug_threshold: float = 0.7
+) -> tuple[list[Q], list[Seed], list[Search]]:
+    """
+    运行一轮
+
+    Args:
+        round_num: 轮次编号
+        q_list: 当前轮的q列表
+        word_list_1: 固定的词库（第0轮分词结果）
+        seed_list: 当前的seed列表
+        o: 原始问题
+        context: 运行上下文
+        xiaohongshu_api: 建议词API
+        xiaohongshu_search: 搜索API
+        sug_threshold: suggestion的阈值
+
+    Returns:
+        (q_list_next, seed_list_next, search_list)
+    """
+    print(f"\n{'='*60}")
+    print(f"第{round_num}轮")
+    print(f"{'='*60}")
+
+    round_data = {
+        "round_num": round_num,
+        "input_q_list": [{"text": q.text, "score": q.score_with_o, "type": "query"} for q in q_list],
+        "input_word_list_1_size": len(word_list_1),
+        "input_seed_list_size": len(seed_list)
+    }
+
+    # 1. 请求sug：q_list -> 每个q请求sug接口 -> sug_list_list
+    print(f"\n[步骤1] 为每个q请求建议词...")
+    sug_list_list = []  # list of list
+    for q in q_list:
+        print(f"\n  处理q: {q.text}")
+        suggestions = xiaohongshu_api.get_recommendations(keyword=q.text)
+
+        q_sug_list = []
+        if suggestions:
+            print(f"    获取到 {len(suggestions)} 个建议词")
+            for sug_text in suggestions:
+                sug = Sug(
+                    text=sug_text,
+                    from_q=QFromQ(text=q.text, score_with_o=q.score_with_o)
+                )
+                q_sug_list.append(sug)
+        else:
+            print(f"    未获取到建议词")
+
+        sug_list_list.append(q_sug_list)
+
+    # 2. sug评估：sug_list_list -> 每个sug与o进行评分（并发）
+    print(f"\n[步骤2] 评估每个建议词与原始问题的相关度...")
+
+    # 2.1 收集所有需要评估的sug，并记录它们所属的q
+    all_sugs = []
+    sug_to_q_map = {}  # 记录每个sug属于哪个q
+    for i, q_sug_list in enumerate(sug_list_list):
+        if q_sug_list:
+            q_text = q_list[i].text
+            for sug in q_sug_list:
+                all_sugs.append(sug)
+                sug_to_q_map[id(sug)] = q_text
+
+    # 2.2 并发评估所有sug（使用信号量限制并发数）
+    # 每个 evaluate_sug 内部会并发调用 2 个 LLM，所以这里限制为 5，实际并发 LLM 请求为 10
+    MAX_CONCURRENT_EVALUATIONS = 5
+    semaphore = asyncio.Semaphore(MAX_CONCURRENT_EVALUATIONS)
+
+    async def evaluate_sug(sug: Sug) -> Sug:
+        async with semaphore:  # 限制并发数
+            # 根据轮次选择 prompt: 第一轮使用 round1 prompt，后续使用标准 prompt
+            sug.score_with_o, sug.reason = await evaluate_with_o(sug.text, o, context.evaluation_cache, round_num=round_num)
+            return sug
+
+    if all_sugs:
+        print(f"  开始评估 {len(all_sugs)} 个建议词（并发限制: {MAX_CONCURRENT_EVALUATIONS}）...")
+        eval_tasks = [evaluate_sug(sug) for sug in all_sugs]
+        await asyncio.gather(*eval_tasks)
+
+    # 2.3 打印结果并组织到sug_details
+    sug_details = {}  # 保存每个Q对应的sug列表
+    for i, q_sug_list in enumerate(sug_list_list):
+        if q_sug_list:
+            q_text = q_list[i].text
+            print(f"\n  来自q '{q_text}' 的建议词:")
+            sug_details[q_text] = []
+            for sug in q_sug_list:
+                print(f"    {sug.text}: {sug.score_with_o:.2f}")
+                # 保存到sug_details
+                sug_details[q_text].append({
+                    "text": sug.text,
+                    "score": sug.score_with_o,
+                    "reason": sug.reason,
+                    "type": "sug"
+                })
+
+    # 2.4 剪枝判断（已禁用 - 保留所有分支）
+    pruned_query_texts = set()
+    if False:  # 原: if round_num >= 2:  # 剪枝功能已禁用，保留代码以便后续调整
+        print(f"\n[剪枝判断] 第{round_num}轮开始应用剪枝策略...")
+        for i, q in enumerate(q_list):
+            q_sug_list = sug_list_list[i]
+
+            if len(q_sug_list) == 0:
+                continue  # 没有sug则不剪枝
+
+            # 剪枝条件1: 所有sug分数都低于query分数
+            all_lower_than_query = all(sug.score_with_o < q.score_with_o for sug in q_sug_list)
+            # 剪枝条件2: 所有sug分数都低于0.5
+            all_below_threshold = all(sug.score_with_o < 0.5 for sug in q_sug_list)
+
+            if all_lower_than_query and all_below_threshold:
+                pruned_query_texts.add(q.text)
+                max_sug_score = max(sug.score_with_o for sug in q_sug_list)
+                print(f"  🔪 剪枝: {q.text} (query分数:{q.score_with_o:.2f}, sug最高分:{max_sug_score:.2f}, 全部<0.5)")
+
+        if pruned_query_texts:
+            print(f"  本轮共剪枝 {len(pruned_query_texts)} 个query")
+        else:
+            print(f"  本轮无query被剪枝")
+    else:
+        print(f"\n[剪枝判断] 剪枝功能已禁用，保留所有分支")
+
+    # 3. search_list构建
+    print(f"\n[步骤3] 构建search_list（阈值>{sug_threshold}）...")
+    search_list = []
+    high_score_sugs = [sug for sug in all_sugs if sug.score_with_o > sug_threshold]
+
+    if high_score_sugs:
+        print(f"  找到 {len(high_score_sugs)} 个高分建议词")
+
+        # 并发搜索
+        async def search_for_sug(sug: Sug) -> Search:
+            print(f"    搜索: {sug.text}")
+            try:
+                search_result = xiaohongshu_search.search(keyword=sug.text)
+                result_str = search_result.get("result", "{}")
+                if isinstance(result_str, str):
+                    result_data = json.loads(result_str)
+                else:
+                    result_data = result_str
+
+                notes = result_data.get("data", {}).get("data", [])
+                post_list = []
+                for note in notes[:10]:  # 只取前10个
+                    post = process_note_data(note)
+                    post_list.append(post)
+
+                print(f"      → 找到 {len(post_list)} 个帖子")
+
+                return Search(
+                    text=sug.text,
+                    score_with_o=sug.score_with_o,
+                    from_q=sug.from_q,
+                    post_list=post_list
+                )
+            except Exception as e:
+                print(f"      ✗ 搜索失败: {e}")
+                return Search(
+                    text=sug.text,
+                    score_with_o=sug.score_with_o,
+                    from_q=sug.from_q,
+                    post_list=[]
+                )
+
+        search_tasks = [search_for_sug(sug) for sug in high_score_sugs]
+        search_list = await asyncio.gather(*search_tasks)
+    else:
+        print(f"  没有高分建议词，search_list为空")
+
+    # 4. 构建q_list_next
+    print(f"\n[步骤4] 构建q_list_next...")
+    q_list_next = []
+    existing_q_texts = set()  # 用于去重
+    add_word_details = {}  # 保存每个seed对应的组合词列表
+    all_seed_combinations = []  # 保存本轮所有seed的组合词（用于后续构建seed_list_next）
+
+    # 4.1 对于seed_list中的每个seed，从word_list_1中选词组合，产生Top 5
+    print(f"\n  4.1 为每个seed加词（产生Top 5组合）...")
+    for seed in seed_list:
+        print(f"\n    处理seed: {seed.text}")
+
+        # 剪枝检查：跳过被剪枝的seed
+        if seed.text in pruned_query_texts:
+            print(f"      ⊗ 跳过被剪枝的seed: {seed.text}")
+            continue
+
+        # 从固定词库word_list_1筛选候选词
+        candidate_words = []
+        for word in word_list_1:
+            # 检查词是否已在seed中
+            if word.text in seed.text:
+                continue
+            # 检查词是否已被添加过
+            if word.text in seed.added_words:
+                continue
+            candidate_words.append(word)
+
+        if not candidate_words:
+            print(f"      没有可用的候选词")
+            continue
+
+        print(f"      候选词数量: {len(candidate_words)}")
+
+        # 调用Agent一次性选择并组合Top 5（添加重试机制）
+        candidate_words_text = ', '.join([w.text for w in candidate_words])
+        selection_input = f"""
+<原始问题>
+{o}
+</原始问题>
+
+<当前Seed>
+{seed.text}
+</当前Seed>
+
+<候选词列表>
+{candidate_words_text}
+</候选词列表>
+
+请从候选词列表中选择最多5个最合适的词，分别与当前seed组合成新的query。
+"""
+
+        # 重试机制
+        max_retries = 2
+        selection_result = None
+        for attempt in range(max_retries):
+            try:
+                result = await Runner.run(word_selector, selection_input)
+                selection_result = result.final_output
+                break  # 成功则跳出
+            except Exception as e:
+                error_msg = str(e)
+                if attempt < max_retries - 1:
+                    print(f"      ⚠️  选词失败 (尝试 {attempt+1}/{max_retries}): {error_msg[:100]}")
+                    await asyncio.sleep(1)
+                else:
+                    print(f"      ❌ 选词失败，跳过该seed: {error_msg[:100]}")
+                    break
+
+        if selection_result is None:
+            print(f"      跳过seed: {seed.text}")
+            continue
+
+        print(f"      Agent选择了 {len(selection_result.combinations)} 个组合")
+        print(f"      整体选择思路: {selection_result.overall_reasoning}")
+
+        # 并发评估所有组合的相关度
+        async def evaluate_combination(comb: WordCombination) -> dict:
+            combined = comb.combined_query
+
+            # 验证：组合结果必须包含完整的seed和word
+            # 检查是否包含seed的所有字符
+            seed_chars_in_combined = all(char in combined for char in seed.text)
+            # 检查是否包含word的所有字符
+            word_chars_in_combined = all(char in combined for char in comb.selected_word)
+
+            if not seed_chars_in_combined or not word_chars_in_combined:
+                print(f"        ⚠️  警告：组合不完整")
+                print(f"          Seed: {seed.text}")
+                print(f"          Word: {comb.selected_word}")
+                print(f"          组合: {combined}")
+                print(f"          包含完整seed? {seed_chars_in_combined}")
+                print(f"          包含完整word? {word_chars_in_combined}")
+                # 返回极低分数，让这个组合不会被选中
+                return {
+                    'word': comb.selected_word,
+                    'query': combined,
+                    'score': -1.0,  # 极低分数
+                    'reason': f"组合不完整：缺少seed或word的部分内容",
+                    'reasoning': comb.reasoning
+                }
+
+            # 正常评估，根据轮次选择 prompt
+            score, reason = await evaluate_with_o(combined, o, context.evaluation_cache, round_num=round_num)
+            return {
+                'word': comb.selected_word,
+                'query': combined,
+                'score': score,
+                'reason': reason,
+                'reasoning': comb.reasoning
+            }
+
+        eval_tasks = [evaluate_combination(comb) for comb in selection_result.combinations]
+        top_5 = await asyncio.gather(*eval_tasks)
+
+        print(f"      评估完成，得到 {len(top_5)} 个组合")
+
+        # 将Top 5全部加入q_list_next（去重检查 + 得分过滤）
+        for comb in top_5:
+            # 得分过滤：组合词必须比种子提升至少REQUIRED_SCORE_GAIN才能加入下一轮
+            if comb['score'] < seed.score_with_o + REQUIRED_SCORE_GAIN:
+                print(f"        ⊗ 跳过低分: {comb['query']} (分数{comb['score']:.2f} < 种子{seed.score_with_o:.2f} + {REQUIRED_SCORE_GAIN:.2f})")
+                continue
+
+            # 去重检查
+            if comb['query'] in existing_q_texts:
+                print(f"        ⊗ 跳过重复: {comb['query']}")
+                continue
+
+            print(f"        ✓ {comb['query']} (分数: {comb['score']:.2f} > 种子: {seed.score_with_o:.2f})")
+
+            new_q = Q(
+                text=comb['query'],
+                score_with_o=comb['score'],
+                reason=comb['reason'],
+                from_source="add"
+            )
+            q_list_next.append(new_q)
+            existing_q_texts.add(comb['query'])  # 记录到去重集合
+
+            # 记录已添加的词
+            seed.added_words.append(comb['word'])
+
+        # 保存到add_word_details
+        add_word_details[seed.text] = [
+            {
+                "text": comb['query'],
+                "score": comb['score'],
+                "reason": comb['reason'],
+                "selected_word": comb['word'],
+                "seed_score": seed.score_with_o,  # 添加原始种子的得分
+                "type": "add"
+            }
+            for comb in top_5
+        ]
+
+        # 保存到all_seed_combinations（用于构建seed_list_next）
+        # 附加seed_score，用于后续过滤
+        for comb in top_5:
+            comb['seed_score'] = seed.score_with_o
+        all_seed_combinations.extend(top_5)
+
+    # 4.2 对于sug_list_list中，每个sug大于来自的query分数，加到q_list_next（去重检查）
+    print(f"\n  4.2 将高分sug加入q_list_next...")
+    for sug in all_sugs:
+        # 剪枝检查：跳过来自被剪枝query的sug
+        if sug.from_q and sug.from_q.text in pruned_query_texts:
+            print(f"    ⊗ 跳过来自被剪枝query的sug: {sug.text} (来源: {sug.from_q.text})")
+            continue
+
+        # sug必须比来源query提升至少REQUIRED_SCORE_GAIN才能加入下一轮
+        if sug.from_q and sug.score_with_o >= sug.from_q.score_with_o + REQUIRED_SCORE_GAIN:
+            # 去重检查
+            if sug.text in existing_q_texts:
+                print(f"    ⊗ 跳过重复: {sug.text}")
+                continue
+
+            new_q = Q(
+                text=sug.text,
+                score_with_o=sug.score_with_o,
+                reason=sug.reason,
+                from_source="sug"
+            )
+            q_list_next.append(new_q)
+            existing_q_texts.add(sug.text)  # 记录到去重集合
+            print(f"    ✓ {sug.text} (分数: {sug.score_with_o:.2f} >= 来源query: {sug.from_q.score_with_o:.2f} + {REQUIRED_SCORE_GAIN:.2f})")
+
+    # 5. 构建seed_list_next（关键修改：不保留上一轮的seed）
+    print(f"\n[步骤5] 构建seed_list_next（不保留上轮seed）...")
+    seed_list_next = []
+    existing_seed_texts = set()
+
+    # 5.1 加入本轮所有组合词（只加入得分提升的）
+    print(f"  5.1 加入本轮所有组合词（得分过滤）...")
+    for comb in all_seed_combinations:
+        # 得分过滤：组合词必须比种子提升至少REQUIRED_SCORE_GAIN才作为下一轮种子
+        seed_score = comb.get('seed_score', 0)
+        if comb['score'] < seed_score + REQUIRED_SCORE_GAIN:
+            print(f"    ⊗ 跳过低分: {comb['query']} (分数{comb['score']:.2f} < 种子{seed_score:.2f} + {REQUIRED_SCORE_GAIN:.2f})")
+            continue
+
+        if comb['query'] not in existing_seed_texts:
+            new_seed = Seed(
+                text=comb['query'],
+                added_words=[],  # 新seed的added_words清空
+                from_type="add",
+                score_with_o=comb['score']
+            )
+            seed_list_next.append(new_seed)
+            existing_seed_texts.add(comb['query'])
+            print(f"    ✓ {comb['query']} (分数: {comb['score']:.2f} >= 种子: {seed_score:.2f} + {REQUIRED_SCORE_GAIN:.2f})")
+
+    # 5.2 加入高分sug
+    print(f"  5.2 加入高分sug...")
+    for sug in all_sugs:
+        # 剪枝检查：跳过来自被剪枝query的sug
+        if sug.from_q and sug.from_q.text in pruned_query_texts:
+            continue
+
+        # sug必须比来源query提升至少REQUIRED_SCORE_GAIN才作为下一轮种子
+        if sug.from_q and sug.score_with_o >= sug.from_q.score_with_o + REQUIRED_SCORE_GAIN and sug.text not in existing_seed_texts:
+            new_seed = Seed(
+                text=sug.text,
+                added_words=[],
+                from_type="sug",
+                score_with_o=sug.score_with_o
+            )
+            seed_list_next.append(new_seed)
+            existing_seed_texts.add(sug.text)
+            print(f"    ✓ {sug.text} (分数: {sug.score_with_o:.2f} >= 来源query: {sug.from_q.score_with_o:.2f} + {REQUIRED_SCORE_GAIN:.2f})")
+
+    # 序列化搜索结果数据（包含帖子详情）
+    search_results_data = []
+    for search in search_list:
+        search_results_data.append({
+            "text": search.text,
+            "score_with_o": search.score_with_o,
+            "post_list": [
+                {
+                    "note_id": post.note_id,
+                    "note_url": post.note_url,
+                    "title": post.title,
+                    "body_text": post.body_text,
+                    "images": post.images,
+                    "interact_info": post.interact_info
+                }
+                for post in search.post_list
+            ]
+        })
+
+    # 记录本轮数据
+    round_data.update({
+        "sug_count": len(all_sugs),
+        "high_score_sug_count": len(high_score_sugs),
+        "search_count": len(search_list),
+        "total_posts": sum(len(s.post_list) for s in search_list),
+        "q_list_next_size": len(q_list_next),
+        "seed_list_next_size": len(seed_list_next),
+        "total_combinations": len(all_seed_combinations),
+        "pruned_query_count": len(pruned_query_texts),
+        "pruned_queries": list(pruned_query_texts),
+        "output_q_list": [{"text": q.text, "score": q.score_with_o, "reason": q.reason, "from": q.from_source, "type": "query"} for q in q_list_next],
+        "seed_list_next": [{"text": seed.text, "from": seed.from_type, "score": seed.score_with_o} for seed in seed_list_next],
+        "sug_details": sug_details,
+        "add_word_details": add_word_details,
+        "search_results": search_results_data
+    })
+    context.rounds.append(round_data)
+
+    print(f"\n本轮总结:")
+    print(f"  建议词数量: {len(all_sugs)}")
+    print(f"  高分建议词: {len(high_score_sugs)}")
+    print(f"  搜索数量: {len(search_list)}")
+    print(f"  帖子总数: {sum(len(s.post_list) for s in search_list)}")
+    print(f"  组合词数量: {len(all_seed_combinations)}")
+    print(f"  下轮q数量: {len(q_list_next)}")
+    print(f"  下轮seed数量: {len(seed_list_next)}")
+
+    return q_list_next, seed_list_next, search_list
+
+
+async def iterative_loop(
+    context: RunContext,
+    max_rounds: int = 2,
+    sug_threshold: float = 0.7
+):
+    """主迭代循环"""
+
+    print(f"\n{'='*60}")
+    print(f"开始迭代循环")
+    print(f"最大轮数: {max_rounds}")
+    print(f"sug阈值: {sug_threshold}")
+    print(f"{'='*60}")
+
+    # 初始化
+    seg_list, word_list_1, q_list, seed_list = await initialize(context.o, context)
+
+    # API实例
+    xiaohongshu_api = XiaohongshuSearchRecommendations()
+    xiaohongshu_search = XiaohongshuSearch()
+
+    # 保存初始化数据
+    context.rounds.append({
+        "round_num": 0,
+        "type": "initialization",
+        "seg_list": [{"text": s.text, "score": s.score_with_o, "reason": s.reason, "type": "seg"} for s in seg_list],
+        "word_list_1": [{"text": w.text, "score": w.score_with_o} for w in word_list_1],
+        "q_list_1": [{"text": q.text, "score": q.score_with_o, "reason": q.reason, "type": "query"} for q in q_list],
+        "seed_list": [{"text": s.text, "from_type": s.from_type, "score": s.score_with_o, "type": "seed"} for s in seed_list]
+    })
+
+    # 收集所有搜索结果
+    all_search_list = []
+
+    # 迭代
+    round_num = 1
+    while q_list and round_num <= max_rounds:
+        q_list, seed_list, search_list = await run_round(
+            round_num=round_num,
+            q_list=q_list,
+            word_list_1=word_list_1,  # 传递固定词库
+            seed_list=seed_list,
+            o=context.o,
+            context=context,
+            xiaohongshu_api=xiaohongshu_api,
+            xiaohongshu_search=xiaohongshu_search,
+            sug_threshold=sug_threshold
+        )
+
+        all_search_list.extend(search_list)
+        round_num += 1
+
+    print(f"\n{'='*60}")
+    print(f"迭代完成")
+    print(f"  总轮数: {round_num - 1}")
+    print(f"  总搜索次数: {len(all_search_list)}")
+    print(f"  总帖子数: {sum(len(s.post_list) for s in all_search_list)}")
+    print(f"{'='*60}")
+
+    return all_search_list
+
+
+# ============================================================================
+# v121 新架构核心流程函数
+# ============================================================================
+
+async def initialize_v2(o: str, context: RunContext) -> list[Segment]:
+    """
+    v121 Round 0 初始化阶段
+
+    流程:
+    1. 语义分段: 调用 semantic_segmenter 将原始问题拆分成语义片段
+    2. 拆词: 对每个segment调用 word_segmenter 进行拆词
+    3. 评估: 对每个segment和词进行评估
+    4. 不进行组合（Round 0只分段和拆词）
+
+    Returns:
+        语义片段列表 (Segment)
+    """
+    print(f"\n{'='*60}")
+    print(f"Round 0: 初始化阶段（语义分段 + 拆词）")
+    print(f"{'='*60}")
+
+    # 1. 语义分段
+    print(f"\n[步骤1] 语义分段...")
+    result = await Runner.run(semantic_segmenter, o)
+    segmentation: SemanticSegmentation = result.final_output
+
+    print(f"语义分段结果: {len(segmentation.segments)} 个片段")
+    print(f"整体分段思路: {segmentation.overall_reasoning}")
+
+    segment_list = []
+    for seg_item in segmentation.segments:
+        segment = Segment(
+            text=seg_item.segment_text,
+            type=seg_item.segment_type,
+            from_o=o
+        )
+        segment_list.append(segment)
+        print(f"  - [{segment.type}] {segment.text}")
+
+    # 2. 对每个segment拆词并评估
+    print(f"\n[步骤2] 对每个segment拆词并评估...")
+
+    MAX_CONCURRENT_EVALUATIONS = 5
+    semaphore = asyncio.Semaphore(MAX_CONCURRENT_EVALUATIONS)
+
+    async def process_segment(segment: Segment) -> Segment:
+        """处理单个segment: 拆词 + 评估segment + 评估词"""
+        async with semaphore:
+            # 2.1 拆词
+            word_result = await Runner.run(word_segmenter, segment.text)
+            word_segmentation: WordSegmentation = word_result.final_output
+            segment.words = word_segmentation.words
+
+            # 2.2 评估segment与原始问题的相关度
+            segment.score_with_o, segment.reason = await evaluate_with_o(
+                segment.text, o, context.evaluation_cache
+            )
+
+            # 2.3 评估每个词与原始问题的相关度
+            word_eval_tasks = []
+            for word in segment.words:
+                async def eval_word(w: str) -> tuple[str, float, str]:
+                    score, reason = await evaluate_with_o(w, o, context.evaluation_cache)
+                    return w, score, reason
+                word_eval_tasks.append(eval_word(word))
+
+            word_results = await asyncio.gather(*word_eval_tasks)
+            for word, score, reason in word_results:
+                segment.word_scores[word] = score
+                segment.word_reasons[word] = reason
+
+            return segment
+
+    if segment_list:
+        print(f"  开始处理 {len(segment_list)} 个segment（并发限制: {MAX_CONCURRENT_EVALUATIONS}）...")
+        process_tasks = [process_segment(seg) for seg in segment_list]
+        await asyncio.gather(*process_tasks)
+
+    # 打印步骤1结果
+    print(f"\n[步骤1: 分段及拆词 结果]")
+    for segment in segment_list:
+        print(f"  [{segment.type}] {segment.text} (分数: {segment.score_with_o:.2f})")
+        print(f"    拆词: {segment.words}")
+        for word in segment.words:
+            score = segment.word_scores.get(word, 0.0)
+            print(f"      - {word}: {score:.2f}")
+
+    # 保存到context（保留旧格式以兼容）
+    context.segments = [
+        {
+            "text": seg.text,
+            "type": seg.type,
+            "score": seg.score_with_o,
+            "reason": seg.reason,
+            "words": seg.words,
+            "word_scores": seg.word_scores,
+            "word_reasons": seg.word_reasons
+        }
+        for seg in segment_list
+    ]
+
+    # 保存 Round 0 到 context.rounds（新格式用于可视化）
+    context.rounds.append({
+        "round_num": 0,
+        "type": "initialization",
+        "segments": [
+            {
+                "text": seg.text,
+                "type": seg.type,
+                "domain_index": idx,
+                "score": seg.score_with_o,
+                "reason": seg.reason,
+                "words": [
+                    {
+                        "text": word,
+                        "score": seg.word_scores.get(word, 0.0),
+                        "reason": seg.word_reasons.get(word, "")
+                    }
+                    for word in seg.words
+                ]
+            }
+            for idx, seg in enumerate(segment_list)
+        ]
+    })
+
+    print(f"\n[Round 0 完成]")
+    print(f"  分段数: {len(segment_list)}")
+    total_words = sum(len(seg.words) for seg in segment_list)
+    print(f"  总词数: {total_words}")
+
+    return segment_list
+
+
+async def run_round_v2(
+    round_num: int,
+    query_input: list[Q],
+    segments: list[Segment],
+    o: str,
+    context: RunContext,
+    xiaohongshu_api: XiaohongshuSearchRecommendations,
+    xiaohongshu_search: XiaohongshuSearch,
+    sug_threshold: float = 0.7
+) -> tuple[list[Q], list[Search]]:
+    """
+    v121 Round N 执行
+
+    正确的流程顺序:
+    1. 为 query_input 请求SUG
+    2. 评估SUG
+    3. 高分SUG搜索
+    4. N域组合（从segments生成）
+    5. 评估组合
+    6. 生成 q_list_next（组合 + 高分SUG）
+
+    Args:
+        round_num: 轮次编号 (1-4)
+        query_input: 本轮的输入query列表（Round 1是words，Round 2+是上轮输出）
+        segments: 语义片段列表（用于组合）
+        o: 原始问题
+        context: 运行上下文
+        xiaohongshu_api: 建议词API
+        xiaohongshu_search: 搜索API
+        sug_threshold: SUG搜索阈值
+
+    Returns:
+        (q_list_next, search_list)
+    """
+    print(f"\n{'='*60}")
+    print(f"Round {round_num}: {round_num}域组合")
+    print(f"{'='*60}")
+
+    round_data = {
+        "round_num": round_num,
+        "n_domains": round_num,
+        "input_query_count": len(query_input)
+    }
+
+    MAX_CONCURRENT_EVALUATIONS = 5
+    semaphore = asyncio.Semaphore(MAX_CONCURRENT_EVALUATIONS)
+
+    # 步骤1: 为 query_input 请求SUG
+    print(f"\n[步骤1] 为{len(query_input)}个输入query请求SUG...")
+    all_sugs = []
+    sug_details = {}
+
+    for q in query_input:
+        suggestions = xiaohongshu_api.get_recommendations(keyword=q.text)
+        if suggestions:
+            print(f"  {q.text}: 获取到 {len(suggestions)} 个SUG")
+            for sug_text in suggestions:
+                sug = Sug(
+                    text=sug_text,
+                    from_q=QFromQ(text=q.text, score_with_o=q.score_with_o)
+                )
+                all_sugs.append(sug)
+        else:
+            print(f"  {q.text}: 未获取到SUG")
+
+    print(f"  共获取 {len(all_sugs)} 个SUG")
+
+    # 步骤2: 评估SUG
+    if len(all_sugs) > 0:
+        print(f"\n[步骤2] 评估{len(all_sugs)}个SUG...")
+
+        async def evaluate_sug(sug: Sug) -> Sug:
+            async with semaphore:
+                sug.score_with_o, sug.reason = await evaluate_with_o(
+                    sug.text, o, context.evaluation_cache
+                )
+                return sug
+
+        eval_tasks = [evaluate_sug(sug) for sug in all_sugs]
+        await asyncio.gather(*eval_tasks)
+
+        # 打印结果
+        for sug in all_sugs:
+            print(f"    {sug.text}: {sug.score_with_o:.2f}")
+            if sug.from_q:
+                if sug.from_q.text not in sug_details:
+                    sug_details[sug.from_q.text] = []
+                sug_details[sug.from_q.text].append({
+                    "text": sug.text,
+                    "score": sug.score_with_o,
+                    "reason": sug.reason,
+                    "type": "sug"
+                })
+
+    # 步骤3: 搜索高分SUG
+    print(f"\n[步骤3] 搜索高分SUG（阈值 > {sug_threshold}）...")
+    high_score_sugs = [sug for sug in all_sugs if sug.score_with_o > sug_threshold]
+    print(f"  找到 {len(high_score_sugs)} 个高分SUG")
+
+    search_list = []
+    if len(high_score_sugs) > 0:
+        async def search_for_sug(sug: Sug) -> Search:
+            print(f"    搜索: {sug.text}")
+            try:
+                search_result = xiaohongshu_search.search(keyword=sug.text)
+                result_str = search_result.get("result", "{}")
+                if isinstance(result_str, str):
+                    result_data = json.loads(result_str)
+                else:
+                    result_data = result_str
+
+                notes = result_data.get("data", {}).get("data", [])
+                post_list = []
+                for note in notes[:10]:
+                    post = process_note_data(note)
+                    post_list.append(post)
+
+                print(f"      → 找到 {len(post_list)} 个帖子")
+
+                return Search(
+                    text=sug.text,
+                    score_with_o=sug.score_with_o,
+                    from_q=sug.from_q,
+                    post_list=post_list
+                )
+            except Exception as e:
+                print(f"      ✗ 搜索失败: {e}")
+                return Search(
+                    text=sug.text,
+                    score_with_o=sug.score_with_o,
+                    from_q=sug.from_q,
+                    post_list=[]
+                )
+
+        search_tasks = [search_for_sug(sug) for sug in high_score_sugs]
+        search_list = await asyncio.gather(*search_tasks)
+
+    # 步骤4: 生成N域组合
+    print(f"\n[步骤4] 生成{round_num}域组合...")
+    domain_combinations = generate_domain_combinations(segments, round_num)
+    print(f"  生成了 {len(domain_combinations)} 个组合")
+
+    if len(domain_combinations) == 0:
+        print(f"  无法生成{round_num}域组合")
+        # 即使无法组合，也返回高分SUG作为下轮输入
+        q_list_next = []
+        for sug in all_sugs:
+            if sug.from_q and sug.score_with_o >= sug.from_q.score_with_o + REQUIRED_SCORE_GAIN:
+                q = Q(
+                    text=sug.text,
+                    score_with_o=sug.score_with_o,
+                    reason=sug.reason,
+                    from_source="sug",
+                    type_label=""
+                )
+                q_list_next.append(q)
+
+        round_data.update({
+            "domain_combinations_count": 0,
+            "sug_count": len(all_sugs),
+            "high_score_sug_count": len(high_score_sugs),
+            "search_count": len(search_list),
+            "sug_details": sug_details,
+            "q_list_next_size": len(q_list_next)
+        })
+        context.rounds.append(round_data)
+        return q_list_next, search_list
+
+    # 步骤5: 评估所有组合
+    print(f"\n[步骤5] 评估{len(domain_combinations)}个组合...")
+
+    async def evaluate_combination(comb: DomainCombination) -> DomainCombination:
+        async with semaphore:
+            comb.score_with_o, comb.reason = await evaluate_with_o(
+                comb.text, o, context.evaluation_cache
+            )
+            return comb
+
+    eval_tasks = [evaluate_combination(comb) for comb in domain_combinations]
+    await asyncio.gather(*eval_tasks)
+
+    # 排序
+    domain_combinations.sort(key=lambda x: x.score_with_o, reverse=True)
+
+    # 打印Top 10
+    print(f"  评估完成，Top 10:")
+    for i, comb in enumerate(domain_combinations[:10], 1):
+        print(f"    {i}. {comb.text} {comb.type_label} (分数: {comb.score_with_o:.2f})")
+
+    # 步骤6: 构建 q_list_next（组合 + 高分SUG）
+    print(f"\n[步骤6] 生成下轮输入...")
+    q_list_next = []
+
+    # 6.1 添加高分组合
+    high_score_combinations = [comb for comb in domain_combinations if comb.score_with_o > REQUIRED_SCORE_GAIN]
+    for comb in high_score_combinations:
+        q = Q(
+            text=comb.text,
+            score_with_o=comb.score_with_o,
+            reason=comb.reason,
+            from_source="domain_comb",
+            type_label=comb.type_label
+        )
+        q_list_next.append(q)
+
+    print(f"  添加 {len(high_score_combinations)} 个高分组合")
+
+    # 6.2 添加高分SUG（满足增益条件）
+    high_gain_sugs = []
+    for sug in all_sugs:
+        if sug.from_q and sug.score_with_o >= sug.from_q.score_with_o + REQUIRED_SCORE_GAIN:
+            q = Q(
+                text=sug.text,
+                score_with_o=sug.score_with_o,
+                reason=sug.reason,
+                from_source="sug",
+                type_label=""
+            )
+            q_list_next.append(q)
+            high_gain_sugs.append(sug)
+
+    print(f"  添加 {len(high_gain_sugs)} 个高增益SUG（增益 > {REQUIRED_SCORE_GAIN}）")
+
+    # 保存round数据
+    search_results_data = [
+        {
+            "text": search.text,
+            "score_with_o": search.score_with_o,
+            "post_count": len(search.post_list)
+        }
+        for search in search_list
+    ]
+
+    round_data.update({
+        "input_queries": [{"text": q.text, "score": q.score_with_o, "from_source": q.from_source, "type": "input"} for q in query_input],
+        "domain_combinations_count": len(domain_combinations),
+        "domain_combinations_top10": [
+            {
+                "text": comb.text,
+                "type_label": comb.type_label,
+                "score": comb.score_with_o,
+                "reason": comb.reason,
+                "domains": comb.domains,
+                "source_words": comb.source_words,
+                "from_segments": comb.from_segments
+            }
+            for comb in domain_combinations[:10]
+        ],
+        "high_score_combinations": [{"text": q.text, "score": q.score_with_o, "type_label": q.type_label, "type": "combination"} for q in q_list_next if q.from_source == "domain_comb"],
+        "sug_count": len(all_sugs),
+        "sug_details": sug_details,
+        "high_score_sug_count": len(high_score_sugs),
+        "high_gain_sugs": [{"text": q.text, "score": q.score_with_o, "type": "sug"} for q in q_list_next if q.from_source == "sug"],
+        "search_count": len(search_list),
+        "search_results": search_results_data,
+        "q_list_next_size": len(q_list_next)
+    })
+    context.rounds.append(round_data)
+
+    print(f"\nRound {round_num} 总结:")
+    print(f"  输入Query数: {len(query_input)}")
+    print(f"  域组合数: {len(domain_combinations)}")
+    print(f"  高分组合: {len(high_score_combinations)}")
+    print(f"  SUG数: {len(all_sugs)}")
+    print(f"  高分SUG数: {len(high_score_sugs)}")
+    print(f"  高增益SUG: {len(high_gain_sugs)}")
+    print(f"  搜索数: {len(search_list)}")
+    print(f"  下轮Query数: {len(q_list_next)}")
+
+    return q_list_next, search_list
+
+
+async def iterative_loop_v2(
+    context: RunContext,
+    max_rounds: int = 4,
+    sug_threshold: float = 0.7
+):
+    """v121 主迭代循环"""
+
+    print(f"\n{'='*60}")
+    print(f"开始v121迭代循环（语义分段跨域组词版）")
+    print(f"最大轮数: {max_rounds}")
+    print(f"sug阈值: {sug_threshold}")
+    print(f"{'='*60}")
+
+    # Round 0: 初始化（语义分段 + 拆词）
+    segments = await initialize_v2(context.o, context)
+
+    # API实例
+    xiaohongshu_api = XiaohongshuSearchRecommendations()
+    xiaohongshu_search = XiaohongshuSearch()
+
+    # 收集所有搜索结果
+    all_search_list = []
+
+    # 准备 Round 1 的输入：从 segments 提取所有 words
+    query_input = extract_words_from_segments(segments)
+    print(f"\n提取了 {len(query_input)} 个词作为 Round 1 的输入")
+
+    # Round 1-N: 迭代循环
+    num_segments = len(segments)
+    actual_max_rounds = min(max_rounds, num_segments)
+    round_num = 1
+
+    while query_input and round_num <= actual_max_rounds:
+        query_input, search_list = await run_round_v2(
+            round_num=round_num,
+            query_input=query_input,  # 传递上一轮的输出
+            segments=segments,
+            o=context.o,
+            context=context,
+            xiaohongshu_api=xiaohongshu_api,
+            xiaohongshu_search=xiaohongshu_search,
+            sug_threshold=sug_threshold
+        )
+
+        all_search_list.extend(search_list)
+
+        # 如果没有新的query，提前结束
+        if not query_input:
+            print(f"\n第{round_num}轮后无新query生成，提前结束迭代")
+            break
+
+        round_num += 1
+
+    print(f"\n{'='*60}")
+    print(f"迭代完成")
+    print(f"  实际轮数: {round_num}")
+    print(f"  总搜索次数: {len(all_search_list)}")
+    print(f"  总帖子数: {sum(len(s.post_list) for s in all_search_list)}")
+    print(f"{'='*60}")
+
+    return all_search_list
+
+
+# ============================================================================
+# 主函数
+# ============================================================================
+
+async def main(input_dir: str, max_rounds: int = 2, sug_threshold: float = 0.7, visualize: bool = False):
+    """主函数"""
+    current_time, log_url = set_trace()
+
+    # 读取输入
+    input_context_file = os.path.join(input_dir, 'context.md')
+    input_q_file = os.path.join(input_dir, 'q.md')
+
+    c = read_file_as_string(input_context_file)  # 原始需求
+    o = read_file_as_string(input_q_file)  # 原始问题
+
+    # 版本信息
+    version = os.path.basename(__file__)
+    version_name = os.path.splitext(version)[0]
+
+    # 日志目录
+    log_dir = os.path.join(input_dir, "output", version_name, current_time)
+
+    # 创建运行上下文
+    run_context = RunContext(
+        version=version,
+        input_files={
+            "input_dir": input_dir,
+            "context_file": input_context_file,
+            "q_file": input_q_file,
+        },
+        c=c,
+        o=o,
+        log_dir=log_dir,
+        log_url=log_url,
+    )
+
+    # 创建日志目录
+    os.makedirs(run_context.log_dir, exist_ok=True)
+
+    # 配置日志文件
+    log_file_path = os.path.join(run_context.log_dir, "run.log")
+    log_file = open(log_file_path, 'w', encoding='utf-8')
+
+    # 重定向stdout到TeeLogger（同时输出到控制台和文件）
+    original_stdout = sys.stdout
+    sys.stdout = TeeLogger(original_stdout, log_file)
+
+    try:
+        print(f"📝 日志文件: {log_file_path}")
+        print(f"{'='*60}\n")
+
+        # 执行迭代 (v121: 使用新架构)
+        all_search_list = await iterative_loop_v2(
+            run_context,
+            max_rounds=max_rounds,
+            sug_threshold=sug_threshold
+        )
+
+        # 格式化输出
+        output = f"原始需求：{run_context.c}\n"
+        output += f"原始问题：{run_context.o}\n"
+        output += f"总搜索次数：{len(all_search_list)}\n"
+        output += f"总帖子数：{sum(len(s.post_list) for s in all_search_list)}\n"
+        output += "\n" + "="*60 + "\n"
+
+        if all_search_list:
+            output += "【搜索结果】\n\n"
+            for idx, search in enumerate(all_search_list, 1):
+                output += f"{idx}. 搜索词: {search.text} (分数: {search.score_with_o:.2f})\n"
+                output += f"   帖子数: {len(search.post_list)}\n"
+                if search.post_list:
+                    for post_idx, post in enumerate(search.post_list[:3], 1):  # 只显示前3个
+                        output += f"   {post_idx}) {post.title}\n"
+                        output += f"      URL: {post.note_url}\n"
+                output += "\n"
+        else:
+            output += "未找到搜索结果\n"
+
+        run_context.final_output = output
+
+        print(f"\n{'='*60}")
+        print("最终结果")
+        print(f"{'='*60}")
+        print(output)
+
+        # 保存上下文文件
+        context_file_path = os.path.join(run_context.log_dir, "run_context.json")
+        context_dict = run_context.model_dump()
+        with open(context_file_path, "w", encoding="utf-8") as f:
+            json.dump(context_dict, f, ensure_ascii=False, indent=2)
+        print(f"\nRunContext saved to: {context_file_path}")
+
+        # 保存详细的搜索结果
+        search_results_path = os.path.join(run_context.log_dir, "search_results.json")
+        search_results_data = [s.model_dump() for s in all_search_list]
+        with open(search_results_path, "w", encoding="utf-8") as f:
+            json.dump(search_results_data, f, ensure_ascii=False, indent=2)
+        print(f"Search results saved to: {search_results_path}")
+
+        # 可视化
+        if visualize:
+            import subprocess
+            output_html = os.path.join(run_context.log_dir, "visualization.html")
+            print(f"\n🎨 生成可视化HTML...")
+
+            # 获取绝对路径
+            abs_context_file = os.path.abspath(context_file_path)
+            abs_output_html = os.path.abspath(output_html)
+
+            # 运行可视化脚本
+            result = subprocess.run([
+                "node",
+                "visualization/sug_v6_1_2_121/index.js",
+                abs_context_file,
+                abs_output_html
+            ])
+
+            if result.returncode == 0:
+                print(f"✅ 可视化已生成: {output_html}")
+            else:
+                print(f"❌ 可视化生成失败")
+
+    finally:
+        # 恢复stdout
+        sys.stdout = original_stdout
+        log_file.close()
+        print(f"\n📝 运行日志已保存: {log_file_path}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="搜索query优化工具 - v6.1.2.121 语义分段跨域组词版")
+    parser.add_argument(
+        "--input-dir",
+        type=str,
+        default="input/旅游-逸趣玩旅行/如何获取能体现川西秋季特色的高质量风光摄影素材？",
+        help="输入目录路径，默认: input/旅游-逸趣玩旅行/如何获取能体现川西秋季特色的高质量风光摄影素材？"
+    )
+    parser.add_argument(
+        "--max-rounds",
+        type=int,
+        default=4,
+        help="最大轮数，默认: 4"
+    )
+    parser.add_argument(
+        "--sug-threshold",
+        type=float,
+        default=0.7,
+        help="suggestion阈值，默认: 0.7"
+    )
+    parser.add_argument(
+        "--visualize",
+        action="store_true",
+        default=True,
+        help="运行完成后自动生成可视化HTML"
+    )
+    args = parser.parse_args()
+
+    asyncio.run(main(args.input_dir, max_rounds=args.max_rounds, sug_threshold=args.sug_threshold, visualize=args.visualize))

visualization/sug_v6_1_2_121/convert_v8_to_graph.js

@@ -0,0 +1,321 @@
+/**
+ * 将 v6.1.2.8 的 run_context.json 转换成图结构
+ */
+
+function convertV8ToGraph(runContext) {
+  const nodes = {};
+  const edges = [];
+  const iterations = {};
+
+  const o = runContext.o || '原始问题';
+  const rounds = runContext.rounds || [];
+
+  // 添加原始问题根节点
+  const rootId = 'root_o';
+  nodes[rootId] = {
+    type: 'root',
+    query: o,
+    level: 0,
+    relevance_score: 1.0,
+    strategy: '原始问题',
+    iteration: 0,
+    is_terminated: false,
+    no_suggestion_rounds: 0,
+    evaluation_reason: '用户输入的原始问题',
+    is_selected: true
+  };
+
+  iterations[0] = [rootId];
+
+  // 处理每一轮
+  rounds.forEach((round, roundIndex) => {
+    if (round.type === 'initialization') {
+      // 初始化阶段：处理 seg_list
+      const roundNum = 0;
+      if (!iterations[roundNum]) iterations[roundNum] = [];
+
+      round.seg_list.forEach((seg, segIndex) => {
+        const segId = `seg_${seg.text}_${roundNum}`;
+        nodes[segId] = {
+          type: 'seg',
+          query: seg.text,
+          level: 1,
+          relevance_score: seg.score || 0,
+          strategy: '初始分词',
+          iteration: roundNum,
+          is_terminated: false,
+          evaluation_reason: '分词专家分词结果',
+          is_selected: true,
+          parent_query: o
+        };
+
+        // 添加边：root -> seg
+        edges.push({
+          from: rootId,
+          to: segId,
+          edge_type: 'root_to_seg',
+          strategy: '初始分词'
+        });
+
+        iterations[roundNum].push(segId);
+      });
+
+      // 将 seg 作为第一轮的 q
+      round.q_list_1.forEach((q, qIndex) => {
+        const qId = `q_${q.text}_r1`;
+        const segId = `seg_${q.text}_0`;
+
+        nodes[qId] = {
+          type: 'q',
+          query: q.text,
+          level: 1,
+          relevance_score: q.score || 0,
+          strategy: '来自分词',
+          iteration: 1,
+          is_terminated: false,
+          evaluation_reason: '初始Query',
+          is_selected: true,
+          parent_query: q.text,
+          from_source: 'seg'
+        };
+
+        // 添加边：seg -> q
+        if (nodes[segId]) {
+          edges.push({
+            from: segId,
+            to: qId,
+            edge_type: 'seg_to_q',
+            strategy: '作为Query'
+          });
+        }
+
+        if (!iterations[1]) iterations[1] = [];
+        iterations[1].push(qId);
+      });
+
+    } else {
+      // 普通轮次
+      const roundNum = round.round_num;
+      const nextRoundNum = roundNum + 1;
+
+      if (!iterations[nextRoundNum]) iterations[nextRoundNum] = [];
+
+      // 添加本轮的操作步骤节点
+      const stepNodes = [];
+
+      // 获取第一个输入Query作为所有操作节点的连接点（代表本轮输入）
+      // 注意：需要从已创建的节点中查找，因为节点ID可能带有index后缀
+      let firstInputQId = null;
+      if (round.input_q_list && round.input_q_list.length > 0) {
+        const firstInputQ = round.input_q_list[0];
+        // 尝试查找匹配的节点（考虑可能有index后缀）
+        firstInputQId = Object.keys(nodes).find(id => {
+          const node = nodes[id];
+          return node.type === 'q' &&
+                 node.query === firstInputQ.text &&
+                 node.iteration === roundNum;
+        });
+      }
+
+      // 步骤1：请求sug操作节点
+      if (round.sug_count > 0) {
+        const requestSugId = `operation_request_sug_r${roundNum}`;
+        nodes[requestSugId] = {
+          type: 'operation',
+          query: `步骤1: 请求建议词 (${round.sug_count}个)`,
+          level: roundNum,
+          relevance_score: 0,
+          strategy: '请求建议词',
+          iteration: roundNum,
+          operation_type: 'request_sug',
+          detail: `为${round.input_q_list?.length || 0}个Query请求建议词，获得${round.sug_count}个建议`,
+          is_selected: true
+        };
+        stepNodes.push(requestSugId);
+
+        // 平级连接：从第一个输入Query连接
+        if (firstInputQId && nodes[firstInputQId]) {
+          edges.push({
+            from: firstInputQId,
+            to: requestSugId,
+            edge_type: 'q_to_operation',
+            strategy: '请求建议词'
+          });
+        }
+      }
+
+      // 步骤2：评估sug操作节点
+      if (round.sug_count > 0) {
+        const evaluateSugId = `operation_evaluate_sug_r${roundNum}`;
+        nodes[evaluateSugId] = {
+          type: 'operation',
+          query: `步骤2: 评估建议词 (高分:${round.high_score_sug_count})`,
+          level: roundNum,
+          relevance_score: 0,
+          strategy: '评估建议词',
+          iteration: roundNum,
+          operation_type: 'evaluate_sug',
+          detail: `评估${round.sug_count}个建议词，${round.high_score_sug_count}个达到阈值`,
+          is_selected: true
+        };
+        stepNodes.push(evaluateSugId);
+
+        // 平级连接：从第一个输入Query连接
+        if (firstInputQId && nodes[firstInputQId]) {
+          edges.push({
+            from: firstInputQId,
+            to: evaluateSugId,
+            edge_type: 'q_to_operation',
+            strategy: '评估建议词'
+          });
+        }
+      }
+
+      // 步骤3：执行搜索操作节点
+      if (round.search_count > 0) {
+        const searchOpId = `operation_search_r${roundNum}`;
+        nodes[searchOpId] = {
+          type: 'operation',
+          query: `步骤3: 执行搜索 (${round.search_count}次)`,
+          level: roundNum,
+          relevance_score: 0,
+          strategy: '执行搜索',
+          iteration: roundNum,
+          operation_type: 'search',
+          search_count: round.search_count,
+          total_posts: round.total_posts,
+          detail: `搜索${round.search_count}个高分建议词，找到${round.total_posts}个帖子`,
+          is_selected: true
+        };
+        stepNodes.push(searchOpId);
+
+        // 平级连接：从第一个输入Query连接
+        if (firstInputQId && nodes[firstInputQId]) {
+          edges.push({
+            from: firstInputQId,
+            to: searchOpId,
+            edge_type: 'q_to_operation',
+            strategy: '执行搜索'
+          });
+        }
+      }
+
+      // 步骤4：加词操作节点
+      const addWordCount = round.output_q_list?.filter(q => q.from === 'add').length || 0;
+      if (addWordCount > 0) {
+        const addWordId = `operation_add_word_r${roundNum}`;
+        nodes[addWordId] = {
+          type: 'operation',
+          query: `步骤4: 智能加词 (${addWordCount}个)`,
+          level: roundNum,
+          relevance_score: 0,
+          strategy: '智能加词',
+          iteration: roundNum,
+          operation_type: 'add_word',
+          detail: `为Seed选择词并组合，生成${addWordCount}个新Query`,
+          is_selected: true
+        };
+        stepNodes.push(addWordId);
+
+        // 平级连接：从第一个输入Query连接
+        if (firstInputQId && nodes[firstInputQId]) {
+          edges.push({
+            from: firstInputQId,
+            to: addWordId,
+            edge_type: 'q_to_operation',
+            strategy: '智能加词'
+          });
+        }
+      }
+
+      // 步骤5：筛选高分sug操作节点
+      const sugCount = round.output_q_list?.filter(q => q.from === 'sug').length || 0;
+      if (sugCount > 0) {
+        const filterSugId = `operation_filter_sug_r${roundNum}`;
+        nodes[filterSugId] = {
+          type: 'operation',
+          query: `步骤5: 筛选高分sug (${sugCount}个)`,
+          level: roundNum,
+          relevance_score: 0,
+          strategy: '筛选高分sug',
+          iteration: roundNum,
+          operation_type: 'filter_sug',
+          detail: `筛选出${sugCount}个分数高于来源Query的建议词`,
+          is_selected: true
+        };
+        stepNodes.push(filterSugId);
+
+        // 平级连接：从第一个输入Query连接
+        if (firstInputQId && nodes[firstInputQId]) {
+          edges.push({
+            from: firstInputQId,
+            to: filterSugId,
+            edge_type: 'q_to_operation',
+            strategy: '筛选高分sug'
+          });
+        }
+      }
+
+      // 将操作节点添加到当前轮次
+      stepNodes.forEach(nodeId => {
+        if (!iterations[roundNum]) iterations[roundNum] = [];
+        iterations[roundNum].push(nodeId);
+      });
+
+      // 处理输出的 q_list
+      if (round.output_q_list) {
+        round.output_q_list.forEach((q, qIndex) => {
+          const qId = `q_${q.text}_r${nextRoundNum}_${qIndex}`;
+
+          nodes[qId] = {
+            type: 'q',
+            query: q.text,
+            level: nextRoundNum,
+            relevance_score: q.score || 0,
+            strategy: q.from === 'add' ? '加词生成' : q.from === 'sug' ? '建议词' : '未知',
+            iteration: nextRoundNum,
+            is_terminated: false,
+            evaluation_reason: `来源: ${q.from}`,
+            is_selected: true,
+            from_source: q.from
+          };
+
+          // 连接到对应的操作节点
+          if (q.from === 'add') {
+            // 加词生成的Query连接到加词操作节点
+            const addWordId = `operation_add_word_r${roundNum}`;
+            if (nodes[addWordId]) {
+              edges.push({
+                from: addWordId,
+                to: qId,
+                edge_type: 'operation_to_q',
+                strategy: '加词生成'
+              });
+            }
+          } else if (q.from === 'sug') {
+            // sug生成的Query连接到筛选sug操作节点
+            const filterSugId = `operation_filter_sug_r${roundNum}`;
+            if (nodes[filterSugId]) {
+              edges.push({
+                from: filterSugId,
+                to: qId,
+                edge_type: 'operation_to_q',
+                strategy: '建议词生成'
+              });
+            }
+          }
+
+          iterations[nextRoundNum].push(qId);
+        });
+      }
+    }
+  });
+
+  return {
+    nodes,
+    edges,
+    iterations
+  };
+}
+
+module.exports = { convertV8ToGraph };

visualization/sug_v6_1_2_121/convert_v8_to_graph_v2.js

@@ -0,0 +1,887 @@
+/**
+ * 将 v6.1.2.8 的 run_context.json 转换成按 Round > 步骤 > 数据 组织的图结构
+ */
+
+function convertV8ToGraphV2(runContext, searchResults) {
+  const nodes = {};
+  const edges = [];
+  const iterations = {};
+
+  const o = runContext.o || '原始问题';
+  const rounds = runContext.rounds || [];
+
+  // 添加原始问题根节点
+  const rootId = 'root_o';
+  nodes[rootId] = {
+    type: 'root',
+    query: o,
+    level: 0,
+    relevance_score: 1.0,
+    strategy: '原始问题',
+    iteration: 0,
+    is_selected: true
+  };
+
+  iterations[0] = [rootId];
+
+  // 处理每一轮
+  rounds.forEach((round, roundIndex) => {
+    if (round.type === 'initialization') {
+      // Round 0: 初始化阶段
+      const roundNum = 0;
+      const roundId = `round_${roundNum}`;
+
+      // 创建 Round 节点
+      nodes[roundId] = {
+        type: 'round',
+        query: `Round ${roundNum} (初始化)`,
+        level: roundNum,
+        relevance_score: 0,
+        strategy: '初始化',
+        iteration: roundNum,
+        is_selected: true
+      };
+
+      edges.push({
+        from: rootId,
+        to: roundId,
+        edge_type: 'root_to_round',
+        strategy: '初始化'
+      });
+
+      if (!iterations[roundNum]) iterations[roundNum] = [];
+      iterations[roundNum].push(roundId);
+
+      // 创建分词步骤节点
+      const segStepId = `step_seg_r${roundNum}`;
+      nodes[segStepId] = {
+        type: 'step',
+        query: `步骤：分词 (${round.seg_list?.length || 0}个分词)`,
+        level: roundNum,
+        relevance_score: 0,
+        strategy: '分词',
+        iteration: roundNum,
+        is_selected: true
+      };
+
+      edges.push({
+        from: roundId,
+        to: segStepId,
+        edge_type: 'round_to_step',
+        strategy: '分词'
+      });
+
+      iterations[roundNum].push(segStepId);
+
+      // 添加分词结果作为步骤的子节点
+      round.seg_list?.forEach((seg, segIndex) => {
+        const segId = `seg_${seg.text}_${roundNum}_${segIndex}`;
+        nodes[segId] = {
+          type: 'seg',
+          query: seg.text,
+          level: roundNum + 1,
+          relevance_score: seg.score || 0,
+          evaluationReason: seg.reason || '',
+          strategy: '分词结果',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: segStepId,
+          to: segId,
+          edge_type: 'step_to_data',
+          strategy: '分词结果'
+        });
+
+        if (!iterations[roundNum + 1]) iterations[roundNum + 1] = [];
+        iterations[roundNum + 1].push(segId);
+      });
+
+    } else {
+      // 普通轮次
+      const roundNum = round.round_num;
+      const roundId = `round_${roundNum}`;
+
+      // 创建 Round 节点
+      nodes[roundId] = {
+        type: 'round',
+        query: `Round ${roundNum}`,
+        level: roundNum * 10, // 使用10的倍数作为层级
+        relevance_score: 0,
+        strategy: `第${roundNum}轮`,
+        iteration: roundNum,
+        is_selected: true
+      };
+
+      edges.push({
+        from: rootId,
+        to: roundId,
+        edge_type: 'root_to_round',
+        strategy: `第${roundNum}轮`
+      });
+
+      if (!iterations[roundNum * 10]) iterations[roundNum * 10] = [];
+      iterations[roundNum * 10].push(roundId);
+
+      // 步骤1: 请求&评估推荐词
+      if (round.sug_details && Object.keys(round.sug_details).length > 0) {
+        const sugStepId = `step_sug_r${roundNum}`;
+        const totalSugs = Object.values(round.sug_details).reduce((sum, list) => sum + list.length, 0);
+
+        nodes[sugStepId] = {
+          type: 'step',
+          query: `步骤1: 请求&评估推荐词 (${totalSugs}个)`,
+          level: roundNum * 10 + 1,
+          relevance_score: 0,
+          strategy: '请求&评估推荐词',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: roundId,
+          to: sugStepId,
+          edge_type: 'round_to_step',
+          strategy: '推荐词'
+        });
+
+        iterations[roundNum * 10].push(sugStepId);
+
+        // 为每个 Q 创建节点
+        Object.keys(round.sug_details).forEach((qText, qIndex) => {
+          // 从q_list_1中查找对应的q获取分数和理由
+          // Round 0: 从q_list_1查找; Round 1+: 从input_q_list查找
+          let qData = {};
+          if (roundNum === 0) {
+            qData = round.q_list_1?.find(q => q.text === qText) || {};
+          } else {
+            // 从当前轮的input_q_list中查找
+            qData = round.input_q_list?.find(q => q.text === qText) || {};
+          }
+          const qId = `q_${qText}_r${roundNum}_${qIndex}`;
+          nodes[qId] = {
+            type: 'q',
+            query: qText,
+            level: roundNum * 10 + 2,
+            relevance_score: qData.score || 0,
+            evaluationReason: qData.reason || '',
+            strategy: 'Query',
+            iteration: roundNum,
+            is_selected: true
+          };
+
+          edges.push({
+            from: sugStepId,
+            to: qId,
+            edge_type: 'step_to_q',
+            strategy: 'Query'
+          });
+
+          if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+          iterations[roundNum * 10 + 2].push(qId);
+
+          // 为每个 Q 的 sug 创建节点
+          const sugs = round.sug_details[qText] || [];
+          sugs.forEach((sug, sugIndex) => {
+            const sugId = `sug_${sug.text}_r${roundNum}_q${qIndex}_${sugIndex}`;
+            nodes[sugId] = {
+              type: 'sug',
+              query: sug.text,
+              level: roundNum * 10 + 3,
+              relevance_score: sug.score || 0,
+              evaluationReason: sug.reason || '',
+              strategy: '推荐词',
+              iteration: roundNum,
+              is_selected: true
+            };
+
+            edges.push({
+              from: qId,
+              to: sugId,
+              edge_type: 'q_to_sug',
+              strategy: '推荐词'
+            });
+
+            if (!iterations[roundNum * 10 + 3]) iterations[roundNum * 10 + 3] = [];
+            iterations[roundNum * 10 + 3].push(sugId);
+          });
+        });
+      }
+
+      // 步骤2: 筛选并执行搜索
+      const searchStepId = `step_search_r${roundNum}`;
+      const searchCountText = round.search_count > 0
+        ? `筛选${round.high_score_sug_count}个高分词，搜索${round.search_count}次，${round.total_posts}个帖子`
+        : `无高分推荐词，未执行搜索`;
+
+      nodes[searchStepId] = {
+        type: 'step',
+        query: `步骤2: 筛选并执行搜索 (${searchCountText})`,
+        level: roundNum * 10 + 1,
+        relevance_score: 0,
+        strategy: '筛选并执行搜索',
+        iteration: roundNum,
+        is_selected: true
+      };
+
+      edges.push({
+        from: roundId,
+        to: searchStepId,
+        edge_type: 'round_to_step',
+        strategy: '搜索'
+      });
+
+      iterations[roundNum * 10].push(searchStepId);
+
+      // 只有在有搜索结果时才添加搜索词和帖子
+      // 优先使用 round.search_results（新格式），否则使用外部传入的 searchResults（兼容旧版本）
+      const roundSearchResults = round.search_results || searchResults;
+      if (round.search_count > 0 && roundSearchResults) {
+        if (Array.isArray(roundSearchResults)) {
+          roundSearchResults.forEach((search, searchIndex) => {
+            const searchWordId = `search_${search.text}_r${roundNum}_${searchIndex}`;
+            nodes[searchWordId] = {
+              type: 'search_word',
+              query: search.text,
+              level: roundNum * 10 + 2,
+              relevance_score: search.score_with_o || 0,
+              strategy: '搜索词',
+              iteration: roundNum,
+              is_selected: true
+            };
+
+            edges.push({
+              from: searchStepId,
+              to: searchWordId,
+              edge_type: 'step_to_search_word',
+              strategy: '搜索词'
+            });
+
+            if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+            iterations[roundNum * 10 + 2].push(searchWordId);
+
+            // 添加帖子
+            if (search.post_list && search.post_list.length > 0) {
+              search.post_list.forEach((post, postIndex) => {
+                const postId = `post_${post.note_id}_${searchIndex}_${postIndex}`;
+
+                // 准备图片列表，将URL字符串转换为对象格式供轮播图使用
+                const imageList = (post.images || []).map(url => ({
+                  image_url: url
+                }));
+
+                nodes[postId] = {
+                  type: 'post',
+                  query: post.title,
+                  level: roundNum * 10 + 3,
+                  relevance_score: 0,
+                  strategy: '帖子',
+                  iteration: roundNum,
+                  is_selected: true,
+                  note_id: post.note_id,
+                  note_url: post.note_url,
+                  body_text: post.body_text || '',
+                  images: post.images || [],
+                  image_list: imageList,
+                  interact_info: post.interact_info || {}
+                };
+
+                edges.push({
+                  from: searchWordId,
+                  to: postId,
+                  edge_type: 'search_word_to_post',
+                  strategy: '搜索结果'
+                });
+
+                if (!iterations[roundNum * 10 + 3]) iterations[roundNum * 10 + 3] = [];
+                iterations[roundNum * 10 + 3].push(postId);
+              });
+            }
+          });
+        }
+      }
+
+      // 步骤3: 加词生成新查询
+      if (round.add_word_details && Object.keys(round.add_word_details).length > 0) {
+        const addWordStepId = `step_add_r${roundNum}`;
+        const totalAddWords = Object.values(round.add_word_details).reduce((sum, list) => sum + list.length, 0);
+
+        nodes[addWordStepId] = {
+          type: 'step',
+          query: `步骤3: 加词生成新查询 (${totalAddWords}个)`,
+          level: roundNum * 10 + 1,
+          relevance_score: 0,
+          strategy: '加词生成新查询',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: roundId,
+          to: addWordStepId,
+          edge_type: 'round_to_step',
+          strategy: '加词'
+        });
+
+        iterations[roundNum * 10].push(addWordStepId);
+
+        // 为每个 Seed 创建节点
+        Object.keys(round.add_word_details).forEach((seedText, seedIndex) => {
+          const seedId = `seed_${seedText}_r${roundNum}_${seedIndex}`;
+
+          // 查找seed的来源信息 - 从Round 0的seed_list查找基础种子的from_type
+          const round0 = rounds.find(r => r.round_num === 0 || r.type === 'initialization');
+          const seedInfo = round0?.seed_list?.find(s => s.text === seedText) || {};
+          const fromType = seedInfo.from_type || 'unknown';
+
+          // 根据来源设置strategy
+          let strategy;
+          if (fromType === 'seg') {
+            strategy = '初始分词';
+          } else if (fromType === 'add') {
+            strategy = '加词';
+          } else if (fromType === 'sug') {
+            strategy = '调用sug';
+          } else {
+            strategy = 'Seed';  // 默认灰色
+          }
+
+          nodes[seedId] = {
+            type: 'seed',
+            query: seedText,
+            level: roundNum * 10 + 2,
+            relevance_score: 0,
+            strategy: strategy,
+            iteration: roundNum,
+            is_selected: true
+          };
+
+          edges.push({
+            from: addWordStepId,
+            to: seedId,
+            edge_type: 'step_to_seed',
+            strategy: 'Seed'
+          });
+
+          if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+          iterations[roundNum * 10 + 2].push(seedId);
+
+          // 为每个 Seed 的组合词创建节点
+          const combinedWords = round.add_word_details[seedText] || [];
+          combinedWords.forEach((word, wordIndex) => {
+            const wordId = `add_${word.text}_r${roundNum}_seed${seedIndex}_${wordIndex}`;
+            nodes[wordId] = {
+              type: 'add_word',
+              query: word.text,
+              level: roundNum * 10 + 3,
+              relevance_score: word.score || 0,
+              evaluationReason: word.reason || '',
+              strategy: '加词生成',
+              iteration: roundNum,
+              is_selected: true,
+              selected_word: word.selected_word
+            };
+
+            edges.push({
+              from: seedId,
+              to: wordId,
+              edge_type: 'seed_to_add_word',
+              strategy: '组合词'
+            });
+
+            if (!iterations[roundNum * 10 + 3]) iterations[roundNum * 10 + 3] = [];
+            iterations[roundNum * 10 + 3].push(wordId);
+          });
+        });
+      }
+
+      // 步骤4: 筛选推荐词进入下轮
+      const filteredSugs = round.output_q_list?.filter(q => q.from === 'sug') || [];
+      if (filteredSugs.length > 0) {
+        const filterStepId = `step_filter_r${roundNum}`;
+        nodes[filterStepId] = {
+          type: 'step',
+          query: `步骤4: 筛选推荐词进入下轮 (${filteredSugs.length}个)`,
+          level: roundNum * 10 + 1,
+          relevance_score: 0,
+          strategy: '筛选推荐词进入下轮',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: roundId,
+          to: filterStepId,
+          edge_type: 'round_to_step',
+          strategy: '筛选'
+        });
+
+        iterations[roundNum * 10].push(filterStepId);
+
+        // 添加筛选出的sug
+        filteredSugs.forEach((sug, sugIndex) => {
+          const sugId = `filtered_sug_${sug.text}_r${roundNum}_${sugIndex}`;
+          nodes[sugId] = {
+            type: 'filtered_sug',
+            query: sug.text,
+            level: roundNum * 10 + 2,
+            relevance_score: sug.score || 0,
+            strategy: '进入下轮',
+            iteration: roundNum,
+            is_selected: true
+          };
+
+          edges.push({
+            from: filterStepId,
+            to: sugId,
+            edge_type: 'step_to_filtered_sug',
+            strategy: '进入下轮'
+          });
+
+          if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+          iterations[roundNum * 10 + 2].push(sugId);
+        });
+      }
+
+      // 步骤5: 构建下一轮
+      const nextRoundStepId = `step_next_round_r${roundNum}`;
+      const nextQCount = round.output_q_list?.length || 0;
+      const nextSeedCount = round.seed_list_next_size || 0;
+
+      nodes[nextRoundStepId] = {
+        type: 'step',
+        query: `步骤5: 构建下一轮 (${nextQCount}个查询, ${nextSeedCount}个种子)`,
+        level: roundNum * 10 + 1,
+        relevance_score: 0,
+        strategy: '构建下一轮',
+        iteration: roundNum,
+        is_selected: true
+      };
+
+      edges.push({
+        from: roundId,
+        to: nextRoundStepId,
+        edge_type: 'round_to_step',
+        strategy: '构建下一轮'
+      });
+
+      iterations[roundNum * 10].push(nextRoundStepId);
+
+      // 5.1: 构建下轮查询
+      if (round.output_q_list && round.output_q_list.length > 0) {
+        const nextQStepId = `step_next_q_r${roundNum}`;
+        nodes[nextQStepId] = {
+          type: 'step',
+          query: `构建下轮查询 (${nextQCount}个)`,
+          level: roundNum * 10 + 2,
+          relevance_score: 0,
+          strategy: '下轮查询',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: nextRoundStepId,
+          to: nextQStepId,
+          edge_type: 'step_to_step',
+          strategy: '查询'
+        });
+
+        if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+        iterations[roundNum * 10 + 2].push(nextQStepId);
+
+        // 添加下轮查询列表
+        round.output_q_list.forEach((q, qIndex) => {
+          const nextQId = `next_q_${q.text}_r${roundNum}_${qIndex}`;
+
+          // 根据来源设置strategy
+          let strategy;
+          if (q.from === 'seg') {
+            strategy = '初始分词';
+          } else if (q.from === 'add') {
+            strategy = '加词';
+          } else if (q.from === 'sug') {
+            strategy = '调用sug';
+          } else {
+            strategy = 'Query'; // 默认
+          }
+
+          nodes[nextQId] = {
+            type: 'next_q',
+            query: q.text,
+            level: roundNum * 10 + 3,
+            relevance_score: q.score || 0,
+            evaluationReason: q.reason || '',
+            strategy: strategy,
+            iteration: roundNum,
+            is_selected: true,
+            from_source: q.from
+          };
+
+          edges.push({
+            from: nextQStepId,
+            to: nextQId,
+            edge_type: 'step_to_next_q',
+            strategy: strategy
+          });
+
+          if (!iterations[roundNum * 10 + 3]) iterations[roundNum * 10 + 3] = [];
+          iterations[roundNum * 10 + 3].push(nextQId);
+        });
+      }
+
+      // 5.2: 构建下轮种子（如果有数据的话）
+      if (nextSeedCount > 0 && round.seed_list_next) {
+        const nextSeedStepId = `step_next_seed_r${roundNum}`;
+        nodes[nextSeedStepId] = {
+          type: 'step',
+          query: `构建下轮种子 (${nextSeedCount}个)`,
+          level: roundNum * 10 + 2,
+          relevance_score: 0,
+          strategy: '下轮种子',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: nextRoundStepId,
+          to: nextSeedStepId,
+          edge_type: 'step_to_step',
+          strategy: '种子'
+        });
+
+        if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+        iterations[roundNum * 10 + 2].push(nextSeedStepId);
+
+        // 添加下轮种子列表
+        round.seed_list_next.forEach((seed, seedIndex) => {
+          const nextSeedId = `next_seed_${seed.text}_r${roundNum}_${seedIndex}`;
+
+          // 根据来源设置strategy
+          let strategy;
+          if (seed.from === 'seg') {
+            strategy = '初始分词';
+          } else if (seed.from === 'add') {
+            strategy = '加词';
+          } else if (seed.from === 'sug') {
+            strategy = '调用sug';
+          } else {
+            strategy = 'Seed'; // 默认
+          }
+
+          nodes[nextSeedId] = {
+            type: 'next_seed',
+            query: seed.text,
+            level: roundNum * 10 + 3,
+            relevance_score: seed.score || 0,
+            strategy: strategy,
+            iteration: roundNum,
+            is_selected: true,
+            from_source: seed.from
+          };
+
+          edges.push({
+            from: nextSeedStepId,
+            to: nextSeedId,
+            edge_type: 'step_to_next_seed',
+            strategy: strategy
+          });
+
+          if (!iterations[roundNum * 10 + 3]) iterations[roundNum * 10 + 3] = [];
+          iterations[roundNum * 10 + 3].push(nextSeedId);
+        });
+      }
+    }
+  });
+
+  return {
+    nodes,
+    edges,
+    iterations
+  };
+}
+
+/**
+ * 简化版转换：专注于query和post的演化
+ * - 合并所有query节点（不区分seg/sug/add_word）
+ * - 合并相同的帖子节点
+ * - 步骤信息放在边上
+ * - 隐藏Round/Step节点
+ */
+function convertV8ToGraphSimplified(runContext, searchResults) {
+  const mergedNodes = {};
+  const edges = [];
+  const iterations = {};
+
+  const o = runContext.o || '原始问题';
+  const rounds = runContext.rounds || [];
+
+  // 添加原始问题根节点
+  const rootId = 'root_o';
+  mergedNodes[rootId] = {
+    type: 'root',
+    query: o,
+    level: 0,
+    relevance_score: 1.0,
+    strategy: '原始问题',
+    iteration: 0,
+    is_selected: true,
+    occurrences: [{round: 0, role: 'root', score: 1.0}]
+  };
+  iterations[0] = [rootId];
+
+  // 用于记录节点之间的演化关系
+  const queryEvolution = {}; // {text: {occurrences: [], parentTexts: [], childTexts: []}}
+  const postMap = {}; // {note_id: {...}}
+
+  // 第一遍：收集所有query和post
+  rounds.forEach((round, roundIndex) => {
+    const roundNum = round.round_num || roundIndex;
+
+    if (round.type === 'initialization') {
+      // Round 0: 收集分词结果
+      (round.q_list_1 || []).forEach(q => {
+        if (!queryEvolution[q.text]) {
+          queryEvolution[q.text] = {
+            occurrences: [],
+            parentTexts: new Set([o]), // 来自原始问题
+            childTexts: new Set()
+          };
+        }
+        queryEvolution[q.text].occurrences.push({
+          round: roundNum,
+          role: 'segmentation',
+          strategy: '分词',
+          score: q.score,
+          reason: q.reason
+        });
+      });
+    } else {
+      // Round 1+
+
+      // 收集sug_details (推荐词)
+      Object.entries(round.sug_details || {}).forEach(([parentText, sugs]) => {
+        sugs.forEach(sug => {
+          if (!queryEvolution[sug.text]) {
+            queryEvolution[sug.text] = {
+              occurrences: [],
+              parentTexts: new Set(),
+              childTexts: new Set()
+            };
+          }
+          queryEvolution[sug.text].occurrences.push({
+            round: roundNum,
+            role: 'sug',
+            strategy: '调用sug',
+            score: sug.score,
+            reason: sug.reason
+          });
+          queryEvolution[sug.text].parentTexts.add(parentText);
+          if (queryEvolution[parentText]) {
+            queryEvolution[parentText].childTexts.add(sug.text);
+          }
+        });
+      });
+
+      // 收集add_word_details (加词结果)
+      Object.entries(round.add_word_details || {}).forEach(([seedText, words]) => {
+        words.forEach(word => {
+          if (!queryEvolution[word.text]) {
+            queryEvolution[word.text] = {
+              occurrences: [],
+              parentTexts: new Set(),
+              childTexts: new Set()
+            };
+          }
+          queryEvolution[word.text].occurrences.push({
+            round: roundNum,
+            role: 'add_word',
+            strategy: '加词',
+            score: word.score,
+            reason: word.reason,
+            selectedWord: word.selected_word
+          });
+          queryEvolution[word.text].parentTexts.add(seedText);
+          if (queryEvolution[seedText]) {
+            queryEvolution[seedText].childTexts.add(word.text);
+          }
+        });
+      });
+
+      // 收集搜索结果和帖子
+      const roundSearchResults = round.search_results || searchResults;
+      if (roundSearchResults && Array.isArray(roundSearchResults)) {
+        roundSearchResults.forEach(search => {
+          const searchText = search.text;
+
+          // 标记这个query被用于搜索
+          if (queryEvolution[searchText]) {
+            queryEvolution[searchText].occurrences.push({
+              round: roundNum,
+              role: 'search',
+              strategy: '执行搜索',
+              score: search.score_with_o,
+              postCount: search.post_list ? search.post_list.length : 0
+            });
+          }
+
+          // 收集帖子
+          if (search.post_list && search.post_list.length > 0) {
+            search.post_list.forEach(post => {
+              if (!postMap[post.note_id]) {
+                postMap[post.note_id] = {
+                  ...post,
+                  foundByQueries: new Set(),
+                  foundInRounds: new Set()
+                };
+              }
+              postMap[post.note_id].foundByQueries.add(searchText);
+              postMap[post.note_id].foundInRounds.add(roundNum);
+
+              // 建立query到post的关系
+              if (!queryEvolution[searchText].posts) {
+                queryEvolution[searchText].posts = new Set();
+              }
+              queryEvolution[searchText].posts.add(post.note_id);
+            });
+          }
+        });
+      }
+    }
+  });
+
+  // 第二遍：创建合并后的节点
+  Object.entries(queryEvolution).forEach(([text, data]) => {
+    const nodeId = `query_${text}`;
+
+    // 获取最新的分数
+    const latestOccurrence = data.occurrences[data.occurrences.length - 1] || {};
+    const hasSearchResults = data.posts && data.posts.size > 0;
+
+    mergedNodes[nodeId] = {
+      type: 'query',
+      query: text,
+      level: Math.max(...data.occurrences.map(o => o.round), 0) * 10 + 2,
+      relevance_score: latestOccurrence.score || 0,
+      evaluationReason: latestOccurrence.reason || '',
+      strategy: data.occurrences.map(o => o.strategy).join(' + '),
+      primaryStrategy: latestOccurrence.strategy || '未知',  // 添加主要策略字段
+      iteration: Math.max(...data.occurrences.map(o => o.round), 0),
+      is_selected: true,
+      occurrences: data.occurrences,
+      hasSearchResults: hasSearchResults,
+      postCount: data.posts ? data.posts.size : 0,
+      selectedWord: data.occurrences.find(o => o.selectedWord)?.selectedWord || ''
+    };
+
+    // 添加到对应的轮次
+    const maxRound = Math.max(...data.occurrences.map(o => o.round), 0);
+    const iterKey = maxRound * 10 + 2;
+    if (!iterations[iterKey]) iterations[iterKey] = [];
+    iterations[iterKey].push(nodeId);
+  });
+
+  // 创建帖子节点
+  Object.entries(postMap).forEach(([noteId, post]) => {
+    const postId = `post_${noteId}`;
+
+    const imageList = (post.images || []).map(url => ({
+      image_url: url
+    }));
+
+    mergedNodes[postId] = {
+      type: 'post',
+      query: post.title,
+      level: 100, // 放在最后
+      relevance_score: 0,
+      strategy: '帖子',
+      iteration: Math.max(...Array.from(post.foundInRounds)),
+      is_selected: true,
+      note_id: post.note_id,
+      note_url: post.note_url,
+      body_text: post.body_text || '',
+      images: post.images || [],
+      image_list: imageList,
+      interact_info: post.interact_info || {},
+      foundByQueries: Array.from(post.foundByQueries),
+      foundInRounds: Array.from(post.foundInRounds)
+    };
+
+    if (!iterations[100]) iterations[100] = [];
+    iterations[100].push(postId);
+  });
+
+  // 第三遍：创建边
+  // 1. 原始问题 -> 分词结果
+  Object.entries(queryEvolution).forEach(([text, data]) => {
+    const nodeId = `query_${text}`;
+    const segOccurrence = data.occurrences.find(o => o.role === 'segmentation');
+
+    if (segOccurrence && data.parentTexts.has(o)) {
+      edges.push({
+        from: rootId,
+        to: nodeId,
+        edge_type: 'segmentation',
+        strategy: '分词',
+        label: '分词',
+        round: 0
+      });
+    }
+  });
+
+  // 2. Query演化关系
+  Object.entries(queryEvolution).forEach(([text, data]) => {
+    const nodeId = `query_${text}`;
+
+    data.parentTexts.forEach(parentText => {
+      if (parentText === o) return; // 跳过原始问题（已处理）
+
+      const parentNodeId = `query_${parentText}`;
+      if (!mergedNodes[parentNodeId]) return;
+
+      // 找到这个演化的策略和轮次
+      const occurrence = data.occurrences.find(o =>
+        o.role === 'sug' || o.role === 'add_word'
+      );
+
+      edges.push({
+        from: parentNodeId,
+        to: nodeId,
+        edge_type: occurrence?.role || 'evolution',
+        strategy: occurrence?.strategy || '演化',
+        label: `${occurrence?.strategy || '演化'} (R${occurrence?.round || 0})`,
+        round: occurrence?.round || 0
+      });
+    });
+  });
+
+  // 3. Query -> Post (搜索关系)
+  Object.entries(queryEvolution).forEach(([text, data]) => {
+    const nodeId = `query_${text}`;
+
+    if (data.posts && data.posts.size > 0) {
+      const searchOccurrence = data.occurrences.find(o => o.role === 'search');
+
+      data.posts.forEach(noteId => {
+        const postId = `post_${noteId}`;
+        edges.push({
+          from: nodeId,
+          to: postId,
+          edge_type: 'search',
+          strategy: '搜索',
+          label: `搜索 (${data.posts.size}个帖子)`,
+          round: searchOccurrence?.round || 0
+        });
+      });
+    }
+  });
+
+  return {
+    nodes: mergedNodes,
+    edges,
+    iterations
+  };
+}
+
+module.exports = { convertV8ToGraphV2, convertV8ToGraphSimplified };

visualization/sug_v6_1_2_121/convert_v8_to_graph_v3.js

@@ -0,0 +1,1031 @@
+/**
+ * 将 v6.1.2.8 的 run_context.json 转换成按 Round > 步骤 > 数据 组织的图结构
+ * v3: 增加 [Q] 和 [SUG] 标识前缀
+ */
+
+function convertV8ToGraphV2(runContext, searchResults) {
+  const nodes = {};
+  const edges = [];
+  const iterations = {};
+
+  const o = runContext.o || '原始问题';
+  const rounds = runContext.rounds || [];
+
+  // 添加原始问题根节点
+  const rootId = 'root_o';
+  nodes[rootId] = {
+    type: 'root',
+    query: o,
+    level: 0,
+    relevance_score: 1.0,
+    strategy: '原始问题',
+    iteration: 0,
+    is_selected: true
+  };
+
+  iterations[0] = [rootId];
+
+  // 处理每一轮
+  rounds.forEach((round, roundIndex) => {
+    if (round.type === 'initialization') {
+      // Round 0: 初始化阶段（新架构）
+      const roundNum = 0;
+      const roundId = `round_${roundNum}`;
+
+      // 创建 Round 节点
+      nodes[roundId] = {
+        type: 'round',
+        query: `Round ${roundNum} (初始化)`,
+        level: roundNum * 10,
+        relevance_score: 0,
+        strategy: '初始化',
+        iteration: roundNum,
+        is_selected: true
+      };
+
+      edges.push({
+        from: rootId,
+        to: roundId,
+        edge_type: 'root_to_round',
+        strategy: '初始化'
+      });
+
+      if (!iterations[roundNum * 10]) iterations[roundNum * 10] = [];
+      iterations[roundNum * 10].push(roundId);
+
+      // 步骤1: 分段及拆词
+      if (round.segments && round.segments.length > 0) {
+        const segStepId = `step_seg_r${roundNum}`;
+        nodes[segStepId] = {
+          type: 'step',
+          query: `步骤1: 分段及拆词 (${round.segments.length}个segment)`,
+          level: roundNum * 10 + 1,
+          relevance_score: 0,
+          strategy: '分段及拆词',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: roundId,
+          to: segStepId,
+          edge_type: 'round_to_step',
+          strategy: '分段及拆词'
+        });
+
+        iterations[roundNum * 10 + 1] = [segStepId];
+
+        // 为每个 Segment 创建节点
+        round.segments.forEach((seg, segIndex) => {
+          const segId = `segment_${segIndex}_r${roundNum}`;
+          nodes[segId] = {
+            type: 'segment',
+            query: `[${seg.type}] ${seg.text}`,
+            level: roundNum * 10 + 2,
+            relevance_score: seg.score || 0,
+            evaluationReason: seg.reason || '',
+            strategy: seg.type,
+            iteration: roundNum,
+            is_selected: true,
+            segment_type: seg.type,
+            domain_index: seg.domain_index
+          };
+
+          edges.push({
+            from: segStepId,
+            to: segId,
+            edge_type: 'step_to_segment',
+            strategy: seg.type
+          });
+
+          if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+          iterations[roundNum * 10 + 2].push(segId);
+
+          // 为每个 Word 创建节点
+          if (seg.words && seg.words.length > 0) {
+            seg.words.forEach((word, wordIndex) => {
+              const wordId = `word_${word.text}_seg${segIndex}_${wordIndex}`;
+              nodes[wordId] = {
+                type: 'word',
+                query: word.text,
+                level: roundNum * 10 + 3,
+                relevance_score: word.score || 0,
+                evaluationReason: word.reason || '',
+                strategy: 'Word',
+                iteration: roundNum,
+                is_selected: true
+              };
+
+              edges.push({
+                from: segId,
+                to: wordId,
+                edge_type: 'segment_to_word',
+                strategy: 'Word'
+              });
+
+              if (!iterations[roundNum * 10 + 3]) iterations[roundNum * 10 + 3] = [];
+              iterations[roundNum * 10 + 3].push(wordId);
+            });
+          }
+        });
+      }
+
+      // 步骤2: 域内组词
+      if (round.step2_domain_combinations && round.step2_domain_combinations.length > 0) {
+        const combStepId = `step_comb_r${roundNum}`;
+        nodes[combStepId] = {
+          type: 'step',
+          query: `步骤2: 域内组词 (${round.step2_domain_combinations.length}个组合)`,
+          level: roundNum * 10 + 1,
+          relevance_score: 0,
+          strategy: '域内组词',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: roundId,
+          to: combStepId,
+          edge_type: 'round_to_step',
+          strategy: '域内组词'
+        });
+
+        if (!iterations[roundNum * 10 + 1]) iterations[roundNum * 10 + 1] = [];
+        iterations[roundNum * 10 + 1].push(combStepId);
+
+        // 为每个域内组合创建节点
+        round.step2_domain_combinations.forEach((comb, combIndex) => {
+          const combId = `comb_${comb.text}_r${roundNum}_${combIndex}`;
+          const sourceWordsStr = comb.source_words ? comb.source_words.map(words => words.join(',')).join(' + ') : '';
+
+          nodes[combId] = {
+            type: 'domain_combination',
+            query: `${comb.text} ${comb.type_label}`,
+            level: roundNum * 10 + 2,
+            relevance_score: comb.score || 0,
+            evaluationReason: comb.reason || '',
+            strategy: '域内组合',
+            iteration: roundNum,
+            is_selected: true,
+            type_label: comb.type_label,
+            source_words: comb.source_words,
+            source_segment: comb.source_segment,
+            domain_index: comb.domain_index
+          };
+
+          edges.push({
+            from: combStepId,
+            to: combId,
+            edge_type: 'step_to_comb',
+            strategy: '域内组合'
+          });
+
+          if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+          iterations[roundNum * 10 + 2].push(combId);
+        });
+      }
+
+    } else {
+      // 普通轮次
+      const roundNum = round.round_num;
+      const roundId = `round_${roundNum}`;
+
+      // 创建 Round 节点
+      nodes[roundId] = {
+        type: 'round',
+        query: `Round ${roundNum}`,
+        level: roundNum * 10, // 使用10的倍数作为层级
+        relevance_score: 0,
+        strategy: `第${roundNum}轮`,
+        iteration: roundNum,
+        is_selected: true
+      };
+
+      edges.push({
+        from: rootId,
+        to: roundId,
+        edge_type: 'root_to_round',
+        strategy: `第${roundNum}轮`
+      });
+
+      if (!iterations[roundNum * 10]) iterations[roundNum * 10] = [];
+      iterations[roundNum * 10].push(roundId);
+
+      // 步骤1: 请求&评估推荐词
+      if (round.sug_details && Object.keys(round.sug_details).length > 0) {
+        const sugStepId = `step_sug_r${roundNum}`;
+        const totalSugs = Object.values(round.sug_details).reduce((sum, list) => sum + list.length, 0);
+
+        nodes[sugStepId] = {
+          type: 'step',
+          query: `步骤1: 请求&评估推荐词 (${totalSugs}个)`,
+          level: roundNum * 10 + 1,
+          relevance_score: 0,
+          strategy: '请求&评估推荐词',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: roundId,
+          to: sugStepId,
+          edge_type: 'round_to_step',
+          strategy: '推荐词'
+        });
+
+        iterations[roundNum * 10].push(sugStepId);
+
+        // 为每个 Q 创建节点
+        Object.keys(round.sug_details).forEach((qText, qIndex) => {
+          // 从q_list_1中查找对应的q获取分数和理由
+          // Round 0: 从q_list_1查找; Round 1+: 从input_q_list查找
+          let qData = {};
+          if (roundNum === 0) {
+            qData = round.q_list_1?.find(q => q.text === qText) || {};
+          } else {
+            // 从当前轮的input_q_list中查找
+            qData = round.input_q_list?.find(q => q.text === qText) || {};
+          }
+          const qId = `q_${qText}_r${roundNum}_${qIndex}`;
+          nodes[qId] = {
+            type: 'q',
+            query: '[Q] ' + qText,
+            level: roundNum * 10 + 2,
+            relevance_score: qData.score || 0,
+            evaluationReason: qData.reason || '',
+            strategy: 'Query',
+            iteration: roundNum,
+            is_selected: true,
+            type_label: qData.type_label || qData.typeLabel || ''
+          };
+
+          edges.push({
+            from: sugStepId,
+            to: qId,
+            edge_type: 'step_to_q',
+            strategy: 'Query'
+          });
+
+          if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+          iterations[roundNum * 10 + 2].push(qId);
+
+          // 为每个 Q 的 sug 创建节点
+          const sugs = round.sug_details[qText] || [];
+          const qScore = qData.score || 0;  // 获取父Q的得分
+
+          sugs.forEach((sug, sugIndex) => {
+            const sugScore = sug.score || 0;
+            // 比较得分决定颜色：SUG得分 > Q得分 → 绿色，否则 → 红色
+            const scoreColor = sugScore > qScore ? '#22c55e' : '#ef4444';
+
+            const sugId = `sug_${sug.text}_r${roundNum}_q${qIndex}_${sugIndex}`;
+            nodes[sugId] = {
+              type: 'sug',
+              query: '[SUG] ' + sug.text,
+              level: roundNum * 10 + 3,
+              relevance_score: sugScore,
+              evaluationReason: sug.reason || '',
+              strategy: '推荐词',
+              iteration: roundNum,
+              is_selected: true,
+              scoreColor: scoreColor,  // 新增：用于控制文字颜色
+              parentQScore: qScore     // 新增：保存父Q得分用于调试
+            };
+
+            edges.push({
+              from: qId,
+              to: sugId,
+              edge_type: 'q_to_sug',
+              strategy: '推荐词'
+            });
+
+            if (!iterations[roundNum * 10 + 3]) iterations[roundNum * 10 + 3] = [];
+            iterations[roundNum * 10 + 3].push(sugId);
+          });
+        });
+      }
+
+      // 步骤2: 筛选并执行搜索
+      const searchStepId = `step_search_r${roundNum}`;
+      const searchCountText = round.search_count > 0
+        ? `筛选${round.high_score_sug_count}个高分词，搜索${round.search_count}次，${round.total_posts}个帖子`
+        : `无高分推荐词，未执行搜索`;
+
+      nodes[searchStepId] = {
+        type: 'step',
+        query: `步骤2: 筛选并执行搜索 (${searchCountText})`,
+        level: roundNum * 10 + 1,
+        relevance_score: 0,
+        strategy: '筛选并执行搜索',
+        iteration: roundNum,
+        is_selected: true
+      };
+
+      edges.push({
+        from: roundId,
+        to: searchStepId,
+        edge_type: 'round_to_step',
+        strategy: '搜索'
+      });
+
+      iterations[roundNum * 10].push(searchStepId);
+
+      // 只有在有搜索结果时才添加搜索词和帖子
+      // 优先使用 round.search_results（新格式），否则使用外部传入的 searchResults（兼容旧版本）
+      const roundSearchResults = round.search_results || searchResults;
+      if (round.search_count > 0 && roundSearchResults) {
+        if (Array.isArray(roundSearchResults)) {
+          roundSearchResults.forEach((search, searchIndex) => {
+            const searchWordId = `search_${search.text}_r${roundNum}_${searchIndex}`;
+            nodes[searchWordId] = {
+              type: 'search_word',
+              query: '[SEARCH] ' + search.text,
+              level: roundNum * 10 + 2,
+              relevance_score: search.score_with_o || 0,
+              strategy: '搜索词',
+              iteration: roundNum,
+              is_selected: true
+            };
+
+            edges.push({
+              from: searchStepId,
+              to: searchWordId,
+              edge_type: 'step_to_search_word',
+              strategy: '搜索词'
+            });
+
+            if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+            iterations[roundNum * 10 + 2].push(searchWordId);
+
+            // 添加帖子
+            if (search.post_list && search.post_list.length > 0) {
+              search.post_list.forEach((post, postIndex) => {
+                const postId = `post_${post.note_id}_${searchIndex}_${postIndex}`;
+
+                // 准备图片列表，将URL字符串转换为对象格式供轮播图使用
+                const imageList = (post.images || []).map(url => ({
+                  image_url: url
+                }));
+
+                nodes[postId] = {
+                  type: 'post',
+                  query: '[R] ' + post.title,
+                  level: roundNum * 10 + 3,
+                  relevance_score: 0,
+                  strategy: '帖子',
+                  iteration: roundNum,
+                  is_selected: true,
+                  note_id: post.note_id,
+                  note_url: post.note_url,
+                  body_text: post.body_text || '',
+                  images: post.images || [],
+                  image_list: imageList,
+                  interact_info: post.interact_info || {}
+                };
+
+                edges.push({
+                  from: searchWordId,
+                  to: postId,
+                  edge_type: 'search_word_to_post',
+                  strategy: '搜索结果'
+                });
+
+                if (!iterations[roundNum * 10 + 3]) iterations[roundNum * 10 + 3] = [];
+                iterations[roundNum * 10 + 3].push(postId);
+              });
+            }
+          });
+        }
+      }
+
+      // 步骤3: 加词生成新查询
+      if (round.add_word_details && Object.keys(round.add_word_details).length > 0) {
+        const addWordStepId = `step_add_r${roundNum}`;
+        const totalAddWords = Object.values(round.add_word_details).reduce((sum, list) => sum + list.length, 0);
+
+        nodes[addWordStepId] = {
+          type: 'step',
+          query: `步骤3: 加词生成新查询 (${totalAddWords}个)`,
+          level: roundNum * 10 + 1,
+          relevance_score: 0,
+          strategy: '加词生成新查询',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: roundId,
+          to: addWordStepId,
+          edge_type: 'round_to_step',
+          strategy: '加词'
+        });
+
+        iterations[roundNum * 10].push(addWordStepId);
+
+        // 为每个 Seed 创建节点
+        Object.keys(round.add_word_details).forEach((seedText, seedIndex) => {
+          const seedId = `seed_${seedText}_r${roundNum}_${seedIndex}`;
+
+          // 查找seed的来源信息和分数 - 动态从正确的轮次查找
+          let seedInfo = {};
+          if (roundNum === 1) {
+            // Round 1：种子来自 Round 0 的 seed_list
+            const round0 = rounds.find(r => r.round_num === 0 || r.type === 'initialization');
+            seedInfo = round0?.seed_list?.find(s => s.text === seedText) || {};
+          } else {
+            // Round 2+：种子来自前一轮的 seed_list_next
+            const prevRound = rounds.find(r => r.round_num === roundNum - 1);
+            seedInfo = prevRound?.seed_list_next?.find(s => s.text === seedText) || {};
+          }
+          const fromType = seedInfo.from_type || seedInfo.from || 'unknown';
+
+          // 根据来源设置strategy
+          let strategy;
+          if (fromType === 'seg') {
+            strategy = '初始分词';
+          } else if (fromType === 'add') {
+            strategy = '加词';
+          } else if (fromType === 'sug') {
+            strategy = '调用sug';
+          } else {
+            strategy = 'Seed';  // 默认灰色
+          }
+
+          nodes[seedId] = {
+            type: 'seed',
+            query: seedText,
+            level: roundNum * 10 + 2,
+            relevance_score: seedInfo.score || 0,  // 从seedInfo读取种子的得分
+            strategy: strategy,
+            iteration: roundNum,
+            is_selected: true
+          };
+
+          edges.push({
+            from: addWordStepId,
+            to: seedId,
+            edge_type: 'step_to_seed',
+            strategy: 'Seed'
+          });
+
+          if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+          iterations[roundNum * 10 + 2].push(seedId);
+
+          // 为每个 Seed 的组合词创建节点
+          const combinedWords = round.add_word_details[seedText] || [];
+          combinedWords.forEach((word, wordIndex) => {
+            const wordScore = word.score || 0;
+            const seedScore = word.seed_score || 0;
+            // 比较得分决定颜色：组合词得分 > 种子得分 → 绿色，否则 → 红色
+            const scoreColor = wordScore > seedScore ? '#22c55e' : '#ef4444';
+
+            const wordId = `add_${word.text}_r${roundNum}_seed${seedIndex}_${wordIndex}`;
+            nodes[wordId] = {
+              type: 'add_word',
+              query: '[Q] ' + word.text,
+              level: roundNum * 10 + 3,
+              relevance_score: wordScore,
+              evaluationReason: word.reason || '',
+              strategy: '加词生成',
+              iteration: roundNum,
+              is_selected: true,
+              selected_word: word.selected_word,
+              seed_score: seedScore,  // 原始种子的得分
+              scoreColor: scoreColor  // 用于控制文字颜色
+            };
+
+            edges.push({
+              from: seedId,
+              to: wordId,
+              edge_type: 'seed_to_add_word',
+              strategy: '组合词'
+            });
+
+            if (!iterations[roundNum * 10 + 3]) iterations[roundNum * 10 + 3] = [];
+            iterations[roundNum * 10 + 3].push(wordId);
+          });
+        });
+      }
+
+      // 步骤4: 筛选推荐词进入下轮
+      const filteredSugs = round.output_q_list?.filter(q => q.from === 'sug') || [];
+      if (filteredSugs.length > 0) {
+        const filterStepId = `step_filter_r${roundNum}`;
+        nodes[filterStepId] = {
+          type: 'step',
+          query: `步骤4: 筛选推荐词进入下轮 (${filteredSugs.length}个)`,
+          level: roundNum * 10 + 1,
+          relevance_score: 0,
+          strategy: '筛选推荐词进入下轮',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: roundId,
+          to: filterStepId,
+          edge_type: 'round_to_step',
+          strategy: '筛选'
+        });
+
+        iterations[roundNum * 10].push(filterStepId);
+
+        // 添加筛选出的sug
+        filteredSugs.forEach((sug, sugIndex) => {
+          const sugScore = sug.score || 0;
+
+          // 尝试从sug_details中找到这个sug对应的父Q得分
+          let parentQScore = 0;
+          if (round.sug_details) {
+            for (const [qText, sugs] of Object.entries(round.sug_details)) {
+              const matchingSug = sugs.find(s => s.text === sug.text);
+              if (matchingSug) {
+                // 找到父Q的得分
+                let qData = {};
+                if (roundNum === 0) {
+                  qData = round.q_list_1?.find(q => q.text === qText) || {};
+                } else {
+                  qData = round.input_q_list?.find(q => q.text === qText) || {};
+                }
+                parentQScore = qData.score || 0;
+                break;
+              }
+            }
+          }
+
+          // 比较得分决定颜色：SUG得分 > Q得分 → 绿色，否则 → 红色
+          const scoreColor = sugScore > parentQScore ? '#22c55e' : '#ef4444';
+
+          const sugId = `filtered_sug_${sug.text}_r${roundNum}_${sugIndex}`;
+          nodes[sugId] = {
+            type: 'filtered_sug',
+            query: '[SUG] ' + sug.text,
+            level: roundNum * 10 + 2,
+            relevance_score: sugScore,
+            strategy: '进入下轮',
+            iteration: roundNum,
+            is_selected: true,
+            scoreColor: scoreColor,       // 新增：用于控制文字颜色
+            parentQScore: parentQScore    // 新增：保存父Q得分用于调试
+          };
+
+          edges.push({
+            from: filterStepId,
+            to: sugId,
+            edge_type: 'step_to_filtered_sug',
+            strategy: '进入下轮'
+          });
+
+          if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+          iterations[roundNum * 10 + 2].push(sugId);
+        });
+      }
+
+      // 步骤5: 构建下一轮
+      const nextRoundStepId = `step_next_round_r${roundNum}`;
+      const nextQCount = round.output_q_list?.length || 0;
+      const nextSeedCount = round.seed_list_next_size || 0;
+
+      nodes[nextRoundStepId] = {
+        type: 'step',
+        query: `步骤5: 构建下一轮 (${nextQCount}个查询, ${nextSeedCount}个种子)`,
+        level: roundNum * 10 + 1,
+        relevance_score: 0,
+        strategy: '构建下一轮',
+        iteration: roundNum,
+        is_selected: true
+      };
+
+      edges.push({
+        from: roundId,
+        to: nextRoundStepId,
+        edge_type: 'round_to_step',
+        strategy: '构建下一轮'
+      });
+
+      iterations[roundNum * 10].push(nextRoundStepId);
+
+      // 5.1: 构建下轮查询
+      if (round.output_q_list && round.output_q_list.length > 0) {
+        const nextQStepId = `step_next_q_r${roundNum}`;
+        nodes[nextQStepId] = {
+          type: 'step',
+          query: `构建下轮查询 (${nextQCount}个)`,
+          level: roundNum * 10 + 2,
+          relevance_score: 0,
+          strategy: '下轮查询',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: nextRoundStepId,
+          to: nextQStepId,
+          edge_type: 'step_to_step',
+          strategy: '查询'
+        });
+
+        if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+        iterations[roundNum * 10 + 2].push(nextQStepId);
+
+        // 添加下轮查询列表
+        round.output_q_list.forEach((q, qIndex) => {
+          const nextQId = `next_q_${q.text}_r${roundNum}_${qIndex}`;
+
+          // 根据来源设置strategy
+          let strategy;
+          if (q.from === 'seg') {
+            strategy = '初始分词';
+          } else if (q.from === 'add') {
+            strategy = '加词';
+          } else if (q.from === 'sug') {
+            strategy = '调用sug';
+          } else {
+            strategy = 'Query'; // 默认
+          }
+
+          nodes[nextQId] = {
+            type: 'next_q',
+            query: '[Q] ' + q.text,
+            level: roundNum * 10 + 3,
+            relevance_score: q.score || 0,
+            evaluationReason: q.reason || '',
+            strategy: strategy,
+            iteration: roundNum,
+            is_selected: true,
+            from_source: q.from,
+            type_label: q.type_label || q.typeLabel || ''
+          };
+
+          edges.push({
+            from: nextQStepId,
+            to: nextQId,
+            edge_type: 'step_to_next_q',
+            strategy: strategy
+          });
+
+          if (!iterations[roundNum * 10 + 3]) iterations[roundNum * 10 + 3] = [];
+          iterations[roundNum * 10 + 3].push(nextQId);
+        });
+      }
+
+      // 5.2: 构建下轮种子（如果有数据的话）
+      if (nextSeedCount > 0 && round.seed_list_next) {
+        const nextSeedStepId = `step_next_seed_r${roundNum}`;
+        nodes[nextSeedStepId] = {
+          type: 'step',
+          query: `构建下轮种子 (${nextSeedCount}个)`,
+          level: roundNum * 10 + 2,
+          relevance_score: 0,
+          strategy: '下轮种子',
+          iteration: roundNum,
+          is_selected: true
+        };
+
+        edges.push({
+          from: nextRoundStepId,
+          to: nextSeedStepId,
+          edge_type: 'step_to_step',
+          strategy: '种子'
+        });
+
+        if (!iterations[roundNum * 10 + 2]) iterations[roundNum * 10 + 2] = [];
+        iterations[roundNum * 10 + 2].push(nextSeedStepId);
+
+        // 添加下轮种子列表
+        round.seed_list_next.forEach((seed, seedIndex) => {
+          const nextSeedId = `next_seed_${seed.text}_r${roundNum}_${seedIndex}`;
+
+          // 根据来源设置strategy
+          let strategy;
+          if (seed.from === 'seg') {
+            strategy = '初始分词';
+          } else if (seed.from === 'add') {
+            strategy = '加词';
+          } else if (seed.from === 'sug') {
+            strategy = '调用sug';
+          } else {
+            strategy = 'Seed'; // 默认
+          }
+
+          nodes[nextSeedId] = {
+            type: 'next_seed',
+            query: seed.text,
+            level: roundNum * 10 + 3,
+            relevance_score: seed.score || 0,
+            strategy: strategy,
+            iteration: roundNum,
+            is_selected: true,
+            from_source: seed.from
+          };
+
+          edges.push({
+            from: nextSeedStepId,
+            to: nextSeedId,
+            edge_type: 'step_to_next_seed',
+            strategy: strategy
+          });
+
+          if (!iterations[roundNum * 10 + 3]) iterations[roundNum * 10 + 3] = [];
+          iterations[roundNum * 10 + 3].push(nextSeedId);
+        });
+      }
+    }
+  });
+
+  return {
+    nodes,
+    edges,
+    iterations
+  };
+}
+
+/**
+ * 简化版转换：专注于query和post的演化
+ * - 合并所有query节点（不区分seg/sug/add_word）
+ * - 合并相同的帖子节点
+ * - 步骤信息放在边上
+ * - 隐藏Round/Step节点
+ */
+function convertV8ToGraphSimplified(runContext, searchResults) {
+  const mergedNodes = {};
+  const edges = [];
+  const iterations = {};
+
+  const o = runContext.o || '原始问题';
+  const rounds = runContext.rounds || [];
+
+  // 添加原始问题根节点
+  const rootId = 'root_o';
+  mergedNodes[rootId] = {
+    type: 'root',
+    query: o,
+    level: 0,
+    relevance_score: 1.0,
+    strategy: '原始问题',
+    iteration: 0,
+    is_selected: true,
+    occurrences: [{round: 0, role: 'root', score: 1.0}]
+  };
+  iterations[0] = [rootId];
+
+  // 用于记录节点之间的演化关系
+  const queryEvolution = {}; // {text: {occurrences: [], parentTexts: [], childTexts: []}}
+  const postMap = {}; // {note_id: {...}}
+
+  // 第一遍：收集所有query和post
+  rounds.forEach((round, roundIndex) => {
+    const roundNum = round.round_num || roundIndex;
+
+    if (round.type === 'initialization') {
+      // Round 0: 收集分词结果
+      (round.q_list_1 || []).forEach(q => {
+        if (!queryEvolution[q.text]) {
+          queryEvolution[q.text] = {
+            occurrences: [],
+            parentTexts: new Set([o]), // 来自原始问题
+            childTexts: new Set()
+          };
+        }
+        queryEvolution[q.text].occurrences.push({
+          round: roundNum,
+          role: 'segmentation',
+          strategy: '分词',
+          score: q.score,
+          reason: q.reason,
+          type_label: q.type_label || q.typeLabel || ''
+        });
+      });
+    } else {
+      // Round 1+
+
+      // 收集sug_details (推荐词)
+      Object.entries(round.sug_details || {}).forEach(([parentText, sugs]) => {
+        sugs.forEach(sug => {
+          if (!queryEvolution[sug.text]) {
+            queryEvolution[sug.text] = {
+              occurrences: [],
+              parentTexts: new Set(),
+              childTexts: new Set()
+            };
+          }
+          queryEvolution[sug.text].occurrences.push({
+            round: roundNum,
+            role: 'sug',
+            strategy: '调用sug',
+            score: sug.score,
+            reason: sug.reason,
+            type_label: sug.type_label || sug.typeLabel || ''
+          });
+          queryEvolution[sug.text].parentTexts.add(parentText);
+          if (queryEvolution[parentText]) {
+            queryEvolution[parentText].childTexts.add(sug.text);
+          }
+        });
+      });
+
+      // 收集add_word_details (加词结果)
+      Object.entries(round.add_word_details || {}).forEach(([seedText, words]) => {
+        words.forEach(word => {
+          if (!queryEvolution[word.text]) {
+            queryEvolution[word.text] = {
+              occurrences: [],
+              parentTexts: new Set(),
+              childTexts: new Set()
+            };
+          }
+          queryEvolution[word.text].occurrences.push({
+            round: roundNum,
+            role: 'add_word',
+            strategy: '加词',
+            score: word.score,
+            reason: word.reason,
+            selectedWord: word.selected_word,
+            seedScore: word.seed_score,  // 添加原始种子的得分
+            type_label: word.type_label || word.typeLabel || ''
+          });
+          queryEvolution[word.text].parentTexts.add(seedText);
+          if (queryEvolution[seedText]) {
+            queryEvolution[seedText].childTexts.add(word.text);
+          }
+        });
+      });
+
+      // 收集搜索结果和帖子
+      const roundSearchResults = round.search_results || searchResults;
+      if (roundSearchResults && Array.isArray(roundSearchResults)) {
+        roundSearchResults.forEach(search => {
+          const searchText = search.text;
+
+          // 标记这个query被用于搜索
+          if (queryEvolution[searchText]) {
+            queryEvolution[searchText].occurrences.push({
+              round: roundNum,
+              role: 'search',
+              strategy: '执行搜索',
+              score: search.score_with_o,
+              postCount: search.post_list ? search.post_list.length : 0
+            });
+          }
+
+          // 收集帖子
+          if (search.post_list && search.post_list.length > 0) {
+            search.post_list.forEach(post => {
+              if (!postMap[post.note_id]) {
+                postMap[post.note_id] = {
+                  ...post,
+                  foundByQueries: new Set(),
+                  foundInRounds: new Set()
+                };
+              }
+              postMap[post.note_id].foundByQueries.add(searchText);
+              postMap[post.note_id].foundInRounds.add(roundNum);
+
+              // 建立query到post的关系
+              if (!queryEvolution[searchText].posts) {
+                queryEvolution[searchText].posts = new Set();
+              }
+              queryEvolution[searchText].posts.add(post.note_id);
+            });
+          }
+        });
+      }
+    }
+  });
+
+  // 第二遍：创建合并后的节点
+  Object.entries(queryEvolution).forEach(([text, data]) => {
+    const nodeId = `query_${text}`;
+
+    // 获取最新的分数
+    const latestOccurrence = data.occurrences[data.occurrences.length - 1] || {};
+    const hasSearchResults = data.posts && data.posts.size > 0;
+
+    mergedNodes[nodeId] = {
+      type: 'query',
+      query: '[Q] ' + text,
+      level: Math.max(...data.occurrences.map(o => o.round), 0) * 10 + 2,
+      relevance_score: latestOccurrence.score || 0,
+      evaluationReason: latestOccurrence.reason || '',
+      strategy: data.occurrences.map(o => o.strategy).join(' + '),
+      primaryStrategy: latestOccurrence.strategy || '未知',  // 添加主要策略字段
+      iteration: Math.max(...data.occurrences.map(o => o.round), 0),
+      is_selected: true,
+      occurrences: data.occurrences,
+      hasSearchResults: hasSearchResults,
+      postCount: data.posts ? data.posts.size : 0,
+      selectedWord: data.occurrences.find(o => o.selectedWord)?.selectedWord || '',
+      seed_score: data.occurrences.find(o => o.seedScore)?.seedScore,  // 添加原始种子的得分
+      type_label: latestOccurrence.type_label || ''  // 使用最新的 type_label
+    };
+
+    // 添加到对应的轮次
+    const maxRound = Math.max(...data.occurrences.map(o => o.round), 0);
+    const iterKey = maxRound * 10 + 2;
+    if (!iterations[iterKey]) iterations[iterKey] = [];
+    iterations[iterKey].push(nodeId);
+  });
+
+  // 创建帖子节点
+  Object.entries(postMap).forEach(([noteId, post]) => {
+    const postId = `post_${noteId}`;
+
+    const imageList = (post.images || []).map(url => ({
+      image_url: url
+    }));
+
+    mergedNodes[postId] = {
+      type: 'post',
+      query: '[R] ' + post.title,
+      level: 100, // 放在最后
+      relevance_score: 0,
+      strategy: '帖子',
+      iteration: Math.max(...Array.from(post.foundInRounds)),
+      is_selected: true,
+      note_id: post.note_id,
+      note_url: post.note_url,
+      body_text: post.body_text || '',
+      images: post.images || [],
+      image_list: imageList,
+      interact_info: post.interact_info || {},
+      foundByQueries: Array.from(post.foundByQueries),
+      foundInRounds: Array.from(post.foundInRounds)
+    };
+
+    if (!iterations[100]) iterations[100] = [];
+    iterations[100].push(postId);
+  });
+
+  // 第三遍：创建边
+  // 1. 原始问题 -> 分词结果
+  Object.entries(queryEvolution).forEach(([text, data]) => {
+    const nodeId = `query_${text}`;
+    const segOccurrence = data.occurrences.find(o => o.role === 'segmentation');
+
+    if (segOccurrence && data.parentTexts.has(o)) {
+      edges.push({
+        from: rootId,
+        to: nodeId,
+        edge_type: 'segmentation',
+        strategy: '分词',
+        label: '分词',
+        round: 0
+      });
+    }
+  });
+
+  // 2. Query演化关系
+  Object.entries(queryEvolution).forEach(([text, data]) => {
+    const nodeId = `query_${text}`;
+
+    data.parentTexts.forEach(parentText => {
+      if (parentText === o) return; // 跳过原始问题（已处理）
+
+      const parentNodeId = `query_${parentText}`;
+      if (!mergedNodes[parentNodeId]) return;
+
+      // 找到这个演化的策略和轮次
+      const occurrence = data.occurrences.find(o =>
+        o.role === 'sug' || o.role === 'add_word'
+      );
+
+      edges.push({
+        from: parentNodeId,
+        to: nodeId,
+        edge_type: occurrence?.role || 'evolution',
+        strategy: occurrence?.strategy || '演化',
+        label: `${occurrence?.strategy || '演化'} (R${occurrence?.round || 0})`,
+        round: occurrence?.round || 0
+      });
+    });
+  });
+
+  // 3. Query -> Post (搜索关系)
+  Object.entries(queryEvolution).forEach(([text, data]) => {
+    const nodeId = `query_${text}`;
+
+    if (data.posts && data.posts.size > 0) {
+      const searchOccurrence = data.occurrences.find(o => o.role === 'search');
+
+      data.posts.forEach(noteId => {
+        const postId = `post_${noteId}`;
+        edges.push({
+          from: nodeId,
+          to: postId,
+          edge_type: 'search',
+          strategy: '搜索',
+          label: `搜索 (${data.posts.size}个帖子)`,
+          round: searchOccurrence?.round || 0
+        });
+      });
+    }
+  });
+
+  return {
+    nodes: mergedNodes,
+    edges,
+    iterations
+  };
+}
+
+module.exports = { convertV8ToGraphV2, convertV8ToGraphSimplified };

visualization/sug_v6_1_2_121/index.js

@@ -0,0 +1,2488 @@
+#!/usr/bin/env node
+
+const fs = require('fs');
+const path = require('path');
+const { build } = require('esbuild');
+const { convertV8ToGraph } = require('./convert_v8_to_graph');
+const { convertV8ToGraphV2, convertV8ToGraphSimplified } = require('./convert_v8_to_graph_v3');
+
+// 读取命令行参数
+const args = process.argv.slice(2);
+if (args.length === 0) {
+  console.error('Usage: node index.js <path-to-run_context.json> [output.html] [--simplified]');
+  process.exit(1);
+}
+
+const inputFile = args[0];
+const outputFile = args[1] || 'query_graph_output.html';
+const useSimplified = args.includes('--simplified');
+
+// 读取输入数据
+const inputData = JSON.parse(fs.readFileSync(inputFile, 'utf-8'));
+
+// 检测数据格式并转换
+let data;
+if (inputData.rounds && inputData.o) {
+  // v6.1.2.8 格式，需要转换
+  console.log('✨ 检测到 v6.1.2.8 格式，正在转换为图结构...');
+
+  // 尝试读取 search_results.json（兼容旧版本）
+  let searchResults = null;
+  const searchResultsPath = path.join(path.dirname(inputFile), 'search_results.json');
+  if (fs.existsSync(searchResultsPath)) {
+    console.log('📄 读取外部搜索结果数据（兼容模式）...');
+    searchResults = JSON.parse(fs.readFileSync(searchResultsPath, 'utf-8'));
+  } else {
+    console.log('✅ 使用 run_context.json 中的内嵌搜索结果');
+  }
+
+  // 选择转换函数
+  let graphData;
+  let fullData = null; // 用于目录的完整数据
+
+  if (useSimplified) {
+    console.log('🎨 使用简化视图（合并query节点）');
+    // 生成简化版用于画布
+    graphData = convertV8ToGraphSimplified(inputData, searchResults);
+    // 生成完整版用于目录
+    const fullGraphData = convertV8ToGraphV2(inputData, searchResults);
+    fullData = {
+      nodes: fullGraphData.nodes,
+      edges: fullGraphData.edges,
+      iterations: fullGraphData.iterations
+    };
+    console.log(`✅ 简化版: ${Object.keys(graphData.nodes).length} 个节点, ${graphData.edges.length} 条边`);
+    console.log(`📋 完整版(用于目录): ${Object.keys(fullData.nodes).length} 个节点`);
+  } else {
+    console.log('📊 使用详细视图（完整流程）');
+    graphData = convertV8ToGraphV2(inputData, searchResults);
+    console.log(`✅ 转换完成: ${Object.keys(graphData.nodes).length} 个节点, ${graphData.edges.length} 条边`);
+  }
+
+  data = {
+    nodes: graphData.nodes,
+    edges: graphData.edges,
+    iterations: graphData.iterations,
+    fullData: fullData // 传递完整数据
+  };
+} else if (inputData.nodes && inputData.edges) {
+  // v6.1.2.5 格式，直接使用
+  console.log('✨ 检测到 v6.1.2.5 格式，直接使用');
+  data = inputData;
+} else {
+  console.error('❌ 无法识别的数据格式');
+  process.exit(1);
+}
+
+// 创建临时 React 组件文件
+const reactComponentPath = path.join(__dirname, 'temp_flow_component_v2.jsx');
+const reactComponent = `
+import React, { useState, useCallback, useMemo, useEffect } from 'react';
+import { createRoot } from 'react-dom/client';
+import {
+  ReactFlow,
+  Controls,
+  Background,
+  useNodesState,
+  useEdgesState,
+  Handle,
+  Position,
+  useReactFlow,
+  ReactFlowProvider,
+} from '@xyflow/react';
+import '@xyflow/react/dist/style.css';
+
+const data = ${JSON.stringify(data, null, 2)};
+
+// 根据节点类型获取边框颜色
+function getNodeTypeColor(type) {
+  const typeColors = {
+    'root': '#6b21a8',        // 紫色 - 根节点
+    'round': '#7c3aed',       // 深紫 - Round节点
+    'step': '#f59e0b',        // 橙色 - 步骤节点
+    'seg': '#10b981',         // 绿色 - 分词
+    'q': '#3b82f6',           // 蓝色 - Query
+    'sug': '#06b6d4',         // 青色 - Sug建议词
+    'seed': '#84cc16',        // 黄绿 - Seed
+    'add_word': '#22c55e',    // 绿色 - 加词生成
+    'search_word': '#8b5cf6', // 紫色 - 搜索词
+    'post': '#ec4899',        // 粉色 - 帖子
+    'filtered_sug': '#14b8a6',// 青绿 - 筛选的sug
+    'next_q': '#2563eb',      // 深蓝 - 下轮查询
+    'next_seed': '#65a30d',   // 深黄绿 - 下轮种子
+    'search': '#8b5cf6',      // 深紫 - 搜索（兼容旧版）
+    'operation': '#f59e0b',   // 橙色 - 操作节点（兼容旧版）
+    'query': '#3b82f6',       // 蓝色 - 查询（兼容旧版）
+    'note': '#ec4899',        // 粉色 - 帖子（兼容旧版）
+  };
+  return typeColors[type] || '#9ca3af';
+}
+
+// 查询节点组件 - 卡片样式
+function QueryNode({ id, data, sourcePosition, targetPosition }) {
+  // 所有节点默认展开
+  const expanded = true;
+
+  // 获取节点类型颜色
+  const typeColor = getNodeTypeColor(data.nodeType || 'query');
+
+  return (
+    <div>
+      <Handle
+        type="target"
+        position={targetPosition || Position.Left}
+        style={{ background: typeColor, width: 8, height: 8 }}
+      />
+      <div
+        style={{
+          padding: '12px',
+          borderRadius: '8px',
+          border: data.isHighlighted ? \`3px solid \${typeColor}\` :
+                  data.isCollapsed ? \`2px solid \${typeColor}\` :
+                  data.isSelected === false ? '2px dashed #d1d5db' :
+                  \`2px solid \${typeColor}\`,
+          background: data.isHighlighted ? '#eef2ff' :
+                      data.isSelected === false ? '#f9fafb' : 'white',
+          minWidth: '200px',
+          maxWidth: '280px',
+          boxShadow: data.isHighlighted ? '0 0 0 4px rgba(102, 126, 234, 0.25), 0 4px 16px rgba(102, 126, 234, 0.4)' :
+                     data.isCollapsed ? '0 4px 12px rgba(102, 126, 234, 0.15)' :
+                     data.level === 0 ? '0 4px 12px rgba(139, 92, 246, 0.15)' : '0 2px 6px rgba(0, 0, 0, 0.06)',
+          transition: 'all 0.3s ease',
+          cursor: 'pointer',
+          position: 'relative',
+          opacity: data.isSelected === false ? 0.6 : 1,
+        }}
+      >
+        {/* 折叠当前节点按钮 - 左边 */}
+        <div
+          style={{
+            position: 'absolute',
+            top: '6px',
+            left: '6px',
+            width: '20px',
+            height: '20px',
+            borderRadius: '50%',
+            background: '#f59e0b',
+            color: 'white',
+            display: 'flex',
+            alignItems: 'center',
+            justifyContent: 'center',
+            fontSize: '11px',
+            fontWeight: 'bold',
+            cursor: 'pointer',
+            transition: 'all 0.2s ease',
+            zIndex: 10,
+          }}
+          onClick={(e) => {
+            e.stopPropagation();
+            if (data.onHideSelf) {
+              data.onHideSelf();
+            }
+          }}
+          onMouseEnter={(e) => {
+            e.currentTarget.style.background = '#d97706';
+          }}
+          onMouseLeave={(e) => {
+            e.currentTarget.style.background = '#f59e0b';
+          }}
+          title="隐藏当前节点"
+        >
+          ×
+        </div>
+
+        {/* 聚焦按钮 - 右上角 */}
+        <div
+          style={{
+            position: 'absolute',
+            top: '6px',
+            right: '6px',
+            width: '20px',
+            height: '20px',
+            borderRadius: '50%',
+            background: data.isFocused ? '#10b981' : '#e5e7eb',
+            color: data.isFocused ? 'white' : '#6b7280',
+            display: 'flex',
+            alignItems: 'center',
+            justifyContent: 'center',
+            fontSize: '11px',
+            fontWeight: 'bold',
+            cursor: 'pointer',
+            transition: 'all 0.2s ease',
+            zIndex: 10,
+          }}
+          onClick={(e) => {
+            e.stopPropagation();
+            if (data.onFocus) {
+              data.onFocus();
+            }
+          }}
+          onMouseEnter={(e) => {
+            if (!data.isFocused) {
+              e.currentTarget.style.background = '#d1d5db';
+            }
+          }}
+          onMouseLeave={(e) => {
+            if (!data.isFocused) {
+              e.currentTarget.style.background = '#e5e7eb';
+            }
+          }}
+          title={data.isFocused ? '取消聚焦' : '聚焦到此节点'}
+        >
+          🎯
+        </div>
+
+        {/* 折叠/展开子节点按钮 - 右边第二个位置 */}
+        {data.hasChildren && (
+          <div
+            style={{
+              position: 'absolute',
+              top: '6px',
+              right: '30px',
+              width: '20px',
+              height: '20px',
+              borderRadius: '50%',
+              background: data.isCollapsed ? '#667eea' : '#e5e7eb',
+              color: data.isCollapsed ? 'white' : '#6b7280',
+              display: 'flex',
+              alignItems: 'center',
+              justifyContent: 'center',
+              fontSize: '11px',
+              fontWeight: 'bold',
+              cursor: 'pointer',
+              transition: 'all 0.2s ease',
+              zIndex: 10,
+            }}
+            onClick={(e) => {
+              e.stopPropagation();
+              data.onToggleCollapse();
+            }}
+            title={data.isCollapsed ? '展开子节点' : '折叠子节点'}
+          >
+            {data.isCollapsed ? '+' : '−'}
+          </div>
+        )}
+
+        {/* 卡片内容 */}
+        <div>
+          {/* 标题行 */}
+          <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'flex-start', marginBottom: '8px', paddingLeft: '24px', paddingRight: data.hasChildren ? '54px' : '28px' }}>
+            <div style={{ flex: 1 }}>
+              <div style={{ display: 'flex', alignItems: 'center', gap: '4px', marginBottom: '3px' }}>
+                <div style={{
+                  fontSize: '13px',
+                  fontWeight: data.level === 0 ? '700' : '600',
+                  color: data.level === 0 ? '#6b21a8' : '#1f2937',
+                  lineHeight: '1.3',
+                  flex: 1,
+                }}>
+                  {data.title}
+                </div>
+                {data.isSelected === false && (
+                  <div style={{
+                    fontSize: '9px',
+                    padding: '1px 4px',
+                    borderRadius: '3px',
+                    background: '#fee2e2',
+                    color: '#991b1b',
+                    fontWeight: '500',
+                    flexShrink: 0,
+                  }}>
+                    未选中
+                  </div>
+                )}
+              </div>
+            </div>
+          </div>
+
+        {/* 展开的详细信息 - 始终显示 */}
+        <div style={{ fontSize: '11px', lineHeight: 1.4 }}>
+            <div style={{ display: 'flex', gap: '4px', marginBottom: '6px', flexWrap: 'wrap' }}>
+              <span style={{
+                display: 'inline-block',
+                padding: '1px 6px',
+                borderRadius: '10px',
+                background: '#eff6ff',
+                color: '#3b82f6',
+                fontSize: '10px',
+                fontWeight: '500',
+              }}>
+                Lv.{data.level}
+              </span>
+              <span style={{
+                display: 'inline-block',
+                padding: '1px 6px',
+                borderRadius: '10px',
+                background: '#f0fdf4',
+                color: '#16a34a',
+                fontSize: '10px',
+                fontWeight: '500',
+              }}>
+                {data.score}
+              </span>
+              {data.strategy && data.strategy !== 'root' && (
+                <span style={{
+                  display: 'inline-block',
+                  padding: '1px 6px',
+                  borderRadius: '10px',
+                  background: '#fef3c7',
+                  color: '#92400e',
+                  fontSize: '10px',
+                  fontWeight: '500',
+                }}>
+                  {data.strategy}
+                </span>
+              )}
+              {(data.typeLabel || data.type_label) && (
+                <span style={{
+                  display: 'inline-block',
+                  padding: '1px 6px',
+                  borderRadius: '10px',
+                  background: '#fce7f3',
+                  color: '#9f1239',
+                  fontSize: '10px',
+                  fontWeight: '500',
+                }}>
+                  {data.typeLabel || data.type_label}
+                </span>
+              )}
+            </div>
+
+            {data.parent && (
+              <div style={{ color: '#6b7280', fontSize: '10px', marginTop: '4px', paddingTop: '4px', borderTop: '1px solid #f3f4f6' }}>
+                <strong>Parent:</strong> {data.parent}
+              </div>
+            )}
+            {data.selectedWord && (
+              <div style={{
+                marginTop: '6px',
+                paddingTop: '6px',
+                borderTop: '1px solid #f3f4f6',
+                fontSize: '10px',
+                color: '#6b7280',
+                lineHeight: '1.5',
+              }}>
+                <strong style={{ color: '#4b5563' }}>选择词:</strong>
+                <span style={{ marginLeft: '4px', color: '#3b82f6', fontWeight: '500' }}>{data.selectedWord}</span>
+                {data.seed_score !== undefined && (
+                  <div style={{ marginTop: '4px' }}>
+                    <strong style={{ color: '#4b5563' }}>种子得分:</strong>
+                    <span style={{ marginLeft: '4px', color: '#16a34a', fontWeight: '500' }}>
+                      {typeof data.seed_score === 'number' ? data.seed_score.toFixed(2) : data.seed_score}
+                    </span>
+                  </div>
+                )}
+              </div>
+            )}
+            {data.evaluationReason && (
+              <div style={{
+                marginTop: '6px',
+                paddingTop: '6px',
+                borderTop: '1px solid #f3f4f6',
+                fontSize: '10px',
+                color: '#6b7280',
+                lineHeight: '1.5',
+              }}>
+                <strong style={{ color: '#4b5563' }}>评估:</strong>
+                <div style={{ marginTop: '2px' }}>{data.evaluationReason}</div>
+              </div>
+            )}
+            {data.occurrences && data.occurrences.length > 1 && (
+              <div style={{
+                marginTop: '6px',
+                paddingTop: '6px',
+                borderTop: '1px solid #f3f4f6',
+                fontSize: '10px',
+                color: '#6b7280',
+              }}>
+                <strong style={{ color: '#4b5563' }}>演化历史 ({data.occurrences.length}次):</strong>
+                <div style={{ marginTop: '4px' }}>
+                  {data.occurrences.map((occ, idx) => (
+                    <div key={idx} style={{ marginTop: '2px', paddingLeft: '8px' }}>
+                      <span style={{ color: '#3b82f6', fontWeight: '500' }}>R{occ.round}</span>
+                      {' · '}
+                      <span>{occ.strategy}</span>
+                      {occ.score !== undefined && (
+                        <span style={{ color: '#16a34a', marginLeft: '4px' }}>
+                          ({typeof occ.score === 'number' ? occ.score.toFixed(2) : occ.score})
+                        </span>
+                      )}
+                    </div>
+                  ))}
+                </div>
+              </div>
+            )}
+            {data.hasSearchResults && (
+              <div style={{
+                marginTop: '6px',
+                paddingTop: '6px',
+                borderTop: '1px solid #f3f4f6',
+                fontSize: '10px',
+                background: '#fef3c7',
+                padding: '4px 6px',
+                borderRadius: '4px',
+                color: '#92400e',
+                fontWeight: '500',
+              }}>
+                🔍 找到 {data.postCount} 个帖子
+              </div>
+            )}
+          </div>
+        </div>
+      </div>
+      <Handle
+        type="source"
+        position={sourcePosition || Position.Right}
+        style={{ background: '#667eea', width: 8, height: 8 }}
+      />
+    </div>
+  );
+}
+
+// 笔记节点组件 - 卡片样式，带轮播图
+function NoteNode({ id, data, sourcePosition, targetPosition }) {
+  const [currentImageIndex, setCurrentImageIndex] = useState(0);
+  const expanded = true;
+  const hasImages = data.imageList && data.imageList.length > 0;
+
+  const nextImage = (e) => {
+    e.stopPropagation();
+    if (hasImages) {
+      setCurrentImageIndex((prev) => (prev + 1) % data.imageList.length);
+    }
+  };
+
+  const prevImage = (e) => {
+    e.stopPropagation();
+    if (hasImages) {
+      setCurrentImageIndex((prev) => (prev - 1 + data.imageList.length) % data.imageList.length);
+    }
+  };
+
+  return (
+    <div>
+      <Handle
+        type="target"
+        position={targetPosition || Position.Left}
+        style={{ background: '#ec4899', width: 8, height: 8 }}
+      />
+      <div
+        style={{
+          padding: '14px',
+          borderRadius: '20px',
+          border: data.isHighlighted ? '3px solid #ec4899' : '2px solid #fce7f3',
+          background: data.isHighlighted ? '#eef2ff' : 'white',
+          minWidth: '220px',
+          maxWidth: '300px',
+          boxShadow: data.isHighlighted ? '0 0 0 4px rgba(236, 72, 153, 0.25), 0 4px 16px rgba(236, 72, 153, 0.4)' : '0 4px 12px rgba(236, 72, 153, 0.15)',
+          transition: 'all 0.3s ease',
+          cursor: 'pointer',
+        }}
+      >
+        {/* 笔记标题 */}
+        <div style={{ display: 'flex', alignItems: 'flex-start', marginBottom: '8px' }}>
+          <div style={{ flex: 1 }}>
+            <div style={{
+              fontSize: '13px',
+              fontWeight: '600',
+              color: '#831843',
+              lineHeight: '1.4',
+              marginBottom: '4px',
+            }}>
+              {data.title}
+            </div>
+          </div>
+        </div>
+
+        {/* 轮播图 */}
+        {hasImages && (
+          <div style={{
+            position: 'relative',
+            marginBottom: '8px',
+            borderRadius: '12px',
+            overflow: 'hidden',
+          }}>
+            <img
+              src={data.imageList[currentImageIndex].image_url}
+              alt={\`Image \${currentImageIndex + 1}\`}
+              style={{
+                width: '100%',
+                height: '160px',
+                objectFit: 'cover',
+                display: 'block',
+              }}
+              onError={(e) => {
+                e.target.style.display = 'none';
+              }}
+            />
+            {data.imageList.length > 1 && (
+              <>
+                {/* 左右切换按钮 */}
+                <button
+                  onClick={prevImage}
+                  style={{
+                    position: 'absolute',
+                    left: '4px',
+                    top: '50%',
+                    transform: 'translateY(-50%)',
+                    background: 'rgba(0, 0, 0, 0.5)',
+                    color: 'white',
+                    border: 'none',
+                    borderRadius: '50%',
+                    width: '24px',
+                    height: '24px',
+                    cursor: 'pointer',
+                    display: 'flex',
+                    alignItems: 'center',
+                    justifyContent: 'center',
+                    fontSize: '14px',
+                  }}
+                >
+                  ‹
+                </button>
+                <button
+                  onClick={nextImage}
+                  style={{
+                    position: 'absolute',
+                    right: '4px',
+                    top: '50%',
+                    transform: 'translateY(-50%)',
+                    background: 'rgba(0, 0, 0, 0.5)',
+                    color: 'white',
+                    border: 'none',
+                    borderRadius: '50%',
+                    width: '24px',
+                    height: '24px',
+                    cursor: 'pointer',
+                    display: 'flex',
+                    alignItems: 'center',
+                    justifyContent: 'center',
+                    fontSize: '14px',
+                  }}
+                >
+                  ›
+                </button>
+                {/* 图片计数 */}
+                <div style={{
+                  position: 'absolute',
+                  bottom: '4px',
+                  right: '4px',
+                  background: 'rgba(0, 0, 0, 0.6)',
+                  color: 'white',
+                  padding: '2px 6px',
+                  borderRadius: '10px',
+                  fontSize: '10px',
+                }}>
+                  {currentImageIndex + 1}/{data.imageList.length}
+                </div>
+              </>
+            )}
+          </div>
+        )}
+
+        {/* 互动数据 */}
+        {data.interact_info && (
+          <div style={{
+            display: 'flex',
+            gap: '8px',
+            marginBottom: '8px',
+            flexWrap: 'wrap',
+            fontSize: '11px',
+            color: '#9f1239',
+          }}>
+            {data.interact_info.liked_count > 0 && (
+              <span style={{ display: 'flex', alignItems: 'center', gap: '2px' }}>
+                ❤️ {data.interact_info.liked_count}
+              </span>
+            )}
+            {data.interact_info.collected_count > 0 && (
+              <span style={{ display: 'flex', alignItems: 'center', gap: '2px' }}>
+                ⭐ {data.interact_info.collected_count}
+              </span>
+            )}
+            {data.interact_info.comment_count > 0 && (
+              <span style={{ display: 'flex', alignItems: 'center', gap: '2px' }}>
+                💬 {data.interact_info.comment_count}
+              </span>
+            )}
+            {data.interact_info.shared_count > 0 && (
+              <span style={{ display: 'flex', alignItems: 'center', gap: '2px' }}>
+                🔗 {data.interact_info.shared_count}
+              </span>
+            )}
+          </div>
+        )}
+
+        {/* 被哪些query找到 */}
+        {data.foundByQueries && data.foundByQueries.length > 0 && (
+          <div style={{
+            marginBottom: '8px',
+            padding: '6px 8px',
+            background: '#f0fdf4',
+            borderRadius: '6px',
+            fontSize: '10px',
+          }}>
+            <strong style={{ color: '#16a34a' }}>🔍 被找到:</strong>
+            <div style={{ marginTop: '4px', display: 'flex', flexWrap: 'wrap', gap: '4px' }}>
+              {data.foundByQueries.map((query, idx) => (
+                <span key={idx} style={{
+                  display: 'inline-block',
+                  padding: '2px 6px',
+                  background: '#dcfce7',
+                  color: '#166534',
+                  borderRadius: '4px',
+                  fontSize: '9px',
+                }}>
+                  {query}
+                </span>
+              ))}
+            </div>
+            {data.foundInRounds && data.foundInRounds.length > 0 && (
+              <div style={{ marginTop: '4px', color: '#6b7280' }}>
+                出现在: Round {data.foundInRounds.join(', ')}
+              </div>
+            )}
+          </div>
+        )}
+
+        {/* 标签 */}
+        {(data.matchLevel || data.score) && (
+          <div style={{ display: 'flex', gap: '6px', marginBottom: '8px', flexWrap: 'wrap' }}>
+            {data.matchLevel && (
+              <span style={{
+                display: 'inline-block',
+                padding: '2px 8px',
+                borderRadius: '12px',
+                background: '#fff1f2',
+                color: '#be123c',
+                fontSize: '10px',
+                fontWeight: '500',
+              }}>
+                {data.matchLevel}
+              </span>
+            )}
+            {data.score && (
+              <span style={{
+                display: 'inline-block',
+                padding: '2px 8px',
+                borderRadius: '12px',
+                background: '#fff7ed',
+                color: '#c2410c',
+                fontSize: '10px',
+                fontWeight: '500',
+              }}>
+                Score: {data.score}
+              </span>
+            )}
+          </div>
+        )}
+
+        {/* 描述 */}
+        {expanded && data.description && (
+          <div style={{
+            fontSize: '11px',
+            color: '#9f1239',
+            lineHeight: '1.5',
+            paddingTop: '8px',
+            borderTop: '1px solid #fbcfe8',
+          }}>
+            {data.description}
+          </div>
+        )}
+
+        {/* 评估理由 */}
+        {expanded && data.evaluationReason && (
+          <div style={{
+            fontSize: '10px',
+            color: '#831843',
+            lineHeight: '1.5',
+            paddingTop: '8px',
+            marginTop: '8px',
+            borderTop: '1px solid #fbcfe8',
+          }}>
+            <strong style={{ color: '#9f1239' }}>评估:</strong>
+            <div style={{ marginTop: '2px' }}>{data.evaluationReason}</div>
+          </div>
+        )}
+      </div>
+      <Handle
+        type="source"
+        position={sourcePosition || Position.Right}
+        style={{ background: '#ec4899', width: 8, height: 8 }}
+      />
+    </div>
+  );
+}
+
+const nodeTypes = {
+  query: QueryNode,
+  note: NoteNode,
+};
+
+// 根据 score 获取颜色
+function getScoreColor(score) {
+  if (score >= 0.7) return '#10b981'; // 绿色 - 高分
+  if (score >= 0.4) return '#f59e0b'; // 橙色 - 中分
+  return '#ef4444'; // 红色 - 低分
+}
+
+// 截断文本，保留头尾，中间显示省略号
+function truncateMiddle(text, maxLength = 20) {
+  if (!text || text.length <= maxLength) return text;
+  const headLength = Math.ceil(maxLength * 0.4);
+  const tailLength = Math.floor(maxLength * 0.4);
+  const head = text.substring(0, headLength);
+  const tail = text.substring(text.length - tailLength);
+  return \`\${head}...\${tail}\`;
+}
+
+// 根据策略获取颜色
+// 智能提取主要策略的辅助函数
+function getPrimaryStrategy(nodeData) {
+  // 优先级1: 使用 primaryStrategy 字段
+  if (nodeData.primaryStrategy) {
+    return nodeData.primaryStrategy;
+  }
+
+  // 优先级2: 从 occurrences 数组中获取最新的策略
+  if (nodeData.occurrences && Array.isArray(nodeData.occurrences) && nodeData.occurrences.length > 0) {
+    const latestOccurrence = nodeData.occurrences[nodeData.occurrences.length - 1];
+    if (latestOccurrence && latestOccurrence.strategy) {
+      return latestOccurrence.strategy;
+    }
+  }
+
+  // 优先级3: 拆分组合策略字符串，取第一个
+  if (nodeData.strategy && typeof nodeData.strategy === 'string') {
+    const strategies = nodeData.strategy.split(' + ');
+    if (strategies.length > 0 && strategies[0]) {
+      return strategies[0].trim();
+    }
+  }
+
+  // 默认返回原始strategy或未知
+  return nodeData.strategy || '未知';
+}
+
+function getStrategyColor(strategy) {
+  const strategyColors = {
+    '初始分词': '#10b981',
+    '调用sug': '#06b6d4',
+    '同义改写': '#f59e0b',
+    '加词': '#3b82f6',
+    '抽象改写': '#8b5cf6',
+    '基于部分匹配改进': '#ec4899',
+    '结果分支-抽象改写': '#a855f7',
+    '结果分支-同义改写': '#fb923c',
+    // v6.1.2.8 新增策略
+    '原始问题': '#6b21a8',
+    '来自分词': '#10b981',
+    '加词生成': '#ef4444',
+    '建议词': '#06b6d4',
+    '执行搜索': '#8b5cf6',
+    // 添加简化版本的策略映射
+    '分词': '#10b981',
+    '推荐词': '#06b6d4',
+  };
+  return strategyColors[strategy] || '#9ca3af';
+}
+
+// 树节点组件
+function TreeNode({ node, level, children, isCollapsed, onToggle, isSelected, onSelect }) {
+  const hasChildren = children && children.length > 0;
+  const score = node.data.score ? parseFloat(node.data.score) : 0;
+  const strategy = getPrimaryStrategy(node.data);  // 使用智能提取函数
+  const strategyColor = getStrategyColor(strategy);
+  const nodeActualType = node.data.nodeType || node.type; // 获取实际节点类型
+
+  // 计算字体颜色：根据分数提升幅度判断
+  let fontColor = '#374151'; // 默认颜色
+  if (node.type === 'note') {
+    fontColor = node.data.matchLevel === 'unsatisfied' ? '#ef4444' : '#374151';
+  } else if (node.data.seed_score !== undefined) {
+    const parentScore = parseFloat(node.data.seed_score);
+    const gain = score - parentScore;
+    fontColor = gain >= 0.05 ? '#16a34a' : '#ef4444';
+  } else if (node.data.isSelected === false) {
+    fontColor = '#ef4444';
+  }
+
+  return (
+    <div style={{ marginLeft: level * 12 + 'px' }}>
+      <div
+        style={{
+          padding: '6px 8px',
+          borderRadius: '4px',
+          cursor: 'pointer',
+          background: 'transparent',
+          border: isSelected ? '1px solid #3b82f6' : '1px solid transparent',
+          display: 'flex',
+          alignItems: 'center',
+          gap: '6px',
+          transition: 'all 0.2s ease',
+          position: 'relative',
+          overflow: 'visible',
+        }}
+        onMouseEnter={(e) => {
+          if (!isSelected) e.currentTarget.style.background = '#f9fafb';
+        }}
+        onMouseLeave={(e) => {
+          if (!isSelected) e.currentTarget.style.background = 'transparent';
+        }}
+      >
+        {/* 策略类型竖线 */}
+        <div style={{
+          width: '3px',
+          height: '20px',
+          background: strategyColor,
+          borderRadius: '2px',
+          flexShrink: 0,
+          position: 'relative',
+          zIndex: 1,
+        }} />
+
+        {hasChildren && (
+          <span
+            style={{
+              fontSize: '10px',
+              color: '#6b7280',
+              cursor: 'pointer',
+              width: '16px',
+              textAlign: 'center',
+              position: 'relative',
+              zIndex: 1,
+            }}
+            onClick={(e) => {
+              e.stopPropagation();
+              onToggle();
+            }}
+          >
+            {isCollapsed ? '▶' : '▼'}
+          </span>
+        )}
+        {!hasChildren && <span style={{ width: '16px', position: 'relative', zIndex: 1 }}></span>}
+
+        <div
+          style={{
+            flex: 1,
+            fontSize: '12px',
+            color: '#374151',
+            position: 'relative',
+            zIndex: 1,
+            minWidth: 0,
+            display: 'flex',
+            flexDirection: 'column',
+            gap: '4px',
+          }}
+          onClick={onSelect}
+        >
+          <div style={{
+            display: 'flex',
+            alignItems: 'center',
+            gap: '8px',
+          }}>
+            <div style={{
+              fontWeight: level === 0 ? '600' : '400',
+              maxWidth: '180px',
+              flex: 1,
+              minWidth: 0,
+              color: node.data.scoreColor || fontColor,
+            }}
+            title={node.data.title || node.id}
+            >
+              {truncateMiddle(node.data.title || node.id, 18)}
+            </div>
+
+            {/* 分数显示 - 步骤和轮次节点不显示分数 */}
+            {nodeActualType !== 'step' && nodeActualType !== 'round' && (
+              <span style={{
+                fontSize: '11px',
+                color: '#6b7280',
+                fontWeight: '500',
+                flexShrink: 0,
+                minWidth: '35px',
+                textAlign: 'right',
+              }}>
+                {score.toFixed(2)}
+              </span>
+            )}
+          </div>
+
+          {/* 分数下划线 - 步骤和轮次节点不显示 */}
+          {nodeActualType !== 'step' && nodeActualType !== 'round' && (
+            <div style={{
+              width: (score * 100) + '%',
+              height: '2px',
+              background: getScoreColor(score),
+              borderRadius: '1px',
+            }} />
+          )}
+        </div>
+      </div>
+
+      {hasChildren && !isCollapsed && (
+        <div>
+          {children}
+        </div>
+      )}
+    </div>
+  );
+}
+
+// 使用 dagre 自动布局
+function getLayoutedElements(nodes, edges, direction = 'LR') {
+  console.log('🎯 Starting layout with dagre...');
+  console.log('Input:', nodes.length, 'nodes,', edges.length, 'edges');
+
+  // 检查 dagre 是否加载
+  if (typeof window === 'undefined' || typeof window.dagre === 'undefined') {
+    console.warn('⚠️ Dagre not loaded, using fallback layout');
+    // 降级到简单布局
+    const levelGroups = {};
+    nodes.forEach(node => {
+      const level = node.data.level || 0;
+      if (!levelGroups[level]) levelGroups[level] = [];
+      levelGroups[level].push(node);
+    });
+
+    Object.entries(levelGroups).forEach(([level, nodeList]) => {
+      const x = parseInt(level) * 350;
+      nodeList.forEach((node, index) => {
+        node.position = { x, y: index * 150 };
+        node.targetPosition = 'left';
+        node.sourcePosition = 'right';
+      });
+    });
+
+    return { nodes, edges };
+  }
+
+  try {
+    const dagreGraph = new window.dagre.graphlib.Graph();
+    dagreGraph.setDefaultEdgeLabel(() => ({}));
+
+    const isHorizontal = direction === 'LR';
+    dagreGraph.setGraph({
+      rankdir: direction,
+      nodesep: 120,   // 垂直间距 - 增加以避免节点重叠
+      ranksep: 280,  // 水平间距 - 增加以容纳更宽的节点
+    });
+
+    // 添加节点 - 根据节点类型设置不同的尺寸
+    nodes.forEach((node) => {
+      let nodeWidth = 280;
+      let nodeHeight = 180;
+
+      // note 节点有轮播图，需要更大的空间
+      if (node.type === 'note') {
+        nodeWidth = 320;
+        nodeHeight = 350;  // 增加高度以容纳轮播图
+      }
+
+      dagreGraph.setNode(node.id, { width: nodeWidth, height: nodeHeight });
+    });
+
+    // 添加边
+    edges.forEach((edge) => {
+      dagreGraph.setEdge(edge.source, edge.target);
+    });
+
+    // 计算布局
+    window.dagre.layout(dagreGraph);
+    console.log('✅ Dagre layout completed');
+
+    // 更新节点位置和 handle 位置
+    nodes.forEach((node) => {
+      const nodeWithPosition = dagreGraph.node(node.id);
+
+      if (!nodeWithPosition) {
+        console.warn('Node position not found for:', node.id);
+        return;
+      }
+
+      node.targetPosition = isHorizontal ? 'left' : 'top';
+      node.sourcePosition = isHorizontal ? 'right' : 'bottom';
+
+      // 根据节点类型获取尺寸
+      let nodeWidth = 280;
+      let nodeHeight = 180;
+      if (node.type === 'note') {
+        nodeWidth = 320;
+        nodeHeight = 350;
+      }
+
+      // 将 dagre 的中心点位置转换为 React Flow 的左上角位置
+      node.position = {
+        x: nodeWithPosition.x - nodeWidth / 2,
+        y: nodeWithPosition.y - nodeHeight / 2,
+      };
+    });
+
+    console.log('✅ Layout completed, sample node:', nodes[0]);
+    return { nodes, edges };
+  } catch (error) {
+    console.error('❌ Error in dagre layout:', error);
+    console.error('Error details:', error.message, error.stack);
+
+    // 降级处理
+    console.log('Using fallback layout...');
+    const levelGroups = {};
+    nodes.forEach(node => {
+      const level = node.data.level || 0;
+      if (!levelGroups[level]) levelGroups[level] = [];
+      levelGroups[level].push(node);
+    });
+
+    Object.entries(levelGroups).forEach(([level, nodeList]) => {
+      const x = parseInt(level) * 350;
+      nodeList.forEach((node, index) => {
+        node.position = { x, y: index * 150 };
+        node.targetPosition = 'left';
+        node.sourcePosition = 'right';
+      });
+    });
+
+    return { nodes, edges };
+  }
+}
+
+function transformData(data) {
+  const nodes = [];
+  const edges = [];
+
+  const originalIdToCanvasId = {}; // 原始ID -> 画布ID的映射
+  const canvasIdToNodeData = {}; // 避免重复创建相同的节点
+
+  // 创建节点
+  Object.entries(data.nodes).forEach(([originalId, node]) => {
+    // 统一处理所有类型的节点
+    const nodeType = node.type || 'query';
+
+    // 直接使用originalId作为canvasId，避免冲突
+    const canvasId = originalId;
+
+    originalIdToCanvasId[originalId] = canvasId;
+
+    // 如果这个 canvasId 还没有创建过节点，则创建
+    if (!canvasIdToNodeData[canvasId]) {
+      canvasIdToNodeData[canvasId] = true;
+
+      // 根据节点类型创建不同的数据结构
+      if (nodeType === 'note' || nodeType === 'post') {
+        nodes.push({
+          id: canvasId,
+          originalId: originalId,
+          type: 'note',
+          data: {
+            title: node.query || node.title || '帖子',
+            matchLevel: node.match_level,
+            score: node.relevance_score ? node.relevance_score.toFixed(2) : '0.00',
+            description: node.body_text || node.desc || '',
+            isSelected: node.is_selected !== undefined ? node.is_selected : true,
+            imageList: node.image_list || [],
+            noteUrl: node.note_url || '',
+            evaluationReason: node.evaluationReason || node.evaluation_reason || '',
+            interact_info: node.interact_info || {},
+            nodeType: nodeType,
+          },
+          position: { x: 0, y: 0 },
+        });
+      } else {
+        // query, seg, q, search, root 等节点
+        let displayTitle = node.query || originalId;
+
+        nodes.push({
+          id: canvasId,
+          originalId: originalId,
+          type: 'query', // 使用 query 组件渲染所有非note节点
+          data: {
+            title: displayTitle,
+            level: node.level || 0,
+            score: node.relevance_score ? node.relevance_score.toFixed(2) : '0.00',
+            strategy: node.strategy || '',
+            parent: node.parent_query || '',
+            isSelected: node.is_selected !== undefined ? node.is_selected : true,
+            evaluationReason: node.evaluationReason || node.evaluation_reason || '',
+            nodeType: nodeType, // 传递实际节点类型用于样式
+            searchCount: node.search_count, // search 节点特有
+            totalPosts: node.total_posts, // search 节点特有
+            selectedWord: node.selected_word || '', // 加词节点特有 - 显示选择的词
+            scoreColor: node.scoreColor || null,        // SUG节点的颜色标识
+            parentQScore: node.parentQScore || 0,       // 父Q得分（用于调试）
+          },
+          position: { x: 0, y: 0 },
+        });
+      }
+    }
+  });
+
+  // 创建边 - 使用虚线样式，映射到画布ID
+  data.edges.forEach((edge, index) => {
+    const edgeColors = {
+      '初始分词': '#10b981',
+      '调用sug': '#06b6d4',
+      '同义改写': '#f59e0b',
+      '加词': '#3b82f6',
+      '抽象改写': '#8b5cf6',
+      '基于部分匹配改进': '#ec4899',
+      '结果分支-抽象改写': '#a855f7',
+      '结果分支-同义改写': '#fb923c',
+      'query_to_note': '#ec4899',
+    };
+
+    const color = edgeColors[edge.strategy] || edgeColors[edge.edge_type] || '#d1d5db';
+    const isNoteEdge = edge.edge_type === 'query_to_note';
+
+    edges.push({
+      id: \`edge-\${index}\`,
+      source: originalIdToCanvasId[edge.from], // 使用画布ID
+      target: originalIdToCanvasId[edge.to],   // 使用画布ID
+      type: 'simplebezier', // 使用简单贝塞尔曲线
+      animated: isNoteEdge,
+      style: {
+        stroke: color,
+        strokeWidth: isNoteEdge ? 2.5 : 2,
+        strokeDasharray: isNoteEdge ? '5,5' : '8,4',
+      },
+      markerEnd: {
+        type: 'arrowclosed',
+        color: color,
+        width: 20,
+        height: 20,
+      },
+    });
+  });
+
+  // 使用 dagre 自动计算布局 - 从左到右
+  return getLayoutedElements(nodes, edges, 'LR');
+}
+
+function FlowContent() {
+  // 画布使用简化数据
+  const { nodes: initialNodes, edges: initialEdges } = useMemo(() => {
+    console.log('🔍 Transforming data for canvas...');
+    const result = transformData(data);
+    console.log('✅ Canvas data:', result.nodes.length, 'nodes,', result.edges.length, 'edges');
+    return result;
+  }, []);
+
+  // 目录使用完整数据（如果存在）
+  const { nodes: fullNodes, edges: fullEdges } = useMemo(() => {
+    if (data.fullData) {
+      console.log('🔍 Transforming full data for tree directory...');
+      const result = transformData(data.fullData);
+      console.log('✅ Directory data:', result.nodes.length, 'nodes,', result.edges.length, 'edges');
+      return result;
+    }
+    // 如果没有 fullData，使用简化数据
+    return { nodes: initialNodes, edges: initialEdges };
+  }, [initialNodes, initialEdges]);
+
+  // 初始化：找出所有有子节点的节点，默认折叠（画布节点）
+  const initialCollapsedNodes = useMemo(() => {
+    const nodesWithChildren = new Set();
+    initialEdges.forEach(edge => {
+      nodesWithChildren.add(edge.source);
+    });
+    // 排除根节点（level 0），让根节点默认展开
+    const rootNode = initialNodes.find(n => n.data.level === 0);
+    if (rootNode) {
+      nodesWithChildren.delete(rootNode.id);
+    }
+    return nodesWithChildren;
+  }, [initialNodes, initialEdges]);
+
+  // 树节点的折叠状态需要在树构建后初始化
+  const [collapsedNodes, setCollapsedNodes] = useState(() => initialCollapsedNodes);
+  const [collapsedTreeNodes, setCollapsedTreeNodes] = useState(new Set());
+  const [selectedNodeId, setSelectedNodeId] = useState(null);
+  const [hiddenNodes, setHiddenNodes] = useState(new Set()); // 用户手动隐藏的节点
+  const [focusMode, setFocusMode] = useState(false); // 全局聚焦模式，默认关闭
+  const [focusedNodeId, setFocusedNodeId] = useState(null); // 单独聚焦的节点ID
+  const [sidebarWidth, setSidebarWidth] = useState(400); // 左侧目录宽度
+  const [isResizing, setIsResizing] = useState(false); // 是否正在拖拽调整宽度
+
+  // 拖拽调整侧边栏宽度的处理逻辑
+  const handleMouseDown = useCallback(() => {
+    setIsResizing(true);
+  }, []);
+
+  useEffect(() => {
+    if (!isResizing) return;
+
+    const handleMouseMove = (e) => {
+      const newWidth = e.clientX;
+      // 限制宽度范围：300px - 700px
+      if (newWidth >= 300 && newWidth <= 700) {
+        setSidebarWidth(newWidth);
+      }
+    };
+
+    const handleMouseUp = () => {
+      setIsResizing(false);
+    };
+
+    document.addEventListener('mousemove', handleMouseMove);
+    document.addEventListener('mouseup', handleMouseUp);
+
+    return () => {
+      document.removeEventListener('mousemove', handleMouseMove);
+      document.removeEventListener('mouseup', handleMouseUp);
+    };
+  }, [isResizing]);
+
+  // 获取 React Flow 实例以控制画布
+  const { setCenter, fitView } = useReactFlow();
+
+  // 获取某个节点的所有后代节点ID
+  const getDescendants = useCallback((nodeId) => {
+    const descendants = new Set();
+    const queue = [nodeId];
+
+    while (queue.length > 0) {
+      const current = queue.shift();
+      initialEdges.forEach(edge => {
+        if (edge.source === current && !descendants.has(edge.target)) {
+          descendants.add(edge.target);
+          queue.push(edge.target);
+        }
+      });
+    }
+
+    return descendants;
+  }, [initialEdges]);
+
+  // 获取直接父节点
+  const getDirectParents = useCallback((nodeId) => {
+    const parents = [];
+    initialEdges.forEach(edge => {
+      if (edge.target === nodeId) {
+        parents.push(edge.source);
+      }
+    });
+    return parents;
+  }, [initialEdges]);
+
+  // 获取直接子节点
+  const getDirectChildren = useCallback((nodeId) => {
+    const children = [];
+    initialEdges.forEach(edge => {
+      if (edge.source === nodeId) {
+        children.push(edge.target);
+      }
+    });
+    return children;
+  }, [initialEdges]);
+
+  // 切换节点折叠状态
+  const toggleNodeCollapse = useCallback((nodeId) => {
+    setCollapsedNodes(prev => {
+      const newSet = new Set(prev);
+      const descendants = getDescendants(nodeId);
+
+      if (newSet.has(nodeId)) {
+        // 展开：移除此节点，但保持其他折叠的节点
+        newSet.delete(nodeId);
+      } else {
+        // 折叠：添加此节点
+        newSet.add(nodeId);
+      }
+
+      return newSet;
+    });
+  }, [getDescendants]);
+
+  // 过滤可见的节点和边，并重新计算布局
+  const { nodes, edges } = useMemo(() => {
+    const nodesToHide = new Set();
+
+    // 判断使用哪个节点ID进行聚焦：优先使用单独聚焦的节点，否则使用全局聚焦模式的选中节点
+    const effectiveFocusNodeId = focusedNodeId || (focusMode ? selectedNodeId : null);
+
+    // 聚焦模式：只显示聚焦节点、其父节点和直接子节点
+    if (effectiveFocusNodeId) {
+      const visibleInFocus = new Set([effectiveFocusNodeId]);
+
+      // 添加所有父节点
+      initialEdges.forEach(edge => {
+        if (edge.target === effectiveFocusNodeId) {
+          visibleInFocus.add(edge.source);
+        }
+      });
+
+      // 添加所有直接子节点
+      initialEdges.forEach(edge => {
+        if (edge.source === effectiveFocusNodeId) {
+          visibleInFocus.add(edge.target);
+        }
+      });
+
+      // 隐藏不在聚焦范围内的节点
+      initialNodes.forEach(node => {
+        if (!visibleInFocus.has(node.id)) {
+          nodesToHide.add(node.id);
+        }
+      });
+    } else {
+      // 非聚焦模式：使用原有的折叠逻辑
+      // 收集所有被折叠节点的后代
+      collapsedNodes.forEach(collapsedId => {
+        const descendants = getDescendants(collapsedId);
+        descendants.forEach(id => nodesToHide.add(id));
+      });
+    }
+
+    // 添加用户手动隐藏的节点
+    hiddenNodes.forEach(id => nodesToHide.add(id));
+
+    const visibleNodes = initialNodes
+      .filter(node => !nodesToHide.has(node.id))
+      .map(node => ({
+        ...node,
+        data: {
+          ...node.data,
+          isCollapsed: collapsedNodes.has(node.id),
+          hasChildren: initialEdges.some(e => e.source === node.id),
+          onToggleCollapse: () => toggleNodeCollapse(node.id),
+          onHideSelf: () => {
+            setHiddenNodes(prev => {
+              const newSet = new Set(prev);
+              newSet.add(node.id);
+              return newSet;
+            });
+          },
+          onFocus: () => {
+            // 切换聚焦状态
+            if (focusedNodeId === node.id) {
+              setFocusedNodeId(null); // 如果已经聚焦，则取消聚焦
+            } else {
+              // 先取消之前的聚焦，然后聚焦到当前节点
+              setFocusedNodeId(node.id);
+
+              // 延迟聚焦视图到该节点
+              setTimeout(() => {
+                fitView({
+                  nodes: [{ id: node.id }],
+                  duration: 800,
+                  padding: 0.3,
+                });
+              }, 100);
+            }
+          },
+          isFocused: focusedNodeId === node.id,
+          isHighlighted: selectedNodeId === node.id,
+        }
+      }));
+
+    const visibleEdges = initialEdges.filter(
+      edge => !nodesToHide.has(edge.source) && !nodesToHide.has(edge.target)
+    );
+
+    // 重新计算布局 - 只对可见节点
+    if (typeof window !== 'undefined' && typeof window.dagre !== 'undefined') {
+      try {
+        const dagreGraph = new window.dagre.graphlib.Graph();
+        dagreGraph.setDefaultEdgeLabel(() => ({}));
+
+        dagreGraph.setGraph({
+          rankdir: 'LR',
+          nodesep: 120,   // 垂直间距 - 增加以避免节点重叠
+          ranksep: 280,  // 水平间距 - 增加以容纳更宽的节点
+        });
+
+        visibleNodes.forEach((node) => {
+          let nodeWidth = 280;
+          let nodeHeight = 180;
+
+          // note 节点有轮播图，需要更大的空间
+          if (node.type === 'note') {
+            nodeWidth = 320;
+            nodeHeight = 350;
+          }
+
+          dagreGraph.setNode(node.id, { width: nodeWidth, height: nodeHeight });
+        });
+
+        visibleEdges.forEach((edge) => {
+          dagreGraph.setEdge(edge.source, edge.target);
+        });
+
+        window.dagre.layout(dagreGraph);
+
+        visibleNodes.forEach((node) => {
+          const nodeWithPosition = dagreGraph.node(node.id);
+          if (nodeWithPosition) {
+            // 根据节点类型获取对应的尺寸
+            let nodeWidth = 280;
+            let nodeHeight = 180;
+            if (node.type === 'note') {
+              nodeWidth = 320;
+              nodeHeight = 350;
+            }
+
+            node.position = {
+              x: nodeWithPosition.x - nodeWidth / 2,
+              y: nodeWithPosition.y - nodeHeight / 2,
+            };
+            node.targetPosition = 'left';
+            node.sourcePosition = 'right';
+          }
+        });
+
+        console.log('✅ Dynamic layout recalculated for', visibleNodes.length, 'visible nodes');
+      } catch (error) {
+        console.error('❌ Error in dynamic layout:', error);
+      }
+    }
+
+    return { nodes: visibleNodes, edges: visibleEdges };
+  }, [initialNodes, initialEdges, collapsedNodes, hiddenNodes, focusMode, focusedNodeId, getDescendants, toggleNodeCollapse, selectedNodeId]);
+
+  // 构建树形结构 - 允许一个节点有多个父节点
+  // 为目录构建树（使用完整数据）
+  const buildTree = useCallback(() => {
+    // 使用完整数据构建目录树
+    const nodeMap = new Map();
+    fullNodes.forEach(node => {
+      nodeMap.set(node.id, node);
+    });
+
+    // 为每个节点创建树节点的副本（允许多次出现）
+    const createTreeNode = (nodeId, pathKey) => {
+      const node = nodeMap.get(nodeId);
+      if (!node) return null;
+
+      return {
+        ...node,
+        treeKey: pathKey, // 唯一的树路径key，用于React key
+        children: []
+      };
+    };
+
+    // 构建父子关系映射：记录每个节点的所有父节点，去重边
+    const parentToChildren = new Map();
+    const childToParents = new Map();
+
+    fullEdges.forEach(edge => {
+      // 记录父->子关系（去重：同一个父节点到同一个子节点只记录一次）
+      if (!parentToChildren.has(edge.source)) {
+        parentToChildren.set(edge.source, []);
+      }
+      const children = parentToChildren.get(edge.source);
+      if (!children.includes(edge.target)) {
+        children.push(edge.target);
+      }
+
+      // 记录子->父关系（用于判断是否有多个父节点，也去重）
+      if (!childToParents.has(edge.target)) {
+        childToParents.set(edge.target, []);
+      }
+      const parents = childToParents.get(edge.target);
+      if (!parents.includes(edge.source)) {
+        parents.push(edge.source);
+      }
+    });
+
+    // 递归构建树
+    const buildSubtree = (nodeId, pathKey, visitedInPath) => {
+      // 避免循环引用：如果当前路径中已经访问过这个节点，跳过
+      if (visitedInPath.has(nodeId)) {
+        return null;
+      }
+
+      const treeNode = createTreeNode(nodeId, pathKey);
+      if (!treeNode) return null;
+
+      const newVisitedInPath = new Set(visitedInPath);
+      newVisitedInPath.add(nodeId);
+
+      const children = parentToChildren.get(nodeId) || [];
+      treeNode.children = children
+        .map((childId, index) => buildSubtree(childId, pathKey + '-' + childId + '-' + index, newVisitedInPath))
+        .filter(child => child !== null);
+
+      return treeNode;
+    };
+
+    // 找出所有根节点（没有入边的节点）
+    const hasParent = new Set();
+    fullEdges.forEach(edge => {
+      hasParent.add(edge.target);
+    });
+
+    const roots = [];
+    fullNodes.forEach((node, index) => {
+      if (!hasParent.has(node.id)) {
+        const treeNode = buildSubtree(node.id, 'root-' + node.id + '-' + index, new Set());
+        if (treeNode) roots.push(treeNode);
+      }
+    });
+
+    return roots;
+  }, [fullNodes, fullEdges]);
+
+  const treeRoots = useMemo(() => buildTree(), [buildTree]);
+
+  // 生成树形文本结构（使用完整数据）
+  const generateTreeText = useCallback(() => {
+    const lines = [];
+
+    // 递归生成树形文本
+    const traverse = (nodes, prefix = '', isLast = true, depth = 0) => {
+      nodes.forEach((node, index) => {
+        const isLastNode = index === nodes.length - 1;
+        const nodeData = fullNodes.find(n => n.id === node.id)?.data || {};
+        const nodeType = nodeData.nodeType || node.data?.nodeType || 'unknown';
+        const title = nodeData.title || node.data?.title || node.id;
+
+        // 优先从node.data获取score，然后从nodeData获取
+        let score = null;
+        if (node.data?.score !== undefined && node.data?.score !== null) {
+          score = node.data.score;
+        } else if (node.data?.relevance_score !== undefined && node.data?.relevance_score !== null) {
+          score = node.data.relevance_score;
+        } else if (nodeData.score !== undefined && nodeData.score !== null) {
+          score = nodeData.score;
+        } else if (nodeData.relevance_score !== undefined && nodeData.relevance_score !== null) {
+          score = nodeData.relevance_score;
+        }
+
+        const strategy = nodeData.strategy || node.data?.strategy || '';
+
+        // 构建当前行 - score可能是数字或字符串，step/round节点不显示分数
+        const connector = isLastNode ? '└─' : '├─';
+        let scoreText = '';
+        if (nodeType !== 'step' && nodeType !== 'round' && score !== null && score !== undefined) {
+          // score可能已经是字符串格式（如 "0.05"），也可能是数字
+          const scoreStr = typeof score === 'number' ? score.toFixed(2) : score;
+          scoreText = \` (分数: \${scoreStr})\`;
+        }
+        const strategyText = strategy ? \` [\${strategy}]\` : '';
+
+        lines.push(\`\${prefix}\${connector} \${title}\${scoreText}\${strategyText}\`);
+
+        // 递归处理子节点
+        if (node.children && node.children.length > 0) {
+          const childPrefix = prefix + (isLastNode ? '   ' : '│  ');
+          traverse(node.children, childPrefix, isLastNode, depth + 1);
+        }
+      });
+    };
+
+    // 添加标题
+    const rootNode = fullNodes.find(n => n.data?.level === 0);
+    if (rootNode) {
+      lines.push(\`📊 查询扩展树形结构\`);
+      lines.push(\`原始问题: \${rootNode.data.title || rootNode.data.query}\`);
+      lines.push('');
+    }
+
+    traverse(treeRoots);
+
+    return lines.join('\\n');
+  }, [treeRoots, fullNodes]);
+
+  // 复制树形结构到剪贴板
+  const copyTreeToClipboard = useCallback(async () => {
+    try {
+      const treeText = generateTreeText();
+      await navigator.clipboard.writeText(treeText);
+      alert('✅ 树形结构已复制到剪贴板！');
+    } catch (err) {
+      console.error('复制失败:', err);
+      alert('❌ 复制失败，请手动复制');
+    }
+  }, [generateTreeText]);
+
+  // 初始化树节点折叠状态
+  useEffect(() => {
+    const getAllTreeKeys = (nodes) => {
+      const keys = new Set();
+      const traverse = (node) => {
+        if (node.children && node.children.length > 0) {
+          // 排除根节点
+          if (node.data.level !== 0) {
+            keys.add(node.treeKey);
+          }
+          node.children.forEach(traverse);
+        }
+      };
+      nodes.forEach(traverse);
+      return keys;
+    };
+
+    setCollapsedTreeNodes(getAllTreeKeys(treeRoots));
+  }, [treeRoots]);
+
+  // 映射完整节点ID到画布简化节点ID
+  const mapTreeNodeToCanvasNode = useCallback((treeNodeId) => {
+    // 如果是简化模式，需要映射
+    if (data.fullData) {
+      // 从完整数据中找到节点
+      const fullNode = fullNodes.find(n => n.id === treeNodeId);
+      if (!fullNode) return treeNodeId;
+
+      // 根据节点类型和文本找到画布上的简化节点
+      const nodeText = fullNode.data.title || fullNode.data.query;
+      const nodeType = fullNode.data.nodeType || fullNode.type;
+
+      // Query类节点：找 query_xxx
+      if (['q', 'seg', 'sug', 'add_word', 'query'].includes(nodeType)) {
+        const canvasNode = initialNodes.find(n =>
+          (n.data.title === nodeText || n.data.query === nodeText) &&
+          ['query'].includes(n.type)
+        );
+        return canvasNode ? canvasNode.id : treeNodeId;
+      }
+
+      // Post节点：按note_id查找
+      if (nodeType === 'post' || nodeType === 'note') {
+        const noteId = fullNode.data.note_id;
+        if (noteId) {
+          const canvasNode = initialNodes.find(n => n.data.note_id === noteId);
+          return canvasNode ? canvasNode.id : treeNodeId;
+        }
+      }
+
+      // 其他节点类型（Round/Step等）：直接返回
+      return treeNodeId;
+    }
+
+    // 非简化模式，直接返回
+    return treeNodeId;
+  }, [data.fullData, fullNodes, initialNodes]);
+
+  const renderTree = useCallback((treeNodes, level = 0) => {
+    return treeNodes.map(node => {
+      // 使用 treeKey 来区分树中的不同实例
+      const isCollapsed = collapsedTreeNodes.has(node.treeKey);
+      const isSelected = selectedNodeId === node.id;
+
+      return (
+        <TreeNode
+          key={node.treeKey}
+          node={node}
+          level={level}
+          isCollapsed={isCollapsed}
+          isSelected={isSelected}
+          onToggle={() => {
+            setCollapsedTreeNodes(prev => {
+              const newSet = new Set(prev);
+              if (newSet.has(node.treeKey)) {
+                newSet.delete(node.treeKey);
+              } else {
+                newSet.add(node.treeKey);
+              }
+              return newSet;
+            });
+          }}
+          onSelect={() => {
+            // 将目录节点ID映射到画布节点ID
+            const treeNodeId = node.id;
+            const canvasNodeId = mapTreeNodeToCanvasNode(treeNodeId);
+
+            // 检查画布上是否存在这个节点
+            const canvasNodeExists = initialNodes.some(n => n.id === canvasNodeId);
+            if (!canvasNodeExists) {
+              console.warn(\`节点 \${canvasNodeId} 在画布上不存在（可能被简化了）\`);
+              return;
+            }
+
+            const nodeId = canvasNodeId;
+
+            // 展开所有祖先节点
+            const ancestorIds = [nodeId];
+            const findAncestors = (id) => {
+              initialEdges.forEach(edge => {
+                if (edge.target === id && !ancestorIds.includes(edge.source)) {
+                  ancestorIds.push(edge.source);
+                  findAncestors(edge.source);
+                }
+              });
+            };
+            findAncestors(nodeId);
+
+            // 如果节点或其祖先被隐藏，先恢复它们
+            setHiddenNodes(prev => {
+              const newSet = new Set(prev);
+              ancestorIds.forEach(id => newSet.delete(id));
+              return newSet;
+            });
+
+            setSelectedNodeId(nodeId);
+
+            // 获取选中节点的直接子节点
+            const childrenIds = [];
+            initialEdges.forEach(edge => {
+              if (edge.source === nodeId) {
+                childrenIds.push(edge.target);
+              }
+            });
+
+            setCollapsedNodes(prev => {
+              const newSet = new Set(prev);
+              // 展开所有祖先节点
+              ancestorIds.forEach(id => newSet.delete(id));
+              // 展开选中节点本身
+              newSet.delete(nodeId);
+              // 展开选中节点的直接子节点
+              childrenIds.forEach(id => newSet.delete(id));
+              return newSet;
+            });
+
+            // 延迟聚焦，等待节点展开和布局重新计算
+            setTimeout(() => {
+              fitView({
+                nodes: [{ id: nodeId }],
+                duration: 800,
+                padding: 0.3,
+              });
+            }, 300);
+          }}
+        >
+          {node.children && node.children.length > 0 && renderTree(node.children, level + 1)}
+        </TreeNode>
+      );
+    });
+  }, [collapsedTreeNodes, selectedNodeId, nodes, setCenter, initialEdges, setCollapsedNodes, fitView, mapTreeNodeToCanvasNode, initialNodes, setHiddenNodes]);
+
+  console.log('📊 Rendering with', nodes.length, 'visible nodes and', edges.length, 'visible edges');
+
+  if (nodes.length === 0) {
+    return (
+      <div style={{ padding: 50, color: 'red', fontSize: 20 }}>
+        ERROR: No nodes to display!
+      </div>
+    );
+  }
+
+  return (
+    <div style={{ width: '100vw', height: '100vh', background: '#f9fafb', display: 'flex', flexDirection: 'column' }}>
+      {/* 顶部面包屑导航栏 */}
+      <div style={{
+        minHeight: '48px',
+        maxHeight: '120px',
+        background: 'white',
+        borderBottom: '1px solid #e5e7eb',
+        display: 'flex',
+        alignItems: 'flex-start',
+        padding: '12px 24px',
+        zIndex: 1000,
+        boxShadow: '0 1px 3px rgba(0, 0, 0, 0.05)',
+        flexShrink: 0,
+        overflowY: 'auto',
+      }}>
+        <div style={{ width: '100%' }}>
+          {selectedNodeId ? (
+            <div style={{ fontSize: '12px', color: '#6b7280' }}>
+              {/* 面包屑导航 - 显示所有路径 */}
+              {(() => {
+                const selectedNode = nodes.find(n => n.id === selectedNodeId);
+                if (!selectedNode) return null;
+
+                // 找到所有从根节点到当前节点的路径
+                const findAllPaths = (targetId) => {
+                  const paths = [];
+
+                  const buildPath = (nodeId, currentPath) => {
+                    const node = initialNodes.find(n => n.id === nodeId);
+                    if (!node) return;
+
+                    const newPath = [node, ...currentPath];
+
+                    // 找到所有父节点
+                    const parents = initialEdges.filter(e => e.target === nodeId).map(e => e.source);
+
+                    if (parents.length === 0) {
+                      // 到达根节点
+                      paths.push(newPath);
+                    } else {
+                      // 递归处理所有父节点
+                      parents.forEach(parentId => {
+                        buildPath(parentId, newPath);
+                      });
+                    }
+                  };
+
+                  buildPath(targetId, []);
+                  return paths;
+                };
+
+                const allPaths = findAllPaths(selectedNodeId);
+
+                // 去重：将路径转换为字符串进行比较
+                const uniquePaths = [];
+                const pathStrings = new Set();
+                allPaths.forEach(path => {
+                  const pathString = path.map(n => n.id).join('->');
+                  if (!pathStrings.has(pathString)) {
+                    pathStrings.add(pathString);
+                    uniquePaths.push(path);
+                  }
+                });
+
+                return (
+                  <div style={{ display: 'flex', flexDirection: 'column', gap: '6px' }}>
+                    {uniquePaths.map((path, pathIndex) => (
+                      <div key={pathIndex} style={{ display: 'flex', alignItems: 'center', gap: '6px', flexWrap: 'wrap' }}>
+                        {pathIndex > 0 && <span style={{ color: '#d1d5db', marginRight: '4px' }}>或</span>}
+                        {path.map((node, index) => {
+                          // 获取节点的 score、strategy 和 isSelected
+                          const nodeScore = node.data.score ? parseFloat(node.data.score) : 0;
+                          const nodeStrategy = getPrimaryStrategy(node.data);  // 使用智能提取函数
+                          const strategyColor = getStrategyColor(nodeStrategy);
+                          const nodeIsSelected = node.type === 'note' ? node.data.matchLevel !== 'unsatisfied' : node.data.isSelected !== false;
+                          const nodeActualType = node.data.nodeType || node.type; // 获取实际节点类型
+
+                          // 计算路径节点字体颜色：根据分数提升幅度判断
+                          let pathFontColor = '#374151'; // 默认颜色
+                          if (node.type === 'note') {
+                            pathFontColor = node.data.matchLevel === 'unsatisfied' ? '#ef4444' : '#374151';
+                          } else if (node.data.seed_score !== undefined) {
+                            const parentScore = parseFloat(node.data.seed_score);
+                            const gain = nodeScore - parentScore;
+                            pathFontColor = gain >= 0.05 ? '#16a34a' : '#ef4444';
+                          } else if (index > 0) {
+                            const prevNode = path[index - 1];
+                            const prevScore = prevNode.data.score ? parseFloat(prevNode.data.score) : 0;
+                            const gain = nodeScore - prevScore;
+                            pathFontColor = gain >= 0.05 ? '#16a34a' : '#ef4444';
+                          } else if (node.data.isSelected === false) {
+                            pathFontColor = '#ef4444';
+                          }
+
+                          return (
+                          <React.Fragment key={node.id + '-' + index}>
+                            <span
+                              onClick={() => {
+                                const nodeId = node.id;
+
+                                // 找到所有祖先节点
+                                const ancestorIds = [nodeId];
+                                const findAncestors = (id) => {
+                                  initialEdges.forEach(edge => {
+                                    if (edge.target === id && !ancestorIds.includes(edge.source)) {
+                                      ancestorIds.push(edge.source);
+                                      findAncestors(edge.source);
+                                    }
+                                  });
+                                };
+                                findAncestors(nodeId);
+
+                                // 如果节点或其祖先被隐藏，先恢复它们
+                                setHiddenNodes(prev => {
+                                  const newSet = new Set(prev);
+                                  ancestorIds.forEach(id => newSet.delete(id));
+                                  return newSet;
+                                });
+
+                                // 展开目录树中到达该节点的路径
+                                // 需要找到所有包含该节点的树路径的 treeKey，并展开它们的父节点
+                                setCollapsedTreeNodes(prev => {
+                                  const newSet = new Set(prev);
+                                  // 清空所有折叠状态，让目录树完全展开到选中节点
+                                  // 这样可以确保选中节点在目录中可见
+                                  return new Set();
+                                });
+
+                                setSelectedNodeId(nodeId);
+                                setTimeout(() => {
+                                  fitView({
+                                    nodes: [{ id: nodeId }],
+                                    duration: 800,
+                                    padding: 0.3,
+                                  });
+                                }, 100);
+                              }}
+                              style={{
+                                padding: '6px 8px',
+                                borderRadius: '4px',
+                                background: 'white',
+                                border: index === path.length - 1 ? '2px solid #3b82f6' : '1px solid #d1d5db',
+                                color: '#374151',
+                                fontWeight: index === path.length - 1 ? '600' : '400',
+                                width: '180px',
+                                cursor: 'pointer',
+                                transition: 'all 0.2s ease',
+                                position: 'relative',
+                                display: 'inline-flex',
+                                flexDirection: 'column',
+                                gap: '4px',
+                              }}
+                              onMouseEnter={(e) => {
+                                e.currentTarget.style.opacity = '0.8';
+                              }}
+                              onMouseLeave={(e) => {
+                                e.currentTarget.style.opacity = '1';
+                              }}
+                              title={\`\${node.data.title || node.id} (Score: \${nodeScore.toFixed(2)}, Strategy: \${nodeStrategy}, Selected: \${nodeIsSelected})\`}
+                            >
+                              {/* 上半部分：竖线 + 图标 + 文字 + 分数 */}
+                              <div style={{
+                                display: 'flex',
+                                alignItems: 'center',
+                                gap: '6px',
+                              }}>
+                                {/* 策略类型竖线 */}
+                                <div style={{
+                                  width: '3px',
+                                  height: '16px',
+                                  background: strategyColor,
+                                  borderRadius: '2px',
+                                  flexShrink: 0,
+                                }} />
+
+                                {/* 节点文字 */}
+                                <span style={{
+                                  flex: 1,
+                                  fontSize: '12px',
+                                  color: pathFontColor,
+                                }}>
+                                  {truncateMiddle(node.data.title || node.id, 18)}
+                                </span>
+
+                                {/* 分数显示 - 步骤和轮次节点不显示分数 */}
+                                {nodeActualType !== 'step' && nodeActualType !== 'round' && (
+                                  <span style={{
+                                    fontSize: '10px',
+                                    color: '#6b7280',
+                                    fontWeight: '500',
+                                    flexShrink: 0,
+                                    minWidth: '35px',
+                                    textAlign: 'right',
+                                  }}>
+                                    {nodeScore.toFixed(2)}
+                                  </span>
+                                )}
+                              </div>
+
+                              {/* 分数下划线 - 步骤和轮次节点不显示 */}
+                              {nodeActualType !== 'step' && nodeActualType !== 'round' && (
+                                <div style={{
+                                  width: (nodeScore * 100) + '%',
+                                  height: '2px',
+                                  background: getScoreColor(nodeScore),
+                                  borderRadius: '1px',
+                                  marginLeft: '9px',
+                                }} />
+                              )}
+                            </span>
+                            {index < path.length - 1 && <span style={{ color: '#9ca3af' }}>›</span>}
+                          </React.Fragment>
+                        )})}
+                      </div>
+                    ))}
+                  </div>
+                );
+              })()}
+            </div>
+          ) : (
+            <div style={{ fontSize: '13px', color: '#9ca3af', textAlign: 'center' }}>
+              选择一个节点查看路径
+            </div>
+          )}
+        </div>
+      </div>
+
+      {/* 主内容区：目录 + 画布 */}
+      <div style={{
+        display: 'flex',
+        flex: 1,
+        overflow: 'hidden',
+        cursor: isResizing ? 'col-resize' : 'default',
+        userSelect: isResizing ? 'none' : 'auto',
+      }}>
+        {/* 左侧目录树 */}
+        <div style={{
+          width: \`\${sidebarWidth}px\`,
+          background: 'white',
+          borderRight: '1px solid #e5e7eb',
+          display: 'flex',
+          flexDirection: 'column',
+          flexShrink: 0,
+        }}>
+          <div style={{
+            padding: '12px 16px',
+            borderBottom: '1px solid #e5e7eb',
+            display: 'flex',
+            justifyContent: 'space-between',
+            alignItems: 'center',
+          }}>
+            <span style={{
+              fontWeight: '600',
+              fontSize: '14px',
+              color: '#111827',
+            }}>
+              节点目录
+            </span>
+            <div style={{ display: 'flex', gap: '6px' }}>
+              <button
+                onClick={() => {
+                  setCollapsedTreeNodes(new Set());
+                }}
+                style={{
+                  fontSize: '11px',
+                  padding: '4px 8px',
+                  borderRadius: '4px',
+                  border: '1px solid #d1d5db',
+                  background: 'white',
+                  color: '#6b7280',
+                  cursor: 'pointer',
+                  fontWeight: '500',
+                }}
+                title="展开全部节点"
+              >
+                全部展开
+              </button>
+              <button
+                onClick={() => {
+                  const getAllTreeKeys = (nodes) => {
+                    const keys = new Set();
+                    const traverse = (node) => {
+                      if (node.children && node.children.length > 0) {
+                        keys.add(node.treeKey);
+                        node.children.forEach(traverse);
+                      }
+                    };
+                    nodes.forEach(traverse);
+                    return keys;
+                  };
+                  setCollapsedTreeNodes(getAllTreeKeys(treeRoots));
+                }}
+                style={{
+                  fontSize: '11px',
+                  padding: '4px 8px',
+                  borderRadius: '4px',
+                  border: '1px solid #d1d5db',
+                  background: 'white',
+                  color: '#6b7280',
+                  cursor: 'pointer',
+                  fontWeight: '500',
+                }}
+                title="折叠全部节点"
+              >
+                全部折叠
+              </button>
+              <button
+                onClick={copyTreeToClipboard}
+                style={{
+                  fontSize: '11px',
+                  padding: '4px 8px',
+                  borderRadius: '4px',
+                  border: '1px solid #3b82f6',
+                  background: '#3b82f6',
+                  color: 'white',
+                  cursor: 'pointer',
+                  fontWeight: '500',
+                  transition: 'all 0.2s',
+                }}
+                onMouseEnter={(e) => e.currentTarget.style.background = '#2563eb'}
+                onMouseLeave={(e) => e.currentTarget.style.background = '#3b82f6'}
+                title="复制树形结构为文本格式"
+              >
+                📋 复制树形结构
+              </button>
+            </div>
+          </div>
+          <div style={{
+            flex: 1,
+            overflowX: 'auto',
+            overflowY: 'auto',
+            padding: '8px',
+          }}>
+            <div style={{ minWidth: 'fit-content' }}>
+              {renderTree(treeRoots)}
+            </div>
+          </div>
+        </div>
+
+        {/* 可拖拽的分隔条 */}
+        <div
+          onMouseDown={handleMouseDown}
+          style={{
+            width: '4px',
+            cursor: 'col-resize',
+            background: isResizing ? '#3b82f6' : 'transparent',
+            transition: isResizing ? 'none' : 'background 0.2s',
+            flexShrink: 0,
+            position: 'relative',
+          }}
+          onMouseEnter={(e) => e.currentTarget.style.background = '#e5e7eb'}
+          onMouseLeave={(e) => {
+            if (!isResizing) e.currentTarget.style.background = 'transparent';
+          }}
+        >
+          {/* 拖拽提示线 */}
+          <div style={{
+            position: 'absolute',
+            top: '50%',
+            left: '50%',
+            transform: 'translate(-50%, -50%)',
+            width: '1px',
+            height: '40px',
+            background: '#9ca3af',
+            opacity: isResizing ? 1 : 0.3,
+          }} />
+        </div>
+
+        {/* 画布区域 */}
+        <div style={{ flex: 1, position: 'relative' }}>
+
+          {/* 右侧图例 */}
+          <div style={{
+            position: 'absolute',
+            top: '20px',
+            right: '20px',
+            background: 'white',
+            padding: '16px',
+            borderRadius: '12px',
+            boxShadow: '0 4px 12px rgba(0, 0, 0, 0.08)',
+            zIndex: 1000,
+            maxWidth: '260px',
+            border: '1px solid #e5e7eb',
+          }}>
+        <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: '12px' }}>
+          <h3 style={{ fontSize: '14px', fontWeight: '600', color: '#111827', margin: 0 }}>图例</h3>
+          <button
+            onClick={() => setFocusMode(!focusMode)}
+            style={{
+              fontSize: '11px',
+              padding: '4px 8px',
+              borderRadius: '4px',
+              border: '1px solid',
+              borderColor: focusMode ? '#3b82f6' : '#d1d5db',
+              background: focusMode ? '#3b82f6' : 'white',
+              color: focusMode ? 'white' : '#6b7280',
+              cursor: 'pointer',
+              fontWeight: '500',
+            }}
+            title={focusMode ? '关闭聚焦模式' : '开启聚焦模式'}
+          >
+            {focusMode ? '🎯 聚焦' : '📊 全图'}
+          </button>
+        </div>
+
+        <div style={{ fontSize: '12px' }}>
+          {/* 画布节点展开/折叠控制 */}
+          <div style={{ marginBottom: '12px', paddingBottom: '12px', borderBottom: '1px solid #f3f4f6' }}>
+            <div style={{ fontSize: '12px', fontWeight: '500', marginBottom: '8px', color: '#374151' }}>节点控制</div>
+            <div style={{ display: 'flex', gap: '6px' }}>
+              <button
+                onClick={() => {
+                  setCollapsedNodes(new Set());
+                }}
+                style={{
+                  fontSize: '11px',
+                  padding: '4px 8px',
+                  borderRadius: '4px',
+                  border: '1px solid #d1d5db',
+                  background: 'white',
+                  color: '#6b7280',
+                  cursor: 'pointer',
+                  fontWeight: '500',
+                  flex: 1,
+                }}
+                title="展开画布中所有节点的子节点"
+              >
+                全部展开
+              </button>
+              <button
+                onClick={() => {
+                  const allNodeIds = new Set(initialNodes.map(n => n.id));
+                  setCollapsedNodes(allNodeIds);
+                }}
+                style={{
+                  fontSize: '11px',
+                  padding: '4px 8px',
+                  borderRadius: '4px',
+                  border: '1px solid #d1d5db',
+                  background: 'white',
+                  color: '#6b7280',
+                  cursor: 'pointer',
+                  fontWeight: '500',
+                  flex: 1,
+                }}
+                title="折叠画布中所有节点的子节点"
+              >
+                全部折叠
+              </button>
+            </div>
+          </div>
+
+          <div style={{ paddingTop: '12px', borderTop: '1px solid #f3f4f6' }}>
+            <div style={{ fontSize: '12px', fontWeight: '500', marginBottom: '8px', color: '#374151' }}>策略类型</div>
+            <div style={{ display: 'flex', alignItems: 'center', margin: '6px 0' }}>
+              <div style={{ width: '20px', height: '2px', marginRight: '8px', background: '#10b981', opacity: 0.7 }}></div>
+              <span style={{ color: '#6b7280', fontSize: '11px' }}>初始分词</span>
+            </div>
+            <div style={{ display: 'flex', alignItems: 'center', margin: '6px 0' }}>
+              <div style={{ width: '20px', height: '2px', marginRight: '8px', background: '#06b6d4', opacity: 0.7 }}></div>
+              <span style={{ color: '#6b7280', fontSize: '11px' }}>调用sug</span>
+            </div>
+            <div style={{ display: 'flex', alignItems: 'center', margin: '6px 0' }}>
+              <div style={{ width: '20px', height: '2px', marginRight: '8px', background: '#f59e0b', opacity: 0.7 }}></div>
+              <span style={{ color: '#6b7280', fontSize: '11px' }}>同义改写</span>
+            </div>
+            <div style={{ display: 'flex', alignItems: 'center', margin: '6px 0' }}>
+              <div style={{ width: '20px', height: '2px', marginRight: '8px', background: '#3b82f6', opacity: 0.7 }}></div>
+              <span style={{ color: '#6b7280', fontSize: '11px' }}>加词</span>
+            </div>
+            <div style={{ display: 'flex', alignItems: 'center', margin: '6px 0' }}>
+              <div style={{ width: '20px', height: '2px', marginRight: '8px', background: '#8b5cf6', opacity: 0.7 }}></div>
+              <span style={{ color: '#6b7280', fontSize: '11px' }}>抽象改写</span>
+            </div>
+            <div style={{ display: 'flex', alignItems: 'center', margin: '6px 0' }}>
+              <div style={{ width: '20px', height: '2px', marginRight: '8px', background: '#ec4899', opacity: 0.7 }}></div>
+              <span style={{ color: '#6b7280', fontSize: '11px' }}>基于部分匹配改进</span>
+            </div>
+            <div style={{ display: 'flex', alignItems: 'center', margin: '6px 0' }}>
+              <div style={{ width: '20px', height: '2px', marginRight: '8px', background: '#a855f7', opacity: 0.7 }}></div>
+              <span style={{ color: '#6b7280', fontSize: '11px' }}>结果分支-抽象改写</span>
+            </div>
+            <div style={{ display: 'flex', alignItems: 'center', margin: '6px 0' }}>
+              <div style={{ width: '20px', height: '2px', marginRight: '8px', background: '#fb923c', opacity: 0.7 }}></div>
+              <span style={{ color: '#6b7280', fontSize: '11px' }}>结果分支-同义改写</span>
+            </div>
+          </div>
+
+          <div style={{
+            marginTop: '12px',
+            paddingTop: '12px',
+            borderTop: '1px solid #f3f4f6',
+            fontSize: '11px',
+            color: '#9ca3af',
+            lineHeight: '1.5',
+          }}>
+            💡 点击节点左上角 × 隐藏节点
+          </div>
+
+          {/* 隐藏节点列表 - 在图例内部 */}
+          {hiddenNodes.size > 0 && (
+            <div style={{
+              marginTop: '12px',
+              paddingTop: '12px',
+              borderTop: '1px solid #f3f4f6',
+            }}>
+              <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: '8px' }}>
+                <h4 style={{ fontSize: '12px', fontWeight: '600', color: '#111827' }}>已隐藏节点</h4>
+                <button
+                  onClick={() => setHiddenNodes(new Set())}
+                  style={{
+                    fontSize: '10px',
+                    color: '#3b82f6',
+                    background: 'none',
+                    border: 'none',
+                    cursor: 'pointer',
+                    textDecoration: 'underline',
+                  }}
+                >
+                  全部恢复
+                </button>
+              </div>
+              <div style={{ fontSize: '12px', maxHeight: '200px', overflow: 'auto' }}>
+                {Array.from(hiddenNodes).map(nodeId => {
+                  const node = initialNodes.find(n => n.id === nodeId);
+                  if (!node) return null;
+                  return (
+                    <div
+                      key={nodeId}
+                      style={{
+                        display: 'flex',
+                        justifyContent: 'space-between',
+                        alignItems: 'center',
+                        padding: '6px 8px',
+                        margin: '4px 0',
+                        background: '#f9fafb',
+                        borderRadius: '6px',
+                        fontSize: '11px',
+                      }}
+                    >
+                      <span
+                        style={{
+                          flex: 1,
+                          overflow: 'hidden',
+                          textOverflow: 'ellipsis',
+                          whiteSpace: 'nowrap',
+                          color: '#374151',
+                        }}
+                        title={node.data.title || nodeId}
+                      >
+                        {node.data.title || nodeId}
+                      </span>
+                      <button
+                        onClick={() => {
+                          setHiddenNodes(prev => {
+                            const newSet = new Set(prev);
+                            newSet.delete(nodeId);
+                            return newSet;
+                          });
+                        }}
+                        style={{
+                          marginLeft: '8px',
+                          fontSize: '10px',
+                          color: '#10b981',
+                          background: 'none',
+                          border: 'none',
+                          cursor: 'pointer',
+                          flexShrink: 0,
+                        }}
+                      >
+                        恢复
+                      </button>
+                    </div>
+                  );
+                })}
+              </div>
+            </div>
+          )}
+          </div>
+          </div>
+
+          {/* React Flow 画布 */}
+          <ReactFlow
+            nodes={nodes}
+            edges={edges}
+            nodeTypes={nodeTypes}
+            fitView
+            fitViewOptions={{ padding: 0.2, duration: 500 }}
+            minZoom={0.1}
+            maxZoom={1.5}
+            nodesDraggable={true}
+            nodesConnectable={false}
+            elementsSelectable={true}
+            defaultEdgeOptions={{
+              type: 'smoothstep',
+            }}
+            proOptions={{ hideAttribution: true }}
+            onNodeClick={(event, clickedNode) => {
+              setSelectedNodeId(clickedNode.id);
+            }}
+          >
+            <Controls style={{ bottom: '20px', left: 'auto', right: '20px' }} />
+            <Background variant="dots" gap={20} size={1} color="#e5e7eb" />
+          </ReactFlow>
+        </div>
+      </div>
+    </div>
+  );
+}
+
+function App() {
+  return (
+    <ReactFlowProvider>
+      <FlowContent />
+    </ReactFlowProvider>
+  );
+}
+
+const root = createRoot(document.getElementById('root'));
+root.render(<App />);
+`;
+
+fs.writeFileSync(reactComponentPath, reactComponent);
+
+// 使用 esbuild 打包
+console.log('🎨 Building modern visualization...');
+
+build({
+  entryPoints: [reactComponentPath],
+  bundle: true,
+  outfile: path.join(__dirname, 'bundle_v2.js'),
+  format: 'iife',
+  loader: {
+    '.css': 'css',
+  },
+  minify: false,
+  sourcemap: 'inline',
+  // 强制所有 React 引用指向同一个位置，避免多副本
+  alias: {
+    'react': path.join(__dirname, 'node_modules/react'),
+    'react-dom': path.join(__dirname, 'node_modules/react-dom'),
+    'react/jsx-runtime': path.join(__dirname, 'node_modules/react/jsx-runtime'),
+    'react/jsx-dev-runtime': path.join(__dirname, 'node_modules/react/jsx-dev-runtime'),
+  },
+}).then(() => {
+  // 读取打包后的 JS
+  const bundleJs = fs.readFileSync(path.join(__dirname, 'bundle_v2.js'), 'utf-8');
+
+  // 读取 CSS
+  const cssPath = path.join(__dirname, 'node_modules/@xyflow/react/dist/style.css');
+  const css = fs.readFileSync(cssPath, 'utf-8');
+
+  // 生成最终 HTML
+  const html = `<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>查询图可视化</title>
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600&display=swap" rel="stylesheet">
+    <script src="https://unpkg.com/dagre@0.8.5/dist/dagre.min.js"></script>
+    <script>
+      // 过滤特定的 React 警告
+      const originalError = console.error;
+      console.error = (...args) => {
+        if (typeof args[0] === 'string' && args[0].includes('Each child in a list should have a unique "key" prop')) {
+          return;
+        }
+        originalError.apply(console, args);
+      };
+    </script>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        body {
+            font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
+            overflow: hidden;
+            -webkit-font-smoothing: antialiased;
+            -moz-osx-font-smoothing: grayscale;
+        }
+        #root {
+            width: 100vw;
+            height: 100vh;
+        }
+        ${css}
+
+        /* 自定义样式覆盖 */
+        .react-flow__edge-path {
+            stroke-linecap: round;
+        }
+        .react-flow__controls {
+            box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
+            border: 1px solid #e5e7eb;
+            border-radius: 8px;
+        }
+        .react-flow__controls-button {
+            border: none;
+            border-bottom: 1px solid #e5e7eb;
+        }
+        .react-flow__controls-button:hover {
+            background: #f9fafb;
+        }
+    </style>
+</head>
+<body>
+    <div id="root"></div>
+    <script>${bundleJs}</script>
+</body>
+</html>`;
+
+  // 写入输出文件
+  fs.writeFileSync(outputFile, html);
+
+  // 清理临时文件
+  fs.unlinkSync(reactComponentPath);
+  fs.unlinkSync(path.join(__dirname, 'bundle_v2.js'));
+
+  console.log('✅ Visualization generated: ' + outputFile);
+  console.log('📊 Nodes: ' + Object.keys(data.nodes).length);
+  console.log('🔗 Edges: ' + data.edges.length);
+}).catch(error => {
+  console.error('❌ Build error:', error);
+  process.exit(1);
+});