how/script/data_processing/analyze_node_origin_v4.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
节点来源分析脚本 V4

采用两步法：
1. 第一步（筛选）：筛选可能的来源特征
2. 第二步（评估）：对筛选出的特征进行可能性评估

输入：post_graph 目录中的帖子图谱文件
输出：节点来源分析结果
"""

import asyncio
import json
from pathlib import Path
from typing import Dict, List
import sys

# 添加项目根目录到路径
project_root = Path(__file__).parent.parent.parent
sys.path.insert(0, str(project_root))

from agents import Agent, Runner, ModelSettings, trace
from agents.tracing.create import custom_span
from lib.client import get_model
from lib.my_trace import set_trace_smith as set_trace
from script.data_processing.path_config import PathConfig

# 模型配置
MODEL_NAME = "google/gemini-3-pro-preview"
# MODEL_NAME = 'deepseek/deepseek-v3.2'
# MODEL_NAME = 'anthropic/claude-sonnet-4.5'

# 第一步筛选 Agent
filter_agent = Agent(
    name="Feature Filter",
    model=get_model(MODEL_NAME),
    model_settings=ModelSettings(
        temperature=0.0,
        max_tokens=16384,
    ),
    tools=[],
)

# 第二步评估 Agent
evaluate_agent = Agent(
    name="Feature Evaluator",
    model=get_model(MODEL_NAME),
    model_settings=ModelSettings(
        temperature=0.0,
        max_tokens=32768,
    ),
    tools=[],
)


# ===== 数据提取函数 =====

def get_post_graph_files(config: PathConfig) -> List[Path]:
    """获取所有帖子图谱文件"""
    post_graph_dir = config.intermediate_dir / "post_graph"
    return sorted(post_graph_dir.glob("*_帖子图谱.json"))


def load_post_graph(file_path: Path) -> Dict:
    """加载帖子图谱"""
    with open(file_path, "r", encoding="utf-8") as f:
        return json.load(f)


def extract_tags_from_post_graph(post_graph: Dict) -> List[Dict]:
    """从帖子图谱中提取标签节点"""
    tags = []
    for node_id, node in post_graph.get("nodes", {}).items():
        if node.get("type") == "标签" and node.get("domain") == "帖子":
            tags.append({
                "id": node_id,
                "name": node.get("name", ""),
                "dimension": node.get("dimension", ""),
            })
    return tags


def prepare_analyze_input(post_graph: Dict, target_name: str = None) -> Dict:
    """准备分析输入数据"""
    tags = extract_tags_from_post_graph(post_graph)

    if not tags:
        raise ValueError("帖子图谱中没有找到标签节点")

    # 确定目标节点
    if target_name:
        target_tag = next((t for t in tags if t["name"] == target_name), None)
        if not target_tag:
            raise ValueError(f"未找到目标节点: {target_name}")
    else:
        key_point_tags = [t for t in tags if t["dimension"] == "关键点"]
        if not key_point_tags:
            raise ValueError("没有找到关键点标签")
        target_tag = key_point_tags[0]

    # 候选节点筛选：灵感点/目的点的候选集排除关键点
    target_dimension = target_tag["dimension"]
    candidate_tags = []
    for t in tags:
        if t["name"] == target_tag["name"]:
            continue
        if target_dimension in ["灵感点", "目的点"] and t["dimension"] == "关键点":
            continue
        candidate_tags.append(t)

    return {
        "目标特征": {
            "特征名称": target_tag["name"],
            "特征类型": target_tag["dimension"]
        },
        "候选特征": [
            {
                "特征名称": t["name"],
                "特征类型": t["dimension"]
            }
            for t in candidate_tags
        ]
    }


# ===== Prompt 构建 =====

def build_filter_prompt(input_data: Dict) -> str:
    """构建第一步筛选 prompt"""
    target = input_data["目标特征"]
    candidates = input_data["候选特征"]

    # 构建候选特征列表
    candidates_text = []
    for c in candidates:
        candidates_text.append(f"- {c['特征名称']} ({c['特征类型']})")
    candidates_section = "\n".join(candidates_text)

    return f'''# 背景
推理一个小红书帖子选题前脑海中的点，在创作者脑中的因果顺序

# Task
请分析【输入数据】与【目标点】的关系，按以下两类筛选证据：
1. **单独推理**：哪个特征单凭自己就能有可能指向目标特征？
2. **组合推理**：哪几个特征必须结合在一起，才能指向目标特征？（缺一不可才算组合）

如果能独立推出则无需组合。

# 筛选原则
1. 实质推形式，而不是形式推实质
2. 因推果而不是果推因
3. 目的推理手段而不是手段推理目的
4. 只有当 A 是 B 的充分必要条件的时候，A 可以推理出 B

**本次分析的目标特征是：{target['特征名称']}（{target['特征类型']}）**

# 输入数据
{candidates_section}

# 输出格式
请严格按照以下 JSON 结构输出：

```json
{{
  "目标特征": "{target['特征名称']}",
  "预备分析列表": {{
    "单独推理": [
      {{
        "来源特征": "特征A",
        "来源特征类型": "灵感点/目的点/关键点",
        "初步理由": "简要说明为什么这个特征可能推导出目标"
      }}
    ],
    "组合推理": [
      {{
        "组合成员": ["特征B", "特征C"],
        "成员类型": ["目的点", "关键点"],
        "初步理由": "简要说明为什么这些特征需要组合才能推导出目标"
      }}
    ]
  }}
}}
```

注意：
- 单独推理的来源特征必须是输入数据中的原话
- 组合推理的成员数量通常为 2-3 个
- 如果某个特征完全无法推导出目标，不要勉强添加
'''.strip()


def build_evaluate_prompt(input_data: Dict, filter_result: Dict) -> str:
    """构建第二步评估 prompt"""
    target = input_data["目标特征"]
    prep_list = filter_result.get("预备分析列表", {})

    # 构建单独推理列表
    single_items = prep_list.get("单独推理", [])
    single_text = ""
    if single_items:
        for item in single_items:
            single_text += f"- {item.get('来源特征', '')}（{item.get('来源特征类型', '')}）\n"
    else:
        single_text = "（无）\n"

    # 构建组合推理列表
    combo_items = prep_list.get("组合推理", [])
    combo_text = ""
    if combo_items:
        for item in combo_items:
            members = " + ".join(item.get("组合成员", []))
            combo_text += f"- {members}\n"
    else:
        combo_text = "（无）\n"

    return f'''# 背景
推理一个小红书帖子选题前的点，在创作者脑中的因果顺序

# Task
请判断以下筛选出的特征推理出【{target['特征名称']}】的可能性

## 待评估的单独推理特征：
{single_text}
## 待评估的组合推理特征：
{combo_text}
# 推理约束
1. 实质推形式，而不是形式推实质
2. 因推果而不是果推因
3. 目的推理手段而不是手段推理目的
4. 只有当 A 是 B 的充分必要条件的时候，A 可以推理出 B

# 评分标准
| 分数范围 | 等级 | 说明 |
|---------|------|------|
| 0.80 - 1.00 | 逻辑必然 | A 是 B 的充分必要条件，必然推导 |
| 0.50 - 0.79 | 高可能性 | A 高度倾向于推导出 B，但非唯一选择 |
| 0.20 - 0.49 | 创意偏好 | A 可以推导出 B，但其他选择同样可行 |
| 0.00 - 0.19 | 弱关联 | A 与 B 关联性很弱，不建议保留 |

# 输出格式
请严格按照以下 JSON 结构输出：

```json
{{
  "目标关键特征": "{target['特征名称']}",
  "推理分析": {{
    "单独推理": [
      {{
        "来源特征": "特征A",
        "来源特征类型": "灵感点/目的点/关键点",
        "可能性": 0.xx,
        "结论": "详细说明推导逻辑..."
      }}
    ],
    "组合推理": [
      {{
        "组合成员": ["特征B", "特征C"],
        "成员类型": ["目的点", "关键点"],
        "可能性": 0.xx,
        "结论": "详细说明组合推导逻辑..."
      }}
    ]
  }}
}}
```

注意：
- 如果某个特征经评估后可能性低于 0.2，可以标注但建议说明原因
- 结论要清晰说明推导逻辑，避免空洞表述
'''.strip()


# ===== 主分析函数 =====

async def analyze_node_origin(
    post_id: str = None,
    target_name: str = None,
    config: PathConfig = None
) -> Dict:
    """分析目标节点可能由哪些候选节点推导而来（两步法）"""
    if config is None:
        config = PathConfig()

    # 获取帖子图谱文件
    post_graph_files = get_post_graph_files(config)
    if not post_graph_files:
        raise ValueError("没有找到帖子图谱文件")

    # 选择帖子
    if post_id:
        target_file = next(
            (f for f in post_graph_files if post_id in f.name),
            None
        )
        if not target_file:
            raise ValueError(f"未找到帖子: {post_id}")
    else:
        target_file = post_graph_files[0]

    # 加载帖子图谱
    post_graph = load_post_graph(target_file)
    actual_post_id = post_graph.get("meta", {}).get("postId", "unknown")

    # 准备输入数据
    input_data = prepare_analyze_input(post_graph, target_name)
    actual_target_name = input_data["目标特征"]["特征名称"]

    print(f"帖子ID: {actual_post_id}")
    print(f"目标特征: {actual_target_name}")
    print(f"候选特征数: {len(input_data['候选特征'])}")

    # ===== 第一步：筛选 =====
    filter_prompt = build_filter_prompt(input_data)

    with custom_span(
        name=f"Step1 筛选 - {actual_target_name}",
        data={"目标特征": actual_target_name}
    ):
        filter_result_raw = await Runner.run(filter_agent, input=filter_prompt)
        filter_output = filter_result_raw.final_output

    # 解析筛选结果
    try:
        filter_result = parse_json_output(filter_output)
    except Exception as e:
        return {
            "帖子id": actual_post_id,
            "目标节点": actual_target_name,
            "模型": MODEL_NAME,
            "输入": input_data,
            "输出": None,
            "错误": f"筛选步骤解析失败: {str(e)}",
            "原始输出_筛选": filter_output
        }

    # 检查是否有可评估的特征
    prep_list = filter_result.get("预备分析列表", {})
    single_count = len(prep_list.get("单独推理", []))
    combo_count = len(prep_list.get("组合推理", []))

    if single_count == 0 and combo_count == 0:
        return {
            "帖子id": actual_post_id,
            "目标节点": actual_target_name,
            "模型": MODEL_NAME,
            "输入": input_data,
            "筛选结果": filter_result,
            "输出": {
                "目标关键特征": actual_target_name,
                "推理分析": {
                    "单独推理": [],
                    "组合推理": []
                }
            },
            "说明": "筛选步骤未找到可推导的特征"
        }

    print(f"  筛选结果: 单独推理 {single_count} 个, 组合推理 {combo_count} 个")

    # ===== 第二步：评估 =====
    evaluate_prompt = build_evaluate_prompt(input_data, filter_result)

    with custom_span(
        name=f"Step2 评估 - {actual_target_name}",
        data={"单独推理数": single_count, "组合推理数": combo_count}
    ):
        evaluate_result_raw = await Runner.run(evaluate_agent, input=evaluate_prompt)
        evaluate_output = evaluate_result_raw.final_output

    # 解析评估结果
    try:
        evaluate_result = parse_json_output(evaluate_output)
    except Exception as e:
        return {
            "帖子id": actual_post_id,
            "目标节点": actual_target_name,
            "模型": MODEL_NAME,
            "输入": input_data,
            "筛选结果": filter_result,
            "输出": None,
            "错误": f"评估步骤解析失败: {str(e)}",
            "原始输出_评估": evaluate_output
        }

    return {
        "帖子id": actual_post_id,
        "目标节点": actual_target_name,
        "模型": MODEL_NAME,
        "输入": input_data,
        "筛选结果": filter_result,
        "输出": evaluate_result
    }


def parse_json_output(output: str) -> Dict:
    """解析 JSON 输出"""
    if "```json" in output:
        json_start = output.find("```json") + 7
        json_end = output.find("```", json_start)
        json_str = output[json_start:json_end].strip()
    elif "{" in output and "}" in output:
        json_start = output.find("{")
        json_end = output.rfind("}") + 1
        json_str = output[json_start:json_end]
    else:
        json_str = output

    return json.loads(json_str)


# ===== 图谱构建函数 =====

def build_origin_graph(all_results: List[Dict], post_id: str) -> Dict:
    """将分析结果转换为图谱格式"""
    nodes = {}
    edges = {}
    # 特征名到节点ID的映射（用于修正 LLM 返回的类型名不匹配问题）
    name_to_node_id = {}

    for result in all_results:
        target_input = result.get("输入", {})

        # 添加目标节点
        target_info = target_input.get("目标特征", {})
        target_name = target_info.get("特征名称", "")
        target_type = target_info.get("特征类型", "关键点")
        node_id = f"帖子:{target_type}:标签:{target_name}"
        if node_id not in nodes:
            nodes[node_id] = {
                "name": target_name,
                "type": "标签",
                "dimension": target_type,
                "domain": "帖子",
                "detail": {}
            }
        name_to_node_id[target_name] = node_id

        # 添加候选特征节点
        for candidate in target_input.get("候选特征", []):
            c_name = candidate.get("特征名称", "")
            c_type = candidate.get("特征类型", "关键点")
            c_node_id = f"帖子:{c_type}:标签:{c_name}"
            if c_node_id not in nodes:
                nodes[c_node_id] = {
                    "name": c_name,
                    "type": "标签",
                    "dimension": c_type,
                    "domain": "帖子",
                    "detail": {}
                }
            name_to_node_id[c_name] = c_node_id

    # 构建推导边
    for result in all_results:
        target_name = result.get("目标特征", "")
        # 使用映射获取正确的节点ID
        target_node_id = name_to_node_id.get(target_name)
        if not target_node_id:
            continue

        # V4 的推理分析在顶层，不是在 输出 下面
        reasoning = result.get("推理分析", {})

        # 单独推理的边
        for item in reasoning.get("单独推理", []):
            source_name = item.get("来源特征", "")
            # 使用映射获取正确的节点ID（而非LLM返回的类型名）
            source_node_id = name_to_node_id.get(source_name)
            if not source_node_id:
                continue
            probability = item.get("可能性", 0)

            edge_id = f"{source_node_id}|推导|{target_node_id}"
            edges[edge_id] = {
                "source": source_node_id,
                "target": target_node_id,
                "type": "推导",
                "score": probability,
                "detail": {
                    "推理类型": "单独推理",
                    "结论": item.get("结论", "")
                }
            }

        # 组合推理的边
        for item in reasoning.get("组合推理", []):
            members = item.get("组合成员", [])
            probability = item.get("可能性", 0)

            # 验证所有成员都存在于映射中
            member_node_ids = []
            valid = True
            for m in members:
                m_node_id = name_to_node_id.get(m)
                if not m_node_id:
                    valid = False
                    break
                member_node_ids.append((m, m_node_id))
            if not valid:
                continue

            # 按名称排序
            sorted_member_ids = sorted(member_node_ids, key=lambda x: x[0])

            # 从节点ID中提取实际的维度类型（帖子:灵感点:标签:xxx -> 灵感点）
            combo_parts = []
            for m_name, m_node_id in sorted_member_ids:
                parts = m_node_id.split(":")
                m_dimension = parts[1] if len(parts) > 1 else "关键点"
                combo_parts.append(f"{m_dimension}:{m_name}")

            combo_name = " + ".join(combo_parts)
            combo_node_id = f"帖子:组合:组合:{combo_name}"
            if combo_node_id not in nodes:
                nodes[combo_node_id] = {
                    "name": combo_name,
                    "type": "组合",
                    "dimension": "组合",
                    "domain": "帖子",
                    "detail": {
                        "成员": [m for m, _ in sorted_member_ids],
                        "成员类型": [m_node_id.split(":")[1] for _, m_node_id in sorted_member_ids]
                    }
                }

            # 组合节点到目标的边
            edge_id = f"{combo_node_id}|推导|{target_node_id}"
            edges[edge_id] = {
                "source": combo_node_id,
                "target": target_node_id,
                "type": "推导",
                "score": probability,
                "detail": {
                    "推理类型": "组合推理",
                    "结论": item.get("结论", "")
                }
            }

            # 成员到组合节点的边
            for m_name, m_node_id in sorted_member_ids:
                m_edge_id = f"{m_node_id}|组成|{combo_node_id}"
                if m_edge_id not in edges:
                    edges[m_edge_id] = {
                        "source": m_node_id,
                        "target": combo_node_id,
                        "type": "组成",
                        "score": 1.0,
                        "detail": {}
                    }

    return {
        "meta": {
            "postId": post_id,
            "type": "推导图谱",
            "version": "v4",
            "stats": {
                "nodeCount": len(nodes),
                "edgeCount": len(edges)
            }
        },
        "nodes": nodes,
        "edges": edges
    }


# ===== 辅助函数 =====

def get_all_target_names(post_graph: Dict, dimensions: List[str] = None) -> List[str]:
    """获取所有可作为目标的特征名称"""
    if dimensions is None:
        dimensions = ["灵感点", "目的点", "关键点"]
    tags = extract_tags_from_post_graph(post_graph)
    return [t["name"] for t in tags if t["dimension"] in dimensions]


def get_score_level(score: float) -> str:
    """根据分数返回等级"""
    if score >= 0.80:
        return "逻辑必然"
    elif score >= 0.50:
        return "高可能性"
    elif score >= 0.20:
        return "创意偏好"
    else:
        return "弱关联"


def display_result(result: Dict):
    """显示单个分析结果"""
    output = result.get("输出")
    if output:
        print(f"\n目标关键特征: {output.get('目标关键特征', 'N/A')}")

        reasoning = output.get("推理分析", {})

        # 显示单独推理
        single = reasoning.get("单独推理", [])
        if single:
            print("  【单独推理】")
            for item in single[:5]:
                score = item.get("可能性", 0)
                level = get_score_level(score)
                print(f"    [{score:.2f} {level}] {item.get('来源特征', '')}")

        # 显示组合推理
        combo = reasoning.get("组合推理", [])
        if combo:
            print("  【组合推理】")
            for item in combo[:3]:
                members = " + ".join(item.get("组合成员", []))
                score = item.get("可能性", 0)
                level = get_score_level(score)
                print(f"    [{score:.2f} {level}] {members}")
    else:
        error = result.get("错误", "")
        if error:
            print(f"  分析失败: {error}")
        else:
            print(f"  {result.get('说明', '无结果')}")


# ===== 单帖子处理函数 =====

async def process_single_post(
    post_file: Path,
    config: PathConfig,
    target_name: str = None,
    num_targets: int = 999,
    dimensions: List[str] = None
):
    """处理单个帖子"""
    if dimensions is None:
        dimensions = ["灵感点", "目的点", "关键点"]

    # 为每个帖子生成独立的 trace
    current_time, log_url = set_trace()

    # 加载帖子图谱
    post_graph = load_post_graph(post_file)
    actual_post_id = post_graph.get("meta", {}).get("postId", "unknown")

    print(f"\n{'=' * 60}")
    print(f"帖子ID: {actual_post_id}")
    print(f"Trace URL: {log_url}")

    # 确定要分析的目标特征列表
    if target_name:
        target_names = [target_name]
    else:
        all_targets = get_all_target_names(post_graph, dimensions)
        target_names = all_targets[:num_targets]

    print(f"待分析目标特征: {target_names}")
    print("-" * 60)

    # 输出目录
    output_dir = config.intermediate_dir / "node_origin_analysis"
    output_dir.mkdir(parents=True, exist_ok=True)

    # 使用 trace 上下文包裹单个帖子的分析
    with trace(f"节点来源分析 V4 - {actual_post_id}"):
        # 并发分析所有目标特征
        async def analyze_single(name: str, index: int):
            print(f"\n[{index}/{len(target_names)}] 开始分析: {name}")
            result = await analyze_node_origin(
                post_id=actual_post_id,
                target_name=name,
                config=config
            )
            print(f"[{index}/{len(target_names)}] 完成: {name}")
            display_result(result)

            output = result.get("输出", {})
            return {
                "目标特征": result.get("目标节点"),
                "筛选结果": result.get("筛选结果"),
                "推理分析": output.get("推理分析", {}) if output else {},
                "输入": result.get("输入"),
                "错误": result.get("错误"),
                "说明": result.get("说明")
            }

        # 创建并发任务
        tasks = [
            analyze_single(name, i)
            for i, name in enumerate(target_names, 1)
        ]

        # 并发执行
        all_results = await asyncio.gather(*tasks)

    # 合并保存到一个文件
    merged_output = {
        "元数据": {
            "current_time": current_time,
            "log_url": log_url,
            "model": MODEL_NAME,
            "version": "v4"
        },
        "帖子id": actual_post_id,
        "分析结果列表": all_results
    }

    output_file = output_dir / f"{actual_post_id}_来源分析_v4.json"
    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(merged_output, f, ensure_ascii=False, indent=2)

    # 生成推导关系图谱
    graph_output = build_origin_graph(all_results, actual_post_id)
    graph_file = output_dir / f"{actual_post_id}_推导图谱_v4.json"
    with open(graph_file, "w", encoding="utf-8") as f:
        json.dump(graph_output, f, ensure_ascii=False, indent=2)

    print(f"\n完成! 共分析 {len(target_names)} 个目标特征")
    print(f"分析结果: {output_file}")
    print(f"推导图谱: {graph_file}")
    print(f"Trace: {log_url}")

    return actual_post_id


# ===== 主函数 =====

async def main(
    post_id: str = None,
    target_name: str = None,
    num_targets: int = 999,
    dimensions: List[str] = None,
    all_posts: bool = False
):
    """主函数"""
    if dimensions is None:
        dimensions = ["灵感点", "目的点", "关键点"]

    config = PathConfig()

    print(f"账号: {config.account_name}")
    print(f"使用模型: {MODEL_NAME}")
    print(f"分析维度: {dimensions}")
    print(f"版本: V4 (两步法: 筛选 + 评估)")

    # 获取帖子图谱文件
    post_graph_files = get_post_graph_files(config)
    if not post_graph_files:
        print("错误: 没有找到帖子图谱文件")
        return

    # 确定要处理的帖子列表
    if post_id:
        target_file = next(
            (f for f in post_graph_files if post_id in f.name),
            None
        )
        if not target_file:
            print(f"错误: 未找到帖子 {post_id}")
            return
        files_to_process = [target_file]
    elif all_posts:
        files_to_process = post_graph_files
    else:
        files_to_process = [post_graph_files[0]]

    print(f"待处理帖子数: {len(files_to_process)}")

    # 逐个处理帖子
    processed_posts = []
    for i, post_file in enumerate(files_to_process, 1):
        print(f"\n{'#' * 60}")
        print(f"# 处理帖子 {i}/{len(files_to_process)}")
        print(f"{'#' * 60}")

        post_id_result = await process_single_post(
            post_file=post_file,
            config=config,
            target_name=target_name,
            num_targets=num_targets,
            dimensions=dimensions
        )
        processed_posts.append(post_id_result)

    print(f"\n{'#' * 60}")
    print(f"# 全部完成! 共处理 {len(processed_posts)} 个帖子")
    print(f"{'#' * 60}")


def rebuild_graph_from_file(analysis_file: Path) -> None:
    """从已有的分析结果文件重建图谱"""
    with open(analysis_file, "r", encoding="utf-8") as f:
        data = json.load(f)

    post_id = data.get("帖子id", "unknown")
    all_results = data.get("分析结果列表", [])

    print(f"从分析文件重建图谱: {analysis_file.name}")
    print(f"帖子ID: {post_id}")
    print(f"分析结果数: {len(all_results)}")

    # 构建图谱
    graph_output = build_origin_graph(all_results, post_id)

    # 保存图谱
    graph_file = analysis_file.parent / f"{post_id}_推导图谱_v4.json"
    with open(graph_file, "w", encoding="utf-8") as f:
        json.dump(graph_output, f, ensure_ascii=False, indent=2)

    print(f"图谱已保存: {graph_file}")
    print(f"节点数: {graph_output['meta']['stats']['nodeCount']}")
    print(f"边数: {graph_output['meta']['stats']['edgeCount']}")


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="分析节点来源 (V4 两步法)")
    parser.add_argument("--post-id", type=str, help="帖子ID（指定则只处理该帖子）")
    parser.add_argument("--target", type=str, help="目标节点名称（指定则只分析这一个特征）")
    parser.add_argument("--num", type=int, default=999, help="要分析的目标特征数量")
    parser.add_argument("--dims", type=str, nargs="+",
                       choices=["灵感点", "目的点", "关键点"],
                       help="指定要分析的维度（默认全部）")
    parser.add_argument("--all-posts", action="store_true", help="处理所有帖子")
    parser.add_argument("--rebuild-graph", type=str, metavar="FILE",
                       help="从已有分析文件重建图谱（不重新分析）")
    args = parser.parse_args()

    # 如果指定了 --rebuild-graph，只重建图谱
    if args.rebuild_graph:
        rebuild_graph_from_file(Path(args.rebuild_graph))
    else:
        # 确定维度（默认所有维度）
        if args.dims:
            dimensions = args.dims
        else:
            dimensions = ["灵感点", "目的点", "关键点"]

        # 运行主函数
        asyncio.run(main(
            post_id=args.post_id,
            target_name=args.target,
            num_targets=args.num,
            dimensions=dimensions,
            all_posts=args.all_posts
        ))