how/script/data_processing/match_inspiration_features.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
灵感点特征匹配脚本

从解构任务列表中提取灵感点的特征，与人设灵感特征进行匹配，
使用 relation_analyzer 模块分析特征之间的语义关系。
"""

import json
import asyncio
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 lib.semantic_similarity import compare_phrases

# 全局并发限制
MAX_CONCURRENT_REQUESTS = 20
semaphore = None


def get_semaphore():
    """获取全局信号量"""
    global semaphore
    if semaphore is None:
        semaphore = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
    return semaphore


async def match_single_pair(
    feature_name: str,
    persona_name: str,
    category_mapping: Dict = None,
    model_name: str = None
) -> Dict:
    """
    匹配单个特征对（带并发限制）

    Args:
        feature_name: 要匹配的特征名称
        persona_name: 人设特征名称
        category_mapping: 特征分类映射字典
        model_name: 使用的模型名称

    Returns:
        单个匹配结果，格式：
        {
            "人设特征名称": "xxx",
            "特征类型": "标签",
            "特征分类": ["分类1", "分类2"],
            "匹配结果": {
                "相似度": 0.75,
                "说明": "..."
            }
        }
    """
    sem = get_semaphore()
    async with sem:
        print(f"      匹配: {feature_name} <-> {persona_name}")
        similarity_result = await compare_phrases(
            phrase_a=feature_name,
            phrase_b=persona_name,
        )

        # 判断该特征是标签还是分类
        feature_type = "分类"  # 默认为分类
        categories = []

        if category_mapping:
            # 先在标签特征中查找（灵感点、关键点、目的点）
            is_tag_feature = False
            for ft in ["灵感点", "关键点", "目的点"]:
                if ft in category_mapping:
                    type_mapping = category_mapping[ft]
                    if persona_name in type_mapping:
                        # 找到了，说明是标签特征
                        feature_type = "标签"
                        categories = type_mapping[persona_name].get("所属分类", [])
                        is_tag_feature = True
                        break

            # 如果不是标签特征，检查是否是分类特征
            if not is_tag_feature:
                # 收集所有分类
                all_categories = set()
                for ft in ["灵感点", "关键点", "目的点"]:
                    if ft in category_mapping:
                        for fname, fdata in category_mapping[ft].items():
                            cats = fdata.get("所属分类", [])
                            all_categories.update(cats)

                # 如果当前特征名在分类列表中，则是分类特征
                if persona_name in all_categories:
                    feature_type = "分类"
                    categories = []  # 分类特征本身没有所属分类

        # 去重分类
        unique_categories = list(dict.fromkeys(categories))

        return {
            "人设特征名称": persona_name,
            "特征类型": feature_type,
            "特征分类": unique_categories,
            "匹配结果": similarity_result
        }


async def match_feature_with_persona(
    feature_name: str,
    persona_features: List[Dict],
    category_mapping: Dict = None,
    model_name: str = None
) -> List[Dict]:
    """
    将一个特征与人设特征列表进行匹配（并发执行）

    Args:
        feature_name: 要匹配的特征名称
        persona_features: 人设特征列表
        category_mapping: 特征分类映射字典
        model_name: 使用的模型名称

    Returns:
        匹配结果列表
    """
    # 创建所有匹配任务
    tasks = [
        match_single_pair(feature_name, persona_feature["特征名称"], category_mapping, model_name)
        for persona_feature in persona_features
    ]

    # 并发执行所有匹配
    match_results = await asyncio.gather(*tasks)

    return list(match_results)


async def match_single_feature(
    feature_item: Dict,
    persona_features: List[Dict],
    category_mapping: Dict = None,
    model_name: str = None
) -> Dict:
    """
    匹配单个特征与所有人设特征

    Args:
        feature_item: 特征信息（包含"特征名称"和"权重"）
        persona_features: 人设特征列表
        category_mapping: 特征分类映射字典
        model_name: 使用的模型名称

    Returns:
        特征匹配结果
    """
    feature_name = feature_item.get("特征名称", "")
    feature_weight = feature_item.get("权重", 1.0)

    print(f"    特征: {feature_name} (权重: {feature_weight})")
    match_results = await match_feature_with_persona(
        feature_name=feature_name,
        persona_features=persona_features,
        category_mapping=category_mapping,
        model_name=model_name
    )

    return {
        "特征名称": feature_name,
        "权重": feature_weight,
        "匹配结果": match_results
    }


async def process_single_inspiration_point(
    inspiration_point: Dict,
    persona_features: List[Dict],
    category_mapping: Dict = None,
    model_name: str = None
) -> Dict:
    """
    处理单个灵感点的特征匹配（并发执行）

    Args:
        inspiration_point: 灵感点数据
        persona_features: 人设灵感特征列表
        category_mapping: 特征分类映射字典
        model_name: 使用的模型名称

    Returns:
        包含 how 步骤列表的灵感点数据
    """
    point_name = inspiration_point.get("名称", "")
    feature_list = inspiration_point.get("特征列表", [])

    print(f"  处理灵感点: {point_name}")
    print(f"    特征数量: {len(feature_list)}")

    # 并发匹配所有特征
    tasks = [
        match_single_feature(feature_item, persona_features, category_mapping, model_name)
        for feature_item in feature_list
    ]
    feature_match_results = await asyncio.gather(*tasks)

    # 构建 how 步骤
    how_step = {
        "步骤名称": "灵感特征分别匹配人设特征",
        "特征列表": list(feature_match_results)
    }

    # 返回更新后的灵感点
    result = inspiration_point.copy()
    result["how步骤列表"] = [how_step]

    return result


async def process_single_task(
    task: Dict,
    task_index: int,
    total_tasks: int,
    persona_inspiration_features: List[Dict],
    category_mapping: Dict = None,
    model_name: str = None
) -> Dict:
    """
    处理单个任务

    Args:
        task: 任务数据
        task_index: 任务索引（从1开始）
        total_tasks: 总任务数
        persona_inspiration_features: 人设灵感特征列表
        category_mapping: 特征分类映射字典
        model_name: 使用的模型名称

    Returns:
        包含 how 解构结果的任务
    """
    post_id = task.get("帖子id", "")
    print(f"\n处理任务 [{task_index}/{total_tasks}]: {post_id}")

    # 获取灵感点列表
    what_result = task.get("what解构结果", {})
    inspiration_list = what_result.get("灵感点列表", [])

    print(f"  灵感点数量: {len(inspiration_list)}")

    # 并发处理所有灵感点
    tasks = [
        process_single_inspiration_point(
            inspiration_point=inspiration_point,
            persona_features=persona_inspiration_features,
            category_mapping=category_mapping,
            model_name=model_name
        )
        for inspiration_point in inspiration_list
    ]
    updated_inspiration_list = await asyncio.gather(*tasks)

    # 构建 how 解构结果
    how_result = {
        "灵感点列表": list(updated_inspiration_list)
    }

    # 更新任务
    updated_task = task.copy()
    updated_task["how解构结果"] = how_result

    return updated_task


async def process_task_list(
    task_list: List[Dict],
    persona_features_dict: Dict,
    category_mapping: Dict = None,
    model_name: str = None
) -> List[Dict]:
    """
    处理整个解构任务列表（并发执行）

    Args:
        task_list: 解构任务列表
        persona_features_dict: 人设特征字典（包含灵感点、目的点、关键点）
        category_mapping: 特征分类映射字典
        model_name: 使用的模型名称

    Returns:
        包含 how 解构结果的任务列表
    """
    # 获取标签特征列表
    persona_inspiration_features = persona_features_dict.get("灵感点", [])
    print(f"人设标签特征数量: {len(persona_inspiration_features)}")

    # 从分类映射中提取所有唯一的分类作为分类特征（仅从灵感点中提取）
    category_features = []
    if category_mapping:
        all_categories = set()
        # 只从灵感点中提取分类
        if "灵感点" in category_mapping:
            for _, feature_data in category_mapping["灵感点"].items():
                categories = feature_data.get("所属分类", [])
                all_categories.update(categories)

        # 转换为特征格式
        category_features = [{"特征名称": cat} for cat in sorted(all_categories)]
        print(f"人设分类特征数量: {len(category_features)}")

    # 合并标签特征和分类特征
    all_features = persona_inspiration_features + category_features
    print(f"总特征数量（标签+分类）: {len(all_features)}")

    # 并发处理所有任务
    tasks = [
        process_single_task(
            task=task,
            task_index=i,
            total_tasks=len(task_list),
            persona_inspiration_features=all_features,
            category_mapping=category_mapping,
            model_name=model_name
        )
        for i, task in enumerate(task_list, 1)
    ]
    updated_task_list = await asyncio.gather(*tasks)

    return list(updated_task_list)


async def main():
    """主函数"""
    # 输入输出路径
    script_dir = Path(__file__).parent
    project_root = script_dir.parent.parent
    data_dir = project_root / "data" / "data_1118"

    task_list_file = data_dir / "当前帖子_解构任务列表.json"
    persona_features_file = data_dir / "特征名称_帖子来源.json"
    category_mapping_file = data_dir / "特征名称_分类映射.json"
    output_dir = data_dir / "当前帖子_how解构结果"

    # 创建输出目录
    output_dir.mkdir(parents=True, exist_ok=True)

    print(f"读取解构任务列表: {task_list_file}")
    with open(task_list_file, "r", encoding="utf-8") as f:
        task_list_data = json.load(f)

    print(f"读取人设特征: {persona_features_file}")
    with open(persona_features_file, "r", encoding="utf-8") as f:
        persona_features_data = json.load(f)

    print(f"读取特征分类映射: {category_mapping_file}")
    with open(category_mapping_file, "r", encoding="utf-8") as f:
        category_mapping = json.load(f)

    # 获取任务列表
    task_list = task_list_data.get("解构任务列表", [])
    print(f"\n总任务数: {len(task_list)}")

    # 处理任务列表
    updated_task_list = await process_task_list(
        task_list=task_list,
        persona_features_dict=persona_features_data,
        category_mapping=category_mapping,
        model_name=None  # 使用默认模型
    )

    # 分文件保存结果
    print(f"\n保存结果到: {output_dir}")
    for task in updated_task_list:
        post_id = task.get("帖子id", "unknown")
        output_file = output_dir / f"{post_id}_how.json"

        print(f"  保存: {output_file.name}")
        with open(output_file, "w", encoding="utf-8") as f:
            json.dump(task, f, ensure_ascii=False, indent=4)

    print("\n完成!")

    # 打印统计信息
    total_inspiration_points = sum(
        len(task["how解构结果"]["灵感点列表"])
        for task in updated_task_list
    )
    total_features = sum(
        len(point["特征列表"])
        for task in updated_task_list
        for point in task["how解构结果"]["灵感点列表"]
    )
    print(f"\n统计:")
    print(f"  处理的帖子数: {len(updated_task_list)}")
    print(f"  处理的灵感点数: {total_inspiration_points}")
    print(f"  处理的特征数: {total_features}")


if __name__ == "__main__":
    asyncio.run(main())