knowledge_search/stage7_analyzer.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Stage 7 分析器
对 Stage 6 中完全匹配的帖子进行深度解构分析
"""

import os
import json
import time
import logging
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import Dict, List, Any, Optional

from stage7_api_client import DeconstructionAPIClient, map_note_to_api_format

try:
    from tqdm import tqdm
    TQDM_AVAILABLE = True
except ImportError:
    TQDM_AVAILABLE = False
    logger.warning("tqdm 未安装，将使用简单进度显示。安装命令: pip install tqdm")

logger = logging.getLogger(__name__)


class Stage7DeconstructionAnalyzer:
    """Stage 7: 完全匹配帖子的深度解构分析"""

    def __init__(
        self,
        api_url: str = "http://192.168.245.150:7000/what/analysis/single",
        max_workers: int = 5,
        max_notes: Optional[int] = None,
        min_score: float = 8.0,
        skip_count: int = 0,
        sort_by: str = 'score',
        timeout: int = 30,
        max_retries: int = 3,
        output_dir: str = "output_v2",
        enable_image_download: bool = True,
        image_server_url: str = "http://localhost:8765",
        image_download_dir: str = "downloaded_images",
        target_features: Optional[List[str]] = None
    ):
        """
        初始化 Stage 7 分析器

        Args:
            api_url: API 地址
            max_workers: 并发数
            max_notes: 最多处理多少个帖子（None = 不限制）
            min_score: 最低分数阈值（只处理 >= 此分数的帖子）
            skip_count: 跳过前 N 个
            sort_by: 排序方式 ('score' | 'time' | 'engagement')
            timeout: API 超时时间
            max_retries: API 最大重试次数
            output_dir: 输出目录
            enable_image_download: 是否启用图片下载（下载小红书图片并转换为本地URL）
            image_server_url: 图片服务器URL
            image_download_dir: 图片下载目录
            target_features: 指定要处理的原始特征列表（None = 处理所有特征）
        """
        self.max_workers = max_workers
        self.max_notes = max_notes
        self.min_score = min_score
        self.skip_count = skip_count
        self.sort_by = sort_by
        self.output_dir = output_dir
        self.enable_image_download = enable_image_download
        self.target_features = target_features  # 新增：目标特征过滤

        # 初始化 API 客户端
        self.api_client = DeconstructionAPIClient(
            api_url=api_url,
            timeout=timeout,
            max_retries=max_retries
        )

        # 图片下载功能已弃用，直接使用原始图片URL
        # 保留参数以向后兼容，但不再使用
        if self.enable_image_download:
            logger.warning("  注意: enable_image_download 参数已弃用，将直接使用原始图片URL")

    def extract_matched_notes_from_stage6(
        self,
        stage6_results: List[Dict]
    ) -> List[Dict]:
        """
        从 Stage 6 结果中提取所有完全匹配的帖子

        Args:
            stage6_results: Stage 6 结果（列表）

        Returns:
            完全匹配的帖子列表
        """
        matched_notes = []

        # Stage 6 结果是一个列表，每个元素是一个 feature_group
        for feature_group in stage6_results:
            original_feature = feature_group.get('原始特征名称', '')

            # 如果指定了 target_features，只处理指定的特征
            if self.target_features and original_feature not in self.target_features:
                continue

            # 遍历 组合评估结果_分组（这一层包含了 top10_searches）
            for combo_group in feature_group.get('组合评估结果_分组', []):
                # top10_searches 包含所有搜索结果
                for search_item in combo_group.get('top10_searches', []):
                    search_word = search_item.get('search_word', '')
                    source_word = search_item.get('source_word', '')
                    evaluation = search_item.get('evaluation_with_filter', {})

                    # 检查是否有搜索结果
                    if 'search_result' not in search_item:
                        continue

                    notes = search_item['search_result'].get('data', {}).get('data', [])

                    # 遍历评估结果
                    for note_eval in evaluation.get('notes_evaluation', []):
                        score = note_eval.get('综合得分', 0)

                        # 只处理完全匹配的（分数 >= min_score）
                        if score >= self.min_score:
                            note_index = note_eval.get('note_index', -1)
                            if 0 <= note_index < len(notes):
                                note = notes[note_index]

                                matched_notes.append({
                                    'note': note,
                                    'note_card': note.get('note_card', {}),
                                    'evaluation': note_eval,
                                    'search_word': search_word,
                                    'source_word': source_word,
                                    'original_feature': original_feature,
                                    'top3_persona_features': feature_group.get('top3匹配信息', [])
                                })

        return matched_notes

    def sort_matched_notes(
        self,
        matched_notes: List[Dict]
    ) -> List[Dict]:
        """
        对完全匹配的帖子进行排序

        Args:
            matched_notes: 匹配的帖子列表

        Returns:
            排序后的帖子列表
        """
        if self.sort_by == 'score':
            # 按评分降序（优先处理高分帖子）
            return sorted(
                matched_notes,
                key=lambda x: x['evaluation'].get('综合得分', 0),
                reverse=True
            )

        elif self.sort_by == 'time':
            # 按时间降序（优先处理最新帖子）
            return sorted(
                matched_notes,
                key=lambda x: x['note_card'].get('publish_timestamp', 0),
                reverse=True
            )

        elif self.sort_by == 'engagement':
            # 按互动量降序（点赞+收藏+评论）
            def calc_engagement(note_data):
                interact = note_data['note_card'].get('interact_info', {})
                return (
                    interact.get('liked_count', 0) +
                    interact.get('collected_count', 0) +
                    interact.get('comment_count', 0)
                )

            return sorted(
                matched_notes,
                key=calc_engagement,
                reverse=True
            )

        return matched_notes

    def _save_intermediate_results(
        self,
        results: List[Dict],
        output_path: str,
        processed_count: int,
        total_count: int,
        start_time: float
    ):
        """
        保存中间结果

        Args:
            results: 当前结果列表
            output_path: 输出路径
            processed_count: 已处理数量
            total_count: 总数量
            start_time: 开始时间
        """
        # 构建中间结果文件路径
        base_dir = os.path.dirname(output_path) or 'output_v2'
        base_name = os.path.basename(output_path)
        name_without_ext = os.path.splitext(base_name)[0]

        intermediate_path = os.path.join(
            base_dir,
            f"{name_without_ext}_partial_{processed_count}of{total_count}.json"
        )

        # 统计成功失败数
        success_count = sum(1 for r in results if r['api_response']['status'] == 'success')
        failed_count = len(results) - success_count

        # 构建中间结果
        intermediate_result = {
            'metadata': {
                'stage': 'stage7_partial',
                'description': f'部分结果（{processed_count}/{total_count}）',
                'processed_notes': len(results),
                'success_count': success_count,
                'failed_count': failed_count,
                'saved_at': datetime.now().isoformat(),
                'processing_time_seconds': round(time.time() - start_time, 2)
            },
            'results': results
        }

        # 保存
        os.makedirs(base_dir, exist_ok=True)
        with open(intermediate_path, 'w', encoding='utf-8') as f:
            json.dump(intermediate_result, f, ensure_ascii=False, indent=2)

        logger.info(f"    已保存中间结果: {intermediate_path} ({processed_count}/{total_count})")

    def process_single_note(
        self,
        matched_note_data: Dict,
        index: int,
        total: int
    ) -> Dict:
        """
        处理单个帖子的解构分析

        Args:
            matched_note_data: 匹配的帖子数据
            index: 当前索引（用于日志）
            total: 总数（用于日志）

        Returns:
            处理结果
        """
        note = matched_note_data['note']
        note_card = matched_note_data['note_card']
        evaluation = matched_note_data['evaluation']
        search_word = matched_note_data['search_word']
        original_feature = matched_note_data['original_feature']

        note_id = note.get('id', '')
        note_title = note_card.get('display_title', '')[:30]  # 前30个字符

        logger.info(f"[{index}/{total}] 解构分析: {note_id}")
        logger.info(f"  标题: {note_title}...")
        logger.info(f"  搜索词: {search_word}")
        logger.info(f"  原始特征: {original_feature}")

        # 获取关键匹配点（用于保存到结果中）
        key_points = evaluation.get('关键匹配点', [])

        # 获取 top3 人设特征
        top3_features = matched_note_data.get('top3_persona_features', [])

        # 构建 start_points - 只使用 top3 的第一个人设特征名称
        start_points = []
        if top3_features:
            first_feature = top3_features[0].get('人设特征名称', '')
            if first_feature:
                start_points = [first_feature]

        logger.info(f"  start_points: {start_points}")
        if top3_features:
            logger.info(f"  top3人设特征: {[f.get('人设特征名称', '') for f in top3_features[:3]]}")

        # 直接使用原始图片URL，不做任何处理
        original_images = note_card.get('image_list', [])
        if original_images:
            logger.info(f"  图片数量: {len(original_images)}")

        # 映射数据为 API 格式（直接使用原始图片URL）
        api_payload = map_note_to_api_format(
            note=note,
            note_card=note_card,
            evaluation=evaluation,
            search_word=search_word,
            original_feature=original_feature,
            start_points=start_points,
            processed_image_urls=None  # 不传递处理后的URL，使用原始URL
        )

        # 调用 API
        start_time = time.time()
        api_response = self.api_client.call_api(api_payload)
        processing_time = (time.time() - start_time) * 1000  # 毫秒

        # 构建结果
        result = {
            'note_id': note_id,
            'search_word': search_word,
            'original_feature': original_feature,
            'source_word': matched_note_data['source_word'],
            'evaluation_score': evaluation.get('综合得分', 0),
            'evaluation_type': evaluation.get('匹配类型', ''),
            'evaluation_confidence': evaluation.get('置信度', ''),
            'key_matching_points': key_points,
            'note_data': {
                'title': note_card.get('display_title', ''),
                'author': note_card.get('user', {}).get('nick_name', ''),
                'link': f"https://www.xiaohongshu.com/explore/{note_id}"
            },
            'api_request': api_payload,
            'api_response': api_response,
            'processed_at': datetime.now().isoformat(),
            'processing_time_ms': round(processing_time, 2)
        }

        if api_response['status'] == 'success':
            logger.info(f"  ✓ 成功 ({processing_time:.0f}ms)")
        else:
            logger.error(f"  ✗ 失败: {api_response['error']}")

        return result

    def run(
        self,
        stage6_results: Dict,
        output_path: Optional[str] = None
    ) -> Dict:
        """
        执行 Stage 7 解构分析

        Args:
            stage6_results: Stage 6 结果
            output_path: 输出路径（可选）

        Returns:
            Stage 7 结果
        """
        logger.info("\n" + "=" * 60)
        logger.info("Stage 7: 完全匹配帖子的深度解构分析")
        logger.info("=" * 60)

        # 打印配置参数
        logger.info("配置参数:")
        logger.info(f"  API 地址: {self.api_client.api_url}")
        if self.target_features:
            logger.info(f"  目标特征: {', '.join(self.target_features)}")
        else:
            logger.info(f"  目标特征: 全部")
        logger.info(f"  最低分数阈值: {self.min_score}")
        logger.info(f"  并发数: {self.max_workers}")
        logger.info(f"  最多处理帖子数: {self.max_notes if self.max_notes else '不限制'}")
        logger.info(f"  跳过前 N 个: {self.skip_count}")
        logger.info(f"  排序方式: {self.sort_by}")
        logger.info(f"  API 超时: {self.api_client.timeout}秒")
        logger.info(f"  最大重试次数: {self.api_client.max_retries}")

        # 默认输出路径
        if output_path is None:
            output_path = os.path.join(self.output_dir, "stage7_with_deconstruction.json")

        # 1. 提取完全匹配的帖子
        matched_notes = self.extract_matched_notes_from_stage6(stage6_results)
        total_matched = len(matched_notes)

        logger.info(f"  完全匹配帖子总数: {total_matched} (分数 >= {self.min_score})")

        if total_matched == 0:
            logger.warning("  没有找到完全匹配的帖子")
            return {
                'metadata': {
                    'stage': 'stage7',
                    'total_matched_notes': 0,
                    'processed_notes': 0
                },
                'results': []
            }

        # 2. 排序
        matched_notes = self.sort_matched_notes(matched_notes)
        logger.info(f"  排序方式: {self.sort_by}")

        # 3. 跳过前 N 个
        if self.skip_count > 0:
            logger.info(f"  跳过前 {self.skip_count} 个")
            matched_notes = matched_notes[self.skip_count:]

        # 4. 限制数量
        if self.max_notes is not None and len(matched_notes) > self.max_notes:
            logger.info(f"  数量限制: {self.max_notes}")
            matched_notes = matched_notes[:self.max_notes]

        to_process = len(matched_notes)
        logger.info(f"  实际处理: {to_process} 个")
        logger.info(f"  并发数: {self.max_workers}")
        logger.info(f"  API: {self.api_client.api_url}")

        if to_process == 0:
            logger.warning("  没有需要处理的帖子")
            return {
                'metadata': {
                    'stage': 'stage7',
                    'total_matched_notes': total_matched,
                    'processed_notes': 0,
                    'skipped_notes': self.skip_count
                },
                'results': []
            }

        # 5. 并行处理
        results = []
        start_time = time.time()
        save_interval = 10  # 每处理10个帖子保存一次

        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
            futures = []
            for idx, note_data in enumerate(matched_notes, start=1):
                future = executor.submit(
                    self.process_single_note,
                    note_data,
                    idx,
                    to_process
                )
                futures.append(future)

            # 收集结果（带进度显示）
            if TQDM_AVAILABLE:
                # 使用 tqdm 进度条
                logger.info("  使用进度条显示...")
                iterator = tqdm(
                    as_completed(futures),
                    total=len(futures),
                    desc="  处理进度",
                    unit="帖子",
                    ncols=100
                )
            else:
                # 简单进度显示
                iterator = as_completed(futures)

            processed_count = 0
            for future in iterator:
                try:
                    result = future.result()
                    results.append(result)
                    processed_count += 1

                    # 增量保存（每处理一定数量保存一次）
                    if processed_count % save_interval == 0:
                        self._save_intermediate_results(
                            results,
                            output_path,
                            processed_count,
                            to_process,
                            start_time
                        )

                    # 简单进度显示（如果没有 tqdm）
                    if not TQDM_AVAILABLE and processed_count % 5 == 0:
                        logger.info(f"  进度: {processed_count}/{to_process}")

                except Exception as e:
                    logger.error(f"  处理失败: {e}")

        processing_time = time.time() - start_time

        # 6. 统计
        success_count = sum(1 for r in results if r['api_response']['status'] == 'success')
        failed_count = len(results) - success_count

        logger.info(f"\n  总耗时: {processing_time:.1f}s")
        logger.info(f"  成功: {success_count}")
        logger.info(f"  失败: {failed_count}")

        # 7. 构建最终结果
        final_result = {
            'metadata': {
                'stage': 'stage7',
                'description': '完全匹配帖子的深度解构分析',
                'target_features': self.target_features if self.target_features else '全部',
                'total_matched_notes': total_matched,
                'processed_notes': len(results),
                'skipped_notes': self.skip_count,
                'max_notes_limit': self.max_notes,
                'sort_by': self.sort_by,
                'success_count': success_count,
                'failed_count': failed_count,
                'api_url': self.api_client.api_url,
                'min_score_threshold': self.min_score,
                'created_at': datetime.now().isoformat(),
                'processing_time_seconds': round(processing_time, 2)
            },
            'results': results
        }

        # 8. 保存结果
        os.makedirs(os.path.dirname(output_path), exist_ok=True)
        with open(output_path, 'w', encoding='utf-8') as f:
            json.dump(final_result, f, ensure_ascii=False, indent=2)

        logger.info(f"  结果已保存: {output_path}")

        return final_result


def test_stage7_analyzer():
    """测试 Stage 7 分析器"""
    # 读取 Stage 6 结果
    stage6_path = "output_v2/stage6_with_evaluations.json"

    if not os.path.exists(stage6_path):
        print(f"Stage 6 结果不存在: {stage6_path}")
        return

    with open(stage6_path, 'r', encoding='utf-8') as f:
        stage6_results = json.load(f)

    # 创建分析器
    analyzer = Stage7DeconstructionAnalyzer(
        max_workers=3,
        max_notes=5,  # 只测试 5 个
        skip_count=0,
        sort_by='score'
    )

    # 运行分析
    stage7_results = analyzer.run(stage6_results)

    print(f"\n处理了 {stage7_results['metadata']['processed_notes']} 个帖子")
    print(f"成功: {stage7_results['metadata']['success_count']}")
    print(f"失败: {stage7_results['metadata']['failed_count']}")


if __name__ == '__main__':
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
    )
    test_stage7_analyzer()