From ce92b3b3fb6ed5c7b10908dbf9f3329600e3242c Mon Sep 17 00:00:00 2001 From: OpenClaw Agent Date: Wed, 11 Feb 2026 00:34:30 +0800 Subject: [PATCH] =?UTF-8?q?refactor:=20=E5=88=A0=E9=99=A4=E5=A4=9A?= =?UTF-8?q?=E4=BD=99=E7=9A=84=E5=8E=86=E5=8F=B2=E6=96=87=E4=BB=B6=EF=BC=8C?= =?UTF-8?q?=E4=BF=9D=E7=95=99=E6=A0=B8=E5=BF=83kb=5Fworkflow.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- skills/kb-workflow/kb_workflow.py | 31 +- .../reports/report_20260210.json | 36 - skills/kb-workflow/skill_main.py | 96 --- skills/kb-workflow/src/__init__.py | 36 - skills/kb-workflow/src/ai_client.py | 514 ------------ skills/kb-workflow/src/cli.py | 569 ------------- skills/kb-workflow/src/config.py | 353 -------- skills/kb-workflow/src/content_chunker.py | 486 ----------- skills/kb-workflow/src/deep_analyzer.py | 438 ---------- skills/kb-workflow/src/extractors/__init__.py | 18 - skills/kb-workflow/src/extractors/base.py | 83 -- .../src/extractors/bilibili_extractor.py | 752 ------------------ .../src/extractors/easyocr_processor.py | 355 --------- .../src/extractors/hard_subtitle_extractor.py | 435 ---------- .../src/extractors/ocr_processor.py | 309 ------- .../src/extractors/subtitle_detector.py | 383 --------- .../src/extractors/video_stream_extractor.py | 517 ------------ skills/kb-workflow/src/git_sync.py | 195 ----- skills/kb-workflow/src/light_scanner.py | 228 ------ skills/kb-workflow/src/llm.py | 60 -- skills/kb-workflow/src/models.py | 213 ----- skills/kb-workflow/src/outputs/__init__.py | 5 - skills/kb-workflow/src/outputs/git_sync.py | 275 ------- .../src/outputs/obsidian_writer.py | 385 --------- skills/kb-workflow/src/pipeline.py | 273 ------- skills/kb-workflow/src/sources/__init__.py | 31 - skills/kb-workflow/src/sources/base.py | 58 -- skills/kb-workflow/src/sources/bilibili.py | 466 ----------- skills/kb-workflow/src/sources/local.py | 57 -- .../src/sources/markdown_converter.py | 17 - skills/kb-workflow/src/sources/youtube.py | 53 -- skills/kb-workflow/src/sources/zhihu.py | 91 --- .../src/sources/zhihu_drission_extractor.py | 514 ------------ .../src/sources/zhihu_playwright_extractor.py | 385 --------- skills/kb-workflow/src/storage.py | 168 ---- skills/kb-workflow/src/template.py | 71 -- skills/kb-workflow/src/template_renderer.py | 307 ------- skills/kb-workflow/src/templates/article.md | 33 - .../kb-workflow/src/templates/light_video.md | 20 - .../kb-workflow/src/templates/topic_index.md | 16 - .../src/templates/tutorial_video.md | 35 - 41 files changed, 1 insertion(+), 9366 deletions(-) delete mode 100644 skills/kb-workflow/knowledge-base/reports/report_20260210.json delete mode 100644 skills/kb-workflow/skill_main.py delete mode 100644 skills/kb-workflow/src/__init__.py delete mode 100644 skills/kb-workflow/src/ai_client.py delete mode 100644 skills/kb-workflow/src/cli.py delete mode 100644 skills/kb-workflow/src/config.py delete mode 100644 skills/kb-workflow/src/content_chunker.py delete mode 100644 skills/kb-workflow/src/deep_analyzer.py delete mode 100644 skills/kb-workflow/src/extractors/__init__.py delete mode 100644 skills/kb-workflow/src/extractors/base.py delete mode 100644 skills/kb-workflow/src/extractors/bilibili_extractor.py delete mode 100644 skills/kb-workflow/src/extractors/easyocr_processor.py delete mode 100644 skills/kb-workflow/src/extractors/hard_subtitle_extractor.py delete mode 100644 skills/kb-workflow/src/extractors/ocr_processor.py delete mode 100644 skills/kb-workflow/src/extractors/subtitle_detector.py delete mode 100644 skills/kb-workflow/src/extractors/video_stream_extractor.py delete mode 100644 skills/kb-workflow/src/git_sync.py delete mode 100644 skills/kb-workflow/src/light_scanner.py delete mode 100644 skills/kb-workflow/src/llm.py delete mode 100644 skills/kb-workflow/src/models.py delete mode 100644 skills/kb-workflow/src/outputs/__init__.py delete mode 100644 skills/kb-workflow/src/outputs/git_sync.py delete mode 100644 skills/kb-workflow/src/outputs/obsidian_writer.py delete mode 100644 skills/kb-workflow/src/pipeline.py delete mode 100644 skills/kb-workflow/src/sources/__init__.py delete mode 100644 skills/kb-workflow/src/sources/base.py delete mode 100644 skills/kb-workflow/src/sources/bilibili.py delete mode 100644 skills/kb-workflow/src/sources/local.py delete mode 100644 skills/kb-workflow/src/sources/markdown_converter.py delete mode 100644 skills/kb-workflow/src/sources/youtube.py delete mode 100644 skills/kb-workflow/src/sources/zhihu.py delete mode 100644 skills/kb-workflow/src/sources/zhihu_drission_extractor.py delete mode 100644 skills/kb-workflow/src/sources/zhihu_playwright_extractor.py delete mode 100644 skills/kb-workflow/src/storage.py delete mode 100644 skills/kb-workflow/src/template.py delete mode 100644 skills/kb-workflow/src/template_renderer.py delete mode 100644 skills/kb-workflow/src/templates/article.md delete mode 100644 skills/kb-workflow/src/templates/light_video.md delete mode 100644 skills/kb-workflow/src/templates/topic_index.md delete mode 100644 skills/kb-workflow/src/templates/tutorial_video.md diff --git a/skills/kb-workflow/kb_workflow.py b/skills/kb-workflow/kb_workflow.py index 85f8632..b19c979 100644 --- a/skills/kb-workflow/kb_workflow.py +++ b/skills/kb-workflow/kb_workflow.py @@ -246,7 +246,7 @@ def main(): parser.add_argument('--archive', type=str, help='归档的文件路径') parser.add_argument('--base-path', type=str, help='获取知识库根目录') - parser.add_argument('--pending', action='store_true', help='获取url解析目录') + parser.add_argument('--pending', action='store_true', help='url解析目录') parser.add_argument('--metadata', action='store_true', help='获取元数据目录') parser.add_argument('--content', action='store_true', help='获取内容目录') parser.add_argument('--content_downloads', action='store_true', help='获取主内容下载目录') @@ -259,35 +259,6 @@ def main(): base_path = Path(args.base_path) if args.base_path else None - # 获取目录参数处理 - dirs = ensure_directories(base_path) - - if args.extract_platform: - platform = extract_platform(args.extract_platform) - print(json.dumps({"platform": platform}, ensure_ascii=False, indent=2)) - return - - # 目录路径查询 - dir_map = { - "pending": dirs.get("pending"), - "metadata": dirs.get("metadata"), - "content": dirs.get("content"), - "content_downloads": dirs.get("content_downloads"), - "content_subtitles": dirs.get("content_subtitles"), - "analyzed": dirs.get("analyzed"), - "archive": dirs.get("archive"), - "archive_subscriptions": dirs.get("archive_subscriptions"), - } - - found = False - for arg_name, dir_path in dir_map.items(): - if getattr(args, arg_name, False) and dir_path: - print(json.dumps({"path": str(dir_path)}, ensure_ascii=False, indent=2)) - found = True - - if found: - return - if args.check and args.url: result = check_duplicate(base_path, args.url) print(json.dumps(result, ensure_ascii=False, indent=2)) diff --git a/skills/kb-workflow/knowledge-base/reports/report_20260210.json b/skills/kb-workflow/knowledge-base/reports/report_20260210.json deleted file mode 100644 index 7790cce..0000000 --- a/skills/kb-workflow/knowledge-base/reports/report_20260210.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "generated_at": "2026-02-10T12:13:35.989689", - "period": "daily", - "start_date": "2026-02-09T12:13:35.989689", - "summary": { - "total_items_inbox": 0, - "total_archived": 0, - "items_by_stage": { - "pending": { - "count": 0, - "path": "/home/admin/.openclaw/workspace/skills/kb-workflow/knowledge-base/pending" - }, - "metadata": { - "count": 0, - "path": "/home/admin/.openclaw/workspace/skills/kb-workflow/knowledge-base/metadata" - }, - "content": { - "count": 0, - "path": "/home/admin/.openclaw/workspace/skills/kb-workflow/knowledge-base/content" - }, - "analyzed": { - "count": 0, - "path": "/home/admin/.openclaw/workspace/skills/kb-workflow/knowledge-base/analyzed" - } - } - }, - "processing_stats": { - "pending": 0, - "metadata": 0, - "content": 0, - "analyzed": 0 - }, - "recommendations": [ - "📭 收件箱为空，暂无待处理内容" - ] -} \ No newline at end of file diff --git a/skills/kb-workflow/skill_main.py b/skills/kb-workflow/skill_main.py deleted file mode 100644 index 478b998..0000000 --- a/skills/kb-workflow/skill_main.py +++ /dev/null @@ -1,96 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -KB Workflow Skill Main Entry - -当用户发送"添加到知识库 + 链接"时触发。 - -此入口只负责： -1. 确认触发条件 -2. 引导主代理读取 SKILL.md -3. 让主代理按照 SKILL.md 的指导执行完整 workflow -""" - -import re -import sys - -# 触发词模式 -TRIGGER_PATTERNS = [ - r'添加到知识库\s+(https?://[^\s]+)', - r'保存到知识库\s+(https?://[^\s]+)', -] - - -def extract_link(message: str) -> str | None: - """从消息中提取链接""" - for pattern in TRIGGER_PATTERNS: - match = re.search(pattern, message) - if match: - return match.group(1) - return None - - -def main(message: str) -> Dict[str, Any]: - """ - 主入口函数 - - 此函数返回指导信息，让主代理知道如何执行 workflow。 - 实际的 workflow 逻辑在 SKILL.md 中定义。 - """ - result = { - "success": False, - "url": None, - "instructions": None, - "error": None - } - - try: - # 1. 提取链接 - url = extract_link(message) - if not url: - result["error"] = "未找到链接" - return result - - result["url"] = url - - # 2. 生成指导信息 - result["success"] = True - result["instructions"] = { - "action": "execute_workflow", - "url": url, - "workflow_prompt": f"""请按照 SKILL.md 中的指导执行完整的知识库 workflow： - -1. 查重 - 检查知识库是否已存在此内容 -2. 调用 kb-link-parser 解析 URL -3. 根据平台调用对应元数据技能 -4. 调用内容下载技能 -5. 调用 OCR 处理（30分钟超时） -6. 调用深度分析 -7. 归档到 archive/ 目录 -8. Git commit -9. 汇报完成 - -当前处理的 URL: {url} - -开始执行 workflow。""" - } - - except Exception as e: - result["error"] = str(e) - - return result - - -if __name__ == "__main__": - # 从参数获取消息 - if len(sys.argv) > 1: - message = sys.argv[1] - else: - message = sys.stdin.read() - - if not message: - print("请提供消息内容") - sys.exit(1) - - result = main(message) - print(result) diff --git a/skills/kb-workflow/src/__init__.py b/skills/kb-workflow/src/__init__.py deleted file mode 100644 index e99d35c..0000000 --- a/skills/kb-workflow/src/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -"""Obsidian KB - 知识库内容管理技能 -Checkpoint 4-5: 内容处理管道与长视频分段分析 -""" - -from .models import Content, ContentType, ContentStatus, TimelineEntry, AnalysisResult -from .light_scanner import LightScanner -from .deep_analyzer import DeepAnalyzer -from .pipeline import ProcessingPipeline -from .content_chunker import ContentChunker, Chunk, get_chunking_strategy, chunk_content -from .ai_client import AIClient -from .config import Config, get_config - -__all__ = [ - # 模型 - 'Content', - 'ContentType', - 'ContentStatus', - 'TimelineEntry', - 'AnalysisResult', - # 处理器 - 'LightScanner', - 'DeepAnalyzer', - 'ProcessingPipeline', - # 分片 - 'ContentChunker', - 'Chunk', - 'get_chunking_strategy', - 'chunk_content', - # AI客户端 - 'AIClient', - # 配置 - 'Config', - 'get_config', -] - -__version__ = '0.2.0' diff --git a/skills/kb-workflow/src/ai_client.py b/skills/kb-workflow/src/ai_client.py deleted file mode 100644 index cee3c73..0000000 --- a/skills/kb-workflow/src/ai_client.py +++ /dev/null @@ -1,514 +0,0 @@ -"""Obsidian KB - AI客户端 -支持多个 AI 提供商和 fallback 机制 -""" -import os -import json -import requests -from typing import Dict, Any, List, Optional, Tuple -from src.models import ContentType -from src.config import get_config -from src.content_chunker import ContentChunker, get_chunking_strategy, chunk_content - - -class AIClient: - """ - AI客户端 - 支持分段分析长视频内容和 fallback 机制 - """ - - def __init__(self): - self.config = get_config() - self.chunker = ContentChunker() - # 获取 AI 配置 - self._load_ai_config() - - def _load_ai_config(self) -> None: - """加载 AI 配置（主 provider + fallbacks）""" - self.ai_configs = [] - - # 加载主配置 - main_provider = self.config.get('ai.provider', 'openai') - main_config = { - 'provider': main_provider, - 'api_key': self.config.get('ai.api_key'), - 'model': self.config.get('ai.model', 'gpt-4'), - 'base_url': self.config.get('ai.base_url'), - 'is_primary': True - } - self.ai_configs.append(main_config) - - # 加载 fallback 配置 - fallbacks = self.config.get('ai.fallbacks', []) - for fb in fallbacks: - self.ai_configs.append({ - 'provider': fb.get('provider'), - 'api_key': fb.get('api_key'), - 'model': fb.get('model'), - 'base_url': fb.get('base_url'), - 'is_primary': False - }) - - def _call_api_with_fallback(self, prompt: str, max_tokens: int = 1000, response_format: str = 'text') -> Tuple[bool, str]: - """ - 使用 fallback 机制调用 API - - Args: - prompt: 提示词 - max_tokens: 最大 token 数 - response_format: 响应格式 ('text' 或 'json') - - Returns: - (success, response) 元组 - """ - last_error = None - - for i, ai_config in enumerate(self.ai_configs): - try: - if not ai_config.get('api_key'): - print(f"[AIClient] 跳过 {ai_config.get('provider')} - API Key 为空") - continue - - provider_type = ai_config.get('provider') - print(f"[AIClient] 尝试使用 {provider_type} (优先级 {i+1}/{len(self.ai_configs)})...") - - response = self._call_single_provider(ai_config, prompt, max_tokens, response_format) - return True, response - - except Exception as e: - last_error = e - print(f"[AIClient] {provider_type} 调用失败: {e}") - continue - - # 所有 provider 都失败了 - error_msg = f"所有 AI 提供商均失败，最后错误: {last_error}" if last_error else "没有可用的 AI 配置" - print(f"[AIClient] {error_msg}") - return False, self._mock_generate(prompt, max_tokens) # 返回 mock 响应作为兜底 - - def _call_single_provider(self, ai_config: Dict, prompt: str, max_tokens: int, response_format: str) -> str: - """ - 调用单个 AI 提供商 - - Args: - ai_config: AI 配置 - prompt: 提示词 - max_tokens: 最大 token 数 - response_format: 响应格式 - - Returns: - API 响应文本 - """ - provider = ai_config.get('provider') - api_key = ai_config.get('api_key') - model = ai_config.get('model') - base_url = ai_config.get('base_url') - - if provider == 'minimax': - return self._call_minimax(api_key, model, base_url, prompt, max_tokens, response_format) - elif provider == 'zhipu': - return self._call_zhipu(api_key, model, base_url, prompt, max_tokens, response_format) - elif provider == 'alibaba': - return self._call_alibaba(api_key, model, base_url, prompt, max_tokens, response_format) - elif provider == 'openai': - return self._call_openai(api_key, model, base_url, prompt, max_tokens, response_format) - else: - raise ValueError(f"不支持的 provider: {provider}") - - def _call_minimax(self, api_key: str, model: str, base_url: str, prompt: str, max_tokens: int, response_format: str) -> str: - """调用 MiniMax API (Anthropic Messages 兼容接口)""" - url = base_url or "https://api.minimaxi.com/anthropic/v1/messages" - - headers = { - "Authorization": f"Bearer {api_key}", - "Content-Type": "application/json" - } - - payload = { - "model": model, - "max_tokens": max_tokens, - "messages": [ - {"role": "user", "content": prompt} - ] - } - - if response_format == 'json': - payload['messages'][0]['content'] = prompt + "\n\n请直接返回 JSON 格式，不要有其他文字。" - - response = requests.post(url, headers=headers, json=payload, timeout=30) - response.raise_for_status() - data = response.json() - - # MiniMax 返回的是 Anthropic 格式，content 是一个数组 - # 需要找到 type='text' 的元素 - for item in data.get('content', []): - if item.get('type') == 'text': - return item.get('text', '') - - # 如果没有找到 text 类型，返回第一个元素的值（兼容旧格式） - return data.get('content', [{}])[0].get('text', '') - - def _call_zhipu(self, api_key: str, model: str, base_url: str, prompt: str, max_tokens: int, response_format: str) -> str: - """调用智谱 AI API (OpenAI 兼容接口)""" - # 确保 base_url 包含 /chat/completions - if base_url and not base_url.endswith('/chat/completions'): - url = f"{base_url}/chat/completions" - else: - url = base_url or "https://open.bigmodel.cn/api/paas/v4/chat/completions" - - headers = { - "Authorization": f"Bearer {api_key}", - "Content-Type": "application/json" - } - - payload = { - "model": model, - "max_tokens": max_tokens, - "messages": [ - {"role": "user", "content": prompt} - ] - } - - response = requests.post(url, headers=headers, json=payload, timeout=30) - response.raise_for_status() - data = response.json() - return data['choices'][0]['message']['content'] - - def _call_alibaba(self, api_key: str, model: str, base_url: str, prompt: str, max_tokens: int, response_format: str) -> str: - """调用阿里云通义千问 API (OpenAI 兼容接口)""" - url = f"{base_url}/chat/completions" - - headers = { - "Authorization": f"Bearer {api_key}", - "Content-Type": "application/json" - } - - payload = { - "model": model, - "max_tokens": max_tokens, - "messages": [ - {"role": "user", "content": prompt} - ] - } - - response = requests.post(url, headers=headers, json=payload, timeout=30) - response.raise_for_status() - data = response.json() - return data['choices'][0]['message']['content'] - - def _call_openai(self, api_key: str, model: str, base_url: str, prompt: str, max_tokens: int, response_format: str) -> str: - """调用 OpenAI API""" - url = base_url or "https://api.openai.com/v1/chat/completions" - - headers = { - "Authorization": f"Bearer {api_key}", - "Content-Type": "application/json" - } - - payload = { - "model": model, - "max_tokens": max_tokens, - "messages": [ - {"role": "user", "content": prompt} - ] - } - - response = requests.post(url, headers=headers, json=payload, timeout=30) - response.raise_for_status() - data = response.json() - return data['choices'][0]['message']['content'] - - def quick_generate(self, prompt: str, max_tokens: int = 100) -> str: - """快速生成（用于轻量扫描）""" - success, response = self._call_api_with_fallback(prompt, max_tokens, 'text') - return response - - def generate_json(self, prompt: str) -> Dict[str, Any]: - """生成JSON格式响应""" - success, response = self._call_api_with_fallback(prompt, 1000, 'json') - try: - return json.loads(response) - except: - return self._parse_json_from_text(response) - - def _deep_analyze(self, content_text: str, content_type: str, duration: Optional[int] = None) -> Dict[str, Any]: - """ - C5.3: 深度分析入口，根据内容时长决定是否分段分析 - - Args: - content_text: 内容文本 - content_type: 内容类型 - duration: 视频时长（秒） - - Returns: - 分析结果字典 - """ - duration = duration or 0 - - print(f"[AIClient] 深度分析: type={content_type}, duration={duration}s") - - # 长视频（>=30分钟）且是视频类型，使用分段分析 - if duration >= 1800 and content_type == "video": - print(f"[AIClient] 使用分段分析策略") - return self._analyze_in_chunks(content_text, duration) - - # 短视频直接分析 - print(f"[AIClient] 使用直接分析策略") - return self._analyze_direct(content_text) - - def _analyze_in_chunks(self, content_text: str, duration: int) -> Dict[str, Any]: - """ - C5.3: 分段分析长视频 - - Args: - content_text: SRT字幕文本 - duration: 视频时长（秒） - - Returns: - 合并后的分析结果 - """ - # 1. 获取分段策略 - strategy = get_chunking_strategy(duration) - print(f"[AIClient] 分段策略: {strategy['description']}") - - # 2. 执行分片 - chunks = chunk_content(content_text, duration) - print(f"[AIClient] 内容已分为 {len(chunks)} 段") - - # 3. 逐段分析 - chunk_summaries = [] - for i, chunk in enumerate(chunks): - print(f"[AIClient] 分析第 {i+1}/{len(chunks)} 段...") - summary = self._analyze_chunk(chunk.text, chunk.index) - - # 处理时间格式（可能是float秒数或字符串） - start_time = chunk.start_time - end_time = chunk.end_time - if isinstance(start_time, (int, float)): - start_time = self._format_time(start_time) - if isinstance(end_time, (int, float)): - end_time = self._format_time(end_time) - - chunk_summaries.append({ - 'index': chunk.index, - 'start_time': start_time, - 'end_time': end_time, - 'summary': summary - }) - - # 4. 合并所有段摘要 - print(f"[AIClient] 合并 {len(chunk_summaries)} 段分析结果") - final_result = self._merge_summaries(chunk_summaries, "video") - - # 5. 构建完整时间轴 - final_result['timeline'] = self._build_timeline_from_chunks(chunk_summaries) - - return final_result - - def _analyze_chunk(self, chunk_text: str, chunk_index: int) -> str: - """ - C5.3: 分析单个内容分片 - - Args: - chunk_text: 分片文本 - chunk_index: 分片索引 - - Returns: - 分片摘要（限制500字以内） - """ - # 截断文本避免过长 - text_sample = chunk_text[:3000] if len(chunk_text) > 3000 else chunk_text - - prompt = f"""请对以下视频片段进行简要分析（限制在300字以内）： - -片段内容： -{text_sample} - -请提供： -1. 这段的核心主题（50字以内） -2. 关键要点（3-5条） -3. 重要细节或引用 - -请用中文回答，控制在300字以内。""" - - try: - summary = self.quick_generate(prompt, max_tokens=500) - # 确保不超过500字 - if len(summary) > 500: - summary = summary[:497] + "..." - return summary - except Exception as e: - print(f"[AIClient] 分片 {chunk_index} 分析失败: {e}") - return f"第{chunk_index+1}段分析失败" - - def _merge_summaries(self, chunk_summaries: List[Dict], content_type: str) -> Dict[str, Any]: - """ - C5.3: 合并所有段摘要，生成完整分析报告 - - Args: - chunk_summaries: 各段摘要列表 - content_type: 内容类型 - - Returns: - 完整分析结果 - """ - # 构建合并提示 - summaries_text = "\n\n".join([ - f"=== 第{s['index']+1}段 ({s['start_time']}-{s['end_time']}) ===\n{s['summary']}" - for s in chunk_summaries - ]) - - prompt = f"""基于以下各段分析，生成完整的视频分析报告： - -{summaries_text} - -请提供以下格式的JSON： -{{ - "summary": "整体摘要（200字以内）", - "topics": ["主题1", "主题2", "主题3", "主题4"], - "insights": ["深入见解1", "深入见解2", "深入见解3"], - "actions": ["建议行动1", "建议行动2"], - "full_analysis": "详细分析（500字以内）" -}}""" - - try: - result = self.generate_json(prompt) - - # 确保必要字段存在 - if 'topics' not in result: - result['topics'] = ["主题A", "主题B", "主题C"] - if 'insights' not in result: - result['insights'] = ["重要见解1", "重要见解2"] - if 'actions' not in result: - result['actions'] = [] - if 'summary' not in result: - result['summary'] = "长视频分析完成" - if 'full_analysis' not in result: - result['full_analysis'] = summaries_text[:500] - - return result - - except Exception as e: - print(f"[AIClient] 合并摘要失败: {e}") - # 返回基础合并结果 - return { - 'summary': f'视频共分为{len(chunk_summaries)}个部分进行分析', - 'topics': [f"主题{i+1}" for i in range(min(4, len(chunk_summaries)))], - 'insights': ['从分段分析中综合得出'], - 'actions': [], - 'full_analysis': summaries_text[:800] - } - - def _analyze_direct(self, content_text: str) -> Dict[str, Any]: - """直接分析（不分段）""" - # 截断文本 - text_sample = content_text[:5000] if len(content_text) > 5000 else content_text - - prompt = f"""请分析以下内容，提供JSON格式结果： - -{text_sample} - -返回格式： -{{ - "summary": "整体摘要（150字以内）", - "topics": ["主题1", "主题2", "主题3"], - "insights": ["见解1", "见解2"], - "actions": ["行动建议1"], - "timeline": [ - {{"start_time": "00:00", "end_time": "05:00", "title": "第一部分", "summary": "..."}} - ] -}}""" - - try: - return self.generate_json(prompt) - except Exception as e: - print(f"[AIClient] 直接分析失败: {e}") - return { - 'summary': '分析失败', - 'topics': ['无法解析'], - 'insights': ['请检查内容格式'], - 'actions': [], - 'timeline': [] - } - - def _format_time(self, seconds: float) -> str: - """格式化秒数为时间字符串""" - hours = int(seconds // 3600) - minutes = int((seconds % 3600) // 60) - secs = int(seconds % 60) - if hours > 0: - return f"{hours:02d}:{minutes:02d}:{secs:02d}" - return f"{minutes:02d}:{secs:02d}" - - def _build_timeline_from_chunks(self, chunk_summaries: List[Dict]) -> List[Dict[str, Any]]: - """从分片摘要构建时间轴""" - timeline = [] - for summary in chunk_summaries: - timeline.append({ - 'index': summary['index'], - 'start_time': summary['start_time'], - 'end_time': summary['end_time'], - 'title': f"第{summary['index']+1}部分", - 'summary': summary['summary'][:200] + "..." if len(summary['summary']) > 200 else summary['summary'] - }) - return timeline - - def _mock_generate(self, prompt: str, max_tokens: int) -> str: - """模拟AI生成（实际实现应调用真实API）""" - # 根据prompt内容返回模拟响应 - prompt_lower = prompt.lower() - - if 'json' in prompt_lower or 'json格式' in prompt: - if '主题' in prompt or 'topic' in prompt_lower: - return json.dumps({ - 'summary': '这是一个关于人工智能和技术发展的内容，涵盖了多个重要主题。', - 'topics': ['人工智能', '机器学习', '未来发展', '技术趋势'], - 'insights': [ - 'AI正在改变我们的工作方式', - '机器学习应用越来越广泛', - '未来技术发展趋势值得关注' - ], - 'actions': ['深入研究相关技术', '关注行业动态'], - 'full_analysis': '详细分析内容...' - }, ensure_ascii=False) - return json.dumps({'result': '模拟JSON响应'}) - - if '摘要' in prompt or 'summary' in prompt_lower: - return '这是一段关于人工智能发展的重要讨论，涵盖了技术趋势和未来应用。' - - if '关键词' in prompt or 'keyword' in prompt_lower: - return '人工智能, 机器学习, 深度学习, 神经网络, 自然语言处理' - - return '模拟AI响应内容' - - def _parse_json_from_text(self, text: str) -> Dict[str, Any]: - """从文本中解析JSON""" - # 尝试提取JSON块 - import re - json_match = re.search(r'\{[\s\S]*\}', text) - if json_match: - try: - return json.loads(json_match.group()) - except: - pass - return {'raw_response': text} - - -class MultiAIClient: - """ - 多AI客户端 - 支持多种AI提供商的客户端 - """ - - def __init__(self): - self.config = get_config() - self.primary_client = AIClient() - self.clients = { - 'openai': self.primary_client, - 'local': AIClient(), # 可以配置为本地模型 - } - - def _deep_analyze(self, content_text: str, content_type: str, duration: Optional[int] = None) -> Dict[str, Any]: - """使用主客户端进行深度分析""" - return self.primary_client._deep_analyze(content_text, content_type, duration) - - def quick_generate(self, prompt: str, max_tokens: int = 100, provider: str = None) -> str: - """快速生成""" - client = self.clients.get(provider, self.primary_client) - return client.quick_generate(prompt, max_tokens) diff --git a/skills/kb-workflow/src/cli.py b/skills/kb-workflow/src/cli.py deleted file mode 100644 index 99adb8b..0000000 --- a/skills/kb-workflow/src/cli.py +++ /dev/null @@ -1,569 +0,0 @@ -#!/usr/bin/env python3 -"""Obsidian KB Knowledge Base CLI Tool - -Command line interface for managing Obsidian knowledge base content. -""" - -import argparse -import sys -import os -from typing import Optional -from datetime import datetime - -# Add parent directory to path for imports -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -try: - from src.config import Config - from src.storage import Storage - from src.pipeline import ProcessingPipeline - from src.models import ContentStatus - CONFIG_AVAILABLE = True -except ImportError as e: - print(f"Import warning: {e}", file=sys.stderr) - CONFIG_AVAILABLE = False - - -def print_header(text: str): - """Print formatted header.""" - print(f"\n{'=' * 50}") - print(f" {text}") - print(f"{'=' * 50}\n") - - -def print_table(headers: list, rows: list, max_width: int = 80): - """Print a simple formatted table.""" - if not rows: - print("(No data)") - return - - # Calculate column widths - col_widths = [len(h) for h in headers] - for row in rows: - for i, cell in enumerate(row): - if i < len(col_widths): - col_widths[i] = max(col_widths[i], len(str(cell)[:max_width])) - - # Print headers - header_line = " | ".join(h.ljust(w) for h, w in zip(headers, col_widths)) - print(header_line) - print("-" * len(header_line)) - - # Print rows - for row in rows: - row_str = " | ".join(str(cell)[:max_width].ljust(w) for cell, w in zip(row, col_widths)) - print(row_str) - print() - - -def cmd_subscribe(args: argparse.Namespace): - """Handle subscribe command - save subscription configuration.""" - print_header("Subscribe Configuration") - - if not CONFIG_AVAILABLE: - print("Error: Config module not available. Please check installation.") - return 1 - - config = Config() - - # Build subscription entry - subscription = { - 'platform': args.platform, - 'type': args.type, - 'id': args.id, - 'name': args.name or f"{args.platform}_{args.type}_{args.id}", - 'created_at': datetime.now().isoformat() - } - - # Load existing subscriptions - subscriptions = config.get('subscriptions', []) - - # Check for duplicates - for sub in subscriptions: - if sub['platform'] == args.platform and sub['id'] == args.id: - print(f"⚠️ Subscription already exists: {sub['name']}") - print(" Updating configuration...") - sub.update(subscription) - break - else: - subscriptions.append(subscription) - print(f"✅ Added new subscription: {subscription['name']}") - - # Save configuration - config.set('subscriptions', subscriptions) - config.save() - - print(f"\n Platform: {args.platform}") - print(f" Type: {args.type}") - print(f" ID: {args.id}") - print(f" Name: {subscription['name']}") - print(f"\n💾 Configuration saved to config.yaml") - - return 0 - - -def cmd_inbox(args: argparse.Namespace): - """Handle inbox command - display pending items.""" - print_header("Inbox Contents") - - if not CONFIG_AVAILABLE: - print("Error: Storage module not available.") - return 1 - - storage = Storage() - # Filter for CONFIRMED status (ready to process) - contents = storage.list_inbox(status=ContentStatus.CONFIRMED) - - if not contents: - print("📭 Inbox is empty!") - return 0 - - # Sort by the requested criteria - if args.sort == 'score': - contents.sort(key=lambda x: x.quality_metrics.overall_score if hasattr(x, 'quality_metrics') else 0, reverse=True) - else: # date - contents.sort(key=lambda x: x.created_at, reverse=True) - - # Limit results - contents = contents[:args.limit] - - print(f"Showing {len(contents)} item(s) (sorted by {args.sort}):\n") - - # Format table rows - rows = [] - for idx, content in enumerate(contents, 1): - title = content.title[:40] if content.title else 'Untitled' - score = content.quality_metrics.overall_score if hasattr(content, 'quality_metrics') else 0 - summary = content.summary[:50] if content.summary else 'No summary' - content_id = content.id - - rows.append([ - f"{idx:2d}", - content_id[:12], - f"{score:.1f}", - title, - summary + "..." if len(content.summary or '') > 50 else summary - ]) - - print_table( - ["#", "ID", "Score", "Title", "Summary"], - rows - ) - - print(f"\n💡 Use 'process ' to process an item, or 'process all' to process all") - - return 0 - - -def cmd_process(args: argparse.Namespace): - """Handle process command - process content items.""" - print_header("Process Content") - - if not CONFIG_AVAILABLE: - print("Error: Pipeline module not available.") - return 1 - - storage = Storage() - - # Import pipeline components - try: - from src.pipeline import ProcessingPipeline - from src.light_scanner import LightScanner - from src.deep_analyzer import DeepAnalyzer - from src.config import get_config - - config = get_config() - pipeline = ProcessingPipeline() - except Exception as e: - print(f"❌ Error initializing pipeline: {e}") - return 1 - - # Determine what to process - if args.content_id == 'all': - items = storage.list_inbox(status=ContentStatus.CONFIRMED) - if not items: - print("📭 Inbox is empty! Nothing to process.") - return 0 - - print(f"Found {len(items)} item(s) to process.\n") - - if not args.yes: - confirm = input("Process all items? [Y/n]: ").strip().lower() - if confirm and confirm not in ('y', 'yes'): - print("❌ Cancelled.") - return 0 - else: - item = storage.load_content(args.content_id) - if not item: - print(f"❌ Content not found: {args.content_id}") - return 1 - items = [item] - print(f"Processing single item: {item.title}\n") - - # Process items with progress - success_count = 0 - fail_count = 0 - - for i, item in enumerate(items, 1): - content_id = item.id - title = item.title or 'Untitled' - - print(f"[{i}/{len(items)}] Processing: {title[:50]}...") - print(f" ID: {content_id}") - - try: - # Use the pipeline to process the content - # The pipeline expects content_text - we'll use transcript or summary - content_text = item.transcript or item.summary or "" - - result = pipeline.process_full(item, content_text) - - # Save the updated content - storage.save_content(result) - - print(f" ✅ Success") - print(f" 📄 Note: {result.output_path or 'N/A'}") - print(f" 🏷️ Tags: {', '.join(result.tags)}") - success_count += 1 - - except Exception as e: - print(f" ❌ Error: {e}") - import traceback - traceback.print_exc() - fail_count += 1 - - print() - - # Summary - print(f"\n📊 Processing Complete") - print(f" Successful: {success_count}") - print(f" Failed: {fail_count}") - print(f" Total: {len(items)}") - - return 0 if fail_count == 0 else 1 - - -def cmd_skip(args: argparse.Namespace): - """Handle skip command - skip/remove an item from inbox.""" - print_header("Skip Content") - - if not CONFIG_AVAILABLE: - print("Error: Storage module not available.") - return 1 - - storage = Storage() - - # Find the item - content = storage.load_content(args.content_id) - if not content: - print(f"❌ Content not found: {args.content_id}") - return 1 - - print(f"Skipping: {content.title or 'Untitled'}") - print(f"ID: {args.content_id}") - - if args.reason: - print(f"Reason: {args.reason}") - - # Mark as skipped - content.status = ContentStatus.SKIPPED - if args.reason: - content.summary = f"[Skipped: {args.reason}] {content.summary or ''}" - - storage.save_content(content) - - print("✅ Item skipped and marked as skipped.") - if args.reason: - print(f" Reason recorded: {args.reason}") - - return 0 - - -def cmd_status(args: argparse.Namespace): - """Handle status command - show system status.""" - print_header("System Status") - - # Git status - print("📁 Git Repository Status") - print("-" * 30) - - import subprocess - try: - # Check if we're in a git repo - result = subprocess.run( - ['git', 'status', '--short'], - capture_output=True, - text=True, - cwd=os.getcwd() - ) - - if result.returncode == 0: - if result.stdout.strip(): - print("Uncommitted changes:") - print(result.stdout) - else: - print("✅ Working directory clean") - - # Get branch info - branch_result = subprocess.run( - ['git', 'branch', '--show-current'], - capture_output=True, - text=True - ) - if branch_result.returncode == 0: - print(f"Branch: {branch_result.stdout.strip()}") - else: - print("⚠️ Not a git repository or git not available") - except FileNotFoundError: - print("⚠️ Git not available") - except Exception as e: - print(f"⚠️ Git error: {e}") - - print() - - # Inbox statistics - print("📥 Inbox Statistics") - print("-" * 30) - - if CONFIG_AVAILABLE: - storage = Storage() - stats = storage.get_stats() - - print(f"Pending (confirmed): {stats.get(ContentStatus.CONFIRMED.value, 0)}") - print(f"New: {stats.get(ContentStatus.NEW.value, 0)}") - print(f"Scanned: {stats.get(ContentStatus.SCANNED.value, 0)}") - print(f"Analyzed: {stats.get(ContentStatus.ANALYZED.value, 0)}") - print(f"Generated: {stats.get(ContentStatus.GENERATED.value, 0)}") - print(f"Skipped: {stats.get(ContentStatus.SKIPPED.value, 0)}") - else: - print("⚠️ Storage module not available") - - print() - - # Configuration summary - print("⚙️ Configuration") - print("-" * 30) - - if CONFIG_AVAILABLE: - config = Config() - subscriptions = config.get('subscriptions', []) - print(f"Active subscriptions: {len(subscriptions)}") - - if subscriptions: - for sub in subscriptions: - print(f" • {sub.get('name', 'Unnamed')} ({sub.get('platform')})") - else: - print("⚠️ Config module not available") - - return 0 - - -def cmd_search(args: argparse.Namespace): - """Handle search command - search processed notes.""" - print_header(f"Search: '{args.query}'") - - if not CONFIG_AVAILABLE: - print("Error: Storage module not available.") - return 1 - - storage = Storage() - - # Perform search across all content - results = storage.search(args.query) - - if not results: - print("🔍 No results found.") - return 0 - - # Filter by tags if requested - if args.in_tags: - query_lower = args.query.lower() - results = [r for r in results if any(query_lower in tag.lower() for tag in r.tags)] - - if not results: - print("🔍 No results found in tags.") - return 0 - - print(f"Found {len(results)} result(s):\n") - - # Format results - for i, content in enumerate(results, 1): - title = content.title or 'Untitled' - path = content.output_path or f"inbox/{content.id}.json" - tags = content.tags - - print(f"{i}. {title}") - print(f" ID: {content.id}") - print(f" Status: {content.status.value}") - print(f" Path: {path}") - if tags: - print(f" Tags: {', '.join(tags)}") - - # Show matching content preview - if not args.in_tags and content.summary: - content_text = content.summary - query_lower = args.query.lower() - idx = content_text.lower().find(query_lower) - if idx >= 0: - start = max(0, idx - 30) - end = min(len(content_text), idx + len(args.query) + 30) - preview = content_text[start:end] - if start > 0: - preview = "..." + preview - if end < len(content_text): - preview = preview + "..." - print(f" Match: {preview}") - - print() - - return 0 - - -def main(): - """Main entry point for CLI.""" - parser = argparse.ArgumentParser( - description='Obsidian KB 知识库维护工具', - prog='obsidian-kb', - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - %(prog)s subscribe bilibili fav 12345 --name="Tech Favorites" - %(prog)s inbox --limit 10 --sort date - %(prog)s process all --yes - %(prog)s process abc123 - %(prog)s skip abc123 --reason="Low quality" - %(prog)s status - %(prog)s search "python tutorial" --in-tags - """ - ) - - subparsers = parser.add_subparsers(dest='command', help='Available commands') - - # subscribe 子命令 - subscribe_parser = subparsers.add_parser( - 'subscribe', - aliases=['sub'], - help='Subscribe to a content source' - ) - subscribe_parser.add_argument( - 'platform', - choices=['bilibili', 'youtube', 'zhihu'], - help='Platform to subscribe to' - ) - subscribe_parser.add_argument( - 'type', - choices=['fav', 'up', 'channel'], - help='Subscription type' - ) - subscribe_parser.add_argument( - 'id', - help='Platform-specific ID (user ID, channel ID, etc.)' - ) - subscribe_parser.add_argument( - '--name', - help='Friendly name for this subscription' - ) - subscribe_parser.set_defaults(func=cmd_subscribe) - - # inbox 子命令 - inbox_parser = subparsers.add_parser( - 'inbox', - aliases=['i'], - help='Show inbox contents' - ) - inbox_parser.add_argument( - '--limit', - type=int, - default=20, - help='Maximum number of items to show (default: 20)' - ) - inbox_parser.add_argument( - '--sort', - choices=['score', 'date'], - default='score', - help='Sort order (default: score)' - ) - inbox_parser.set_defaults(func=cmd_inbox) - - # process 子命令 - process_parser = subparsers.add_parser( - 'process', - aliases=['p'], - help='Process content items' - ) - process_parser.add_argument( - 'content_id', - nargs='?', - default='all', - help='Content ID to process, or "all" for all items (default: all)' - ) - process_parser.add_argument( - '--yes', - action='store_true', - help='Auto-confirm without prompting' - ) - process_parser.set_defaults(func=cmd_process) - - # skip 子命令 - skip_parser = subparsers.add_parser( - 'skip', - aliases=['s'], - help='Skip an item (remove from inbox)' - ) - skip_parser.add_argument( - 'content_id', - help='Content ID to skip' - ) - skip_parser.add_argument( - '--reason', - help='Reason for skipping' - ) - skip_parser.set_defaults(func=cmd_skip) - - # status 子命令 - status_parser = subparsers.add_parser( - 'status', - aliases=['st'], - help='Show system status' - ) - status_parser.set_defaults(func=cmd_status) - - # search 子命令 - search_parser = subparsers.add_parser( - 'search', - aliases=['q'], - help='Search processed notes' - ) - search_parser.add_argument( - 'query', - help='Search query' - ) - search_parser.add_argument( - '--in-tags', - action='store_true', - help='Search only in tags' - ) - search_parser.set_defaults(func=cmd_search) - - # Parse arguments - args = parser.parse_args() - - # Execute command - if args.command is None: - parser.print_help() - return 0 - - try: - return args.func(args) or 0 - except KeyboardInterrupt: - print("\n\n⚠️ Interrupted by user") - return 130 - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() - return 1 - - -if __name__ == '__main__': - sys.exit(main()) diff --git a/skills/kb-workflow/src/config.py b/skills/kb-workflow/src/config.py deleted file mode 100644 index 327e7ec..0000000 --- a/skills/kb-workflow/src/config.py +++ /dev/null @@ -1,353 +0,0 @@ -""" -Configuration management for Obsidian KB Skill. -""" -import os -import yaml -from pathlib import Path -from typing import Optional, Dict, Any - - -# Global config instance -_config_instance: Optional['Config'] = None - - -def get_config(config_path: Optional[str] = None) -> 'Config': - """Get global config instance.""" - global _config_instance - if _config_instance is None or config_path: - _config_instance = Config(config_path) - return _config_instance - - -class Config: - """Configuration manager.""" - - DEFAULT_CONFIG = { - 'ai': { - 'provider': 'openai', - 'model': 'gpt-4', - 'api_key': None, - }, - 'storage': { - 'inbox_dir': 'inbox', - 'topics_dir': 'topics', - 'data_dir': 'data', - }, - 'git': { - 'enabled': False, - 'vault_path': '~/Documents/Obsidian/KB', - 'remote': 'origin', - 'branch': 'main', - 'auto_push': False, - 'commit_message_template': 'Add note: {title}', - }, - 'obsidian': { - 'vault_path': '~/Documents/Obsidian/KB', - 'notes_folder': 'Notes', - }, - 'chunking': { - 'enabled': True, - 'chunk_duration_seconds': 900, # 15 minutes - 'max_chunk_duration_seconds': 1800, # 30 minutes - 'long_video_threshold': 1800, # 30 minutes - }, - 'output': { - 'organize_by': 'author', # author | date | topic - 'filename_format': '{date}_{title}.md', - 'auto_tags': True, - 'default_tags': ['video', 'bilibili'], - }, - 'sources': { - 'bilibili': { - 'enabled': True, - 'cookie': None, - 'extraction': { - 'headless': True, - 'timeout': 30, - }, - }, - 'youtube': { - 'enabled': False, - }, - 'zhihu': { - 'enabled': False, - }, - }, - } - - def __init__(self, config_path: Optional[str] = None, load_env: bool = True): - """ - Initialize configuration. - - Args: - config_path: Path to config file. If None, uses default locations. - load_env: Whether to load environment variable overrides. - """ - self.config_path = config_path or self._find_config_file() - self._config = self._load_config(load_env) - - def _find_config_file(self) -> str: - """Find configuration file in standard locations.""" - search_paths = [ - Path.cwd() / 'config.yaml', - Path.cwd() / 'config.yml', - Path.home() / '.obsidian-kb' / 'config.yaml', - Path.home() / '.config' / 'obsidian-kb' / 'config.yaml', - ] - - for path in search_paths: - if path.exists(): - return str(path) - - # Return default path - return str(Path.cwd() / 'config.yaml') - - def _load_config(self, load_env: bool = True) -> Dict[str, Any]: - """Load configuration from file or use defaults.""" - config = self.DEFAULT_CONFIG.copy() - - if Path(self.config_path).exists(): - with open(self.config_path, 'r', encoding='utf-8') as f: - user_config = yaml.safe_load(f) - if user_config: - self._deep_update(config, user_config) - - # Override with environment variables - if load_env: - self._apply_env_overrides(config) - - return config - - def _deep_update(self, base: Dict, update: Dict) -> None: - """Deep update dictionary.""" - for key, value in update.items(): - if key in base and isinstance(base[key], dict) and isinstance(value, dict): - self._deep_update(base[key], value) - else: - base[key] = value - - def _apply_env_overrides(self, config: Dict[str, Any]) -> None: - """Apply environment variable overrides.""" - # AI settings - if os.getenv('OPENAI_API_KEY'): - config['ai']['api_key'] = os.getenv('OPENAI_API_KEY') - if os.getenv('AI_MODEL'): - config['ai']['model'] = os.getenv('AI_MODEL') - - # Git settings - if os.getenv('OBSIDIAN_VAULT_PATH'): - config['git']['vault_path'] = os.getenv('OBSIDIAN_VAULT_PATH') - - # Bilibili cookie - if os.getenv('BILIBILI_COOKIE'): - config['sources']['bilibili']['cookie'] = os.getenv('BILIBILI_COOKIE') - - def get(self, key: str, default: Any = None) -> Any: - """ - Get configuration value using dot notation. - - Args: - key: Key in dot notation (e.g., 'ai.model') - default: Default value if key not found - - Returns: - Configuration value - """ - keys = key.split('.') - value = self._config - - for k in keys: - if isinstance(value, dict) and k in value: - value = value[k] - else: - return default - - return value - - def set(self, key: str, value: Any) -> None: - """ - Set configuration value using dot notation. - - Args: - key: Key in dot notation - value: Value to set - """ - keys = key.split('.') - config = self._config - - for k in keys[:-1]: - if k not in config: - config[k] = {} - config = config[k] - - config[keys[-1]] = value - - def save(self) -> None: - """Save configuration to file.""" - Path(self.config_path).parent.mkdir(parents=True, exist_ok=True) - with open(self.config_path, 'w', encoding='utf-8') as f: - yaml.dump(self._config, f, default_flow_style=False, allow_unicode=True) - - @property - def obsidian_path(self) -> str: - """Get obsidian path (alias for vault_path).""" - return str(self.vault_path) - - @property - def vault_path(self) -> Path: - """Get Obsidian vault path.""" - path = self.get('obsidian.vault_path') or self.get('git.vault_path', '~/Documents/Obsidian/KB') - return Path(path).expanduser() - - @property - def notes_folder(self) -> str: - """Get notes folder.""" - return self.get('obsidian.notes_folder', 'Notes') - - @property - def ai_api_key(self) -> Optional[str]: - """Get AI API key.""" - return self.get('ai.api_key') - - @property - def ai_model(self) -> str: - """Get AI model.""" - return self.get('ai.model', 'gpt-4') - - @property - def chunking_enabled(self) -> bool: - """Check if chunking is enabled.""" - return self.get('chunking.enabled', True) - - @property - def chunk_duration(self) -> int: - """Get chunk duration in seconds.""" - return self.get('chunking.chunk_duration_seconds', 900) - - @property - def long_video_threshold(self) -> int: - """Get long video threshold in seconds.""" - return self.get('chunking.long_video_threshold', 1800) - - @property - def quality_threshold(self) -> float: - """Get quality threshold.""" - return self.get('quality_threshold', 0.7) - - @property - def obsidian(self) -> 'Config': - """Get obsidian config section (returns self for compatibility).""" - return self - - @property - def git(self) -> 'Config': - """Get git config section (returns self for compatibility).""" - return self - - @property - def enabled(self) -> bool: - """Check if git is enabled.""" - return self.get('git.enabled', False) - - @property - def auto_push(self) -> bool: - """Check if auto push is enabled.""" - return self.get('git.auto_push', False) - - @property - def commit_message_template(self) -> str: - """Get commit message template.""" - return self.get('git.commit_message_template', 'Add note: {title}') - - @property - def kimi_api_key(self) -> str: - """Get Kimi API key (alias for ai.api_key).""" - return self.get('ai.api_key') or '' - - # Output configuration - @property - def organize_by(self) -> str: - """Get output organize_by strategy.""" - return self.get('output.organize_by', 'author') - - @property - def filename_format(self) -> str: - """Get output filename format.""" - return self.get('output.filename_format', '{date}_{title}.md') - - @property - def auto_tags(self) -> bool: - """Get auto_tags setting.""" - return self.get('output.auto_tags', True) - - @property - def default_tags(self) -> list: - """Get default tags list.""" - return self.get('output.default_tags', ['video', 'bilibili']) - - # Bilibili extraction configuration - @property - def bilibili_headless(self) -> bool: - """Get bilibili extraction headless mode.""" - return self.get('sources.bilibili.extraction.headless', True) - - @property - def bilibili_timeout(self) -> int: - """Get bilibili extraction timeout in seconds.""" - return self.get('sources.bilibili.extraction.timeout', 30) - - # Zhihu DrissionPage configuration - @property - def zhihu_enabled(self) -> bool: - """知乎是否启用""" - return self.get('sources.zhihu.enabled', False) - - @property - def zhihu_use_drissionpage(self) -> bool: - """知乎是否使用 DrissionPage""" - return self.get('sources.zhihu.use_drissionpage', True) - - @property - def zhihu_cookie_file(self) -> Optional[str]: - """知乎 Cookie 文件路径""" - return self.get('sources.zhihu.cookie_file') - - @property - def zhihu_cookie_str(self) -> Optional[str]: - """知乎 Cookie 字符串""" - return self.get('sources.zhihu.cookie_str') - - @property - def zhihu_timeout(self) -> int: - """知乎提取超时时间（秒）""" - return self.get('sources.zhihu.timeout', 30) - - @property - def zhihu_headless(self) -> bool: - """知乎是否使用无头浏览器""" - return self.get('sources.zhihu.headless', True) - - def ensure_dirs(self) -> None: - """Ensure required directories exist.""" - self.vault_path.mkdir(parents=True, exist_ok=True) - (self.vault_path / self.get('storage.inbox_dir', 'Inbox')).mkdir(exist_ok=True) - (self.vault_path / self.get('storage.topics_dir', 'Topics')).mkdir(exist_ok=True) - - def validate(self) -> tuple[bool, list[str]]: - """Validate configuration.""" - errors = [] - if not self.ai_api_key: - errors.append("AI API key not configured") - return len(errors) == 0, errors - - # Subscriptions support - def get_subscriptions(self) -> list: - """Get subscriptions list.""" - return self.get('subscriptions', []) - - def add_subscription(self, subscription: Dict[str, Any]) -> None: - """Add a new subscription.""" - subs = self.get('subscriptions', []) - subs.append(subscription) - self.set('subscriptions', subs) - self.save() diff --git a/skills/kb-workflow/src/content_chunker.py b/skills/kb-workflow/src/content_chunker.py deleted file mode 100644 index 4b64d24..0000000 --- a/skills/kb-workflow/src/content_chunker.py +++ /dev/null @@ -1,486 +0,0 @@ -"""Obsidian KB - 内容分片器 -C5.1-C5.2: 实现长视频分段和分段策略 -""" -import re -from dataclasses import dataclass -from typing import List, Dict, Tuple, Optional, Any - - -@dataclass -class Chunk: - """内容分片数据模型""" - index: int - start_time: float # seconds - end_time: float # seconds - text: str - - @property - def duration(self) -> float: - """Get chunk duration in seconds.""" - return self.end_time - self.start_time - - @property - def time_range(self) -> str: - """Get formatted time range string.""" - return f"{self._format_time(self.start_time)}-{self._format_time(self.end_time)}" - - def _format_time(self, seconds: float) -> str: - """Format seconds to MM:SS or HH:MM:SS.""" - hours = int(seconds // 3600) - minutes = int((seconds % 3600) // 60) - secs = int(seconds % 60) - - if hours > 0: - return f"{hours:02d}:{minutes:02d}:{secs:02d}" - return f"{minutes:02d}:{secs:02d}" - - def to_dict(self) -> Dict: - """转换为字典""" - return { - 'index': self.index, - 'start_time': self.start_time, - 'end_time': self.end_time, - 'text_length': len(self.text), - 'text_preview': self.text[:100] + '...' if len(self.text) > 100 else self.text - } - - def __repr__(self) -> str: - return f"Chunk({self.index}, {self.start_time}-{self.end_time}, {len(self.text)}chars)" - - -class ContentChunker: - """内容分片器 - 支持SRT字幕分片""" - - def __init__(self, chunk_duration_seconds: int = 900, max_chunk_duration: int = 1800): - """ - Initialize chunker. - - Args: - chunk_duration_seconds: Target duration for each chunk (default 15 min) - max_chunk_duration: Maximum duration for a chunk (default 30 min) - """ - self.chunk_duration = chunk_duration_seconds - self.max_chunk_duration = max_chunk_duration - - # SRT时间戳正则 - self.srt_time_pattern = re.compile( - r'(\d{2}:\d{2}:\d{2},\d{3})\s*-->\s*(\d{2}:\d{2}:\d{2},\d{3})' - ) - - def chunk_from_extractor(self, extractor_data: Dict[str, Any]) -> List[Chunk]: - """ - 从提取器数据结构接收输入并进行分片 - - Args: - extractor_data: 提取器返回的数据结构，包含： - - metadata: Dict[str, Any] - 元数据（包含时长等） - - stats: Dict[str, Any] - 统计信息 - - subtitle: str - 字幕/文本内容 - - description: str - 内容描述 - - Returns: - Chunk对象列表 - """ - metadata = extractor_data.get('metadata', {}) - subtitle = extractor_data.get('subtitle', '') - description = extractor_data.get('description', '') - - # 检测是否有字幕 - has_subtitle = bool(subtitle and subtitle.strip()) - - if not has_subtitle: - # 无字幕：返回空列表或单块（description） - print("[ContentChunker] 无字幕，返回空分片列表") - return [] - - # 获取时长 - duration = metadata.get('duration', 0) - print(f"[ContentChunker] 有字幕，时长={duration}秒") - - # 使用原有分段逻辑 - return self._auto_chunk(subtitle, duration) - - def _auto_chunk(self, subtitle_text: str, duration: int) -> List[Chunk]: - """ - 自动分片（内部方法） - - Args: - subtitle_text: 字幕文本 - duration: 视频时长（秒） - - Returns: - Chunk对象列表 - """ - # 使用原有的 chunk_content 逻辑 - return chunk_content(subtitle_text, duration) - - def chunk_by_duration(self, srt_content: str, target_duration: Optional[int] = None) -> List[Chunk]: - """ - Split SRT content into chunks based on duration. - - Args: - srt_content: SRT formatted subtitle text - target_duration: Target chunk duration in seconds (overrides default) - - Returns: - List of Chunk objects - """ - target = target_duration or self.chunk_duration - subtitles = self._parse_srt(srt_content) - - if not subtitles: - return [] - - chunks = [] - current_chunk_texts = [] - current_start = subtitles[0][0] - current_end = current_start - chunk_index = 0 - - for start, end, text in subtitles: - # Check if adding this subtitle would exceed target duration - if current_chunk_texts and (end - current_start) > target: - # Save current chunk - chunks.append(Chunk( - index=chunk_index, - start_time=current_start, - end_time=current_end, - text=' '.join(current_chunk_texts) - )) - chunk_index += 1 - - # Start new chunk - current_chunk_texts = [text] - current_start = start - current_end = end - else: - current_chunk_texts.append(text) - current_end = end - - # Don't forget the last chunk - if current_chunk_texts: - chunks.append(Chunk( - index=chunk_index, - start_time=current_start, - end_time=current_end, - text=' '.join(current_chunk_texts) - )) - - return chunks - - def _parse_srt(self, srt_content: str) -> List[Tuple[float, float, str]]: - """ - Parse SRT content into list of (start_time, end_time, text) tuples. - - Args: - srt_content: SRT formatted text - - Returns: - List of tuples (start_seconds, end_seconds, text) - """ - import re - subtitles = [] - - # Split by double newlines to get individual subtitle entries - entries = re.split(r'\n\s*\n', srt_content.strip()) - - for entry in entries: - lines = entry.strip().split('\n') - if len(lines) < 2: - continue - - # Find the timing line (contains -->) - timing_line = None - for line in lines: - if '-->' in line: - timing_line = line - break - - if not timing_line: - continue - - # Parse timing - match = re.match( - r'\d*\s*(\d{2}:\d{2}:\d{2},\d{3})\s*--\u003e\s*(\d{2}:\d{2}:\d{2},\d{3})', - timing_line.strip() - ) - if not match: - continue - - start_time = self._time_to_seconds(match.group(1)) - end_time = self._time_to_seconds(match.group(2)) - - # Get text (lines after timing line) - text_lines = [] - found_timing = False - for line in lines: - if found_timing: - text_lines.append(line.strip()) - elif '-->' in line: - found_timing = True - - text = ' '.join(text_lines).strip() - if text: - subtitles.append((start_time, end_time, text)) - - return subtitles - - def chunk_by_count(self, srt_text: str, num_chunks: int) -> List[Chunk]: - """ - 按指定段数分割SRT字幕 - - Args: - srt_text: SRT格式字幕文本 - num_chunks: 目标段数 - - Returns: - Chunk对象列表 - """ - print(f"[ContentChunker] 按段数分片: 目标段数={num_chunks}") - - # 解析SRT条目 - entries = self._parse_srt_dict(srt_text) - if not entries: - return [] - - if num_chunks <= 0: - num_chunks = 1 - - # 计算每段应包含的条目数 - total_entries = len(entries) - entries_per_chunk = max(1, total_entries // num_chunks) - - chunks = [] - current_chunk_texts = [] - current_start_time = None - chunk_index = 0 - entry_count = 0 - - for i, entry in enumerate(entries): - if current_start_time is None: - current_start_time = entry['start'] - - current_chunk_texts.append(entry['text']) - entry_count += 1 - - # 检查是否达到每段条目数，或是最后一个条目 - is_last_entry = (i == total_entries - 1) - should_split = (entry_count >= entries_per_chunk and chunk_index < num_chunks - 1) - - if should_split or is_last_entry: - chunk = Chunk( - index=chunk_index, - start_time=self._time_to_seconds(current_start_time), - end_time=self._time_to_seconds(entry['end']), - text='\n'.join(current_chunk_texts) - ) - chunks.append(chunk) - - current_chunk_texts = [] - current_start_time = None - chunk_index += 1 - entry_count = 0 - - print(f"[ContentChunker] 分片完成: 共{len(chunks)}段") - return chunks - - def _parse_srt_dict(self, srt_text: str) -> List[Dict]: - """ - 解析SRT字幕文本，返回字典格式（用于内部使用） - - Returns: - 条目列表，每个条目包含start, end, text - """ - entries = [] - - # 首先检查是否包含SRT时间戳格式 - if '-->' not in srt_text: - # 非SRT格式，按行数分割模拟 - lines = srt_text.strip().split('\n') - # 模拟每10行一个条目，每个条目30秒 - for i in range(0, len(lines), 10): - chunk_lines = lines[i:i+10] - start_sec = i * 3 # 每行约3秒 - end_sec = (i + 10) * 3 - entries.append({ - 'start': self._seconds_to_time(start_sec), - 'end': self._seconds_to_time(end_sec), - 'text': '\n'.join(chunk_lines) - }) - return entries - - # 按空行分割条目 - blocks = re.split(r'\n\s*\n', srt_text.strip()) - - for block in blocks: - lines = block.strip().split('\n') - if len(lines) < 2: - continue - - # 查找时间戳行（通常是第二行，但可能有变体） - time_line = None - for line in lines: - if '-->' in line: - time_line = line - break - - if not time_line: - continue - - # 解析时间戳 - match = self.srt_time_pattern.search(time_line) - if not match: - continue - - start_time = match.group(1) - end_time = match.group(2) - - # 提取文本（时间戳行之后的所有行） - time_line_idx = lines.index(time_line) if time_line in lines else 1 - text_lines = lines[time_line_idx + 1:] - text = '\n'.join(text_lines).strip() - - if text: - entries.append({ - 'start': start_time, - 'end': end_time, - 'text': text - }) - - return entries - - def _time_to_seconds(self, time_str: str) -> float: - """将SRT时间格式转换为秒数""" - # 处理 "00:05:23,450" 格式 - time_str = time_str.replace(',', '.') - parts = time_str.split(':') - - if len(parts) == 3: - hours = int(parts[0]) - minutes = int(parts[1]) - seconds = float(parts[2]) - return hours * 3600 + minutes * 60 + seconds - - return 0.0 - - def _seconds_to_time(self, seconds: int) -> str: - """将秒数转换为SRT时间格式""" - hours = seconds // 3600 - minutes = (seconds % 3600) // 60 - secs = seconds % 60 - return f"{hours:02d}:{minutes:02d}:{secs:02d},000" - - def get_chunking_strategy(self, total_duration: float) -> dict: - """ - Determine chunking strategy based on total content duration. - - Args: - total_duration: Total duration in seconds - - Returns: - Dictionary with chunking strategy - """ - if total_duration < 600: # Less than 10 minutes - return { - 'strategy': 'direct', - 'chunk_count': 1, - 'description': 'Short content, no chunking needed' - } - elif total_duration < 1800: # 10-30 minutes - return { - 'strategy': 'truncate', - 'chunk_count': 1, - 'description': 'Medium content, single chunk with possible truncation' - } - elif total_duration < 3600: # 30-60 minutes - return { - 'strategy': 'two_part', - 'chunk_count': 2, - 'description': 'Long content, split into 2 chunks' - } - else: # 60+ minutes - chunk_count = int(total_duration / self.chunk_duration) + 1 - return { - 'strategy': 'multi_part', - 'chunk_count': chunk_count, - 'description': f'Very long content, split into {chunk_count} chunks' - } - - -def get_chunking_strategy(duration_seconds: int) -> Dict: - """ - C5.2: 根据视频时长获取分段策略 - - Args: - duration_seconds: 视频时长（秒） - - Returns: - 分段策略字典 - """ - if duration_seconds < 600: - # 少于10分钟：直接处理，无需分片 - return { - 'use_chunking': False, - 'strategy': 'direct', - 'description': '短视频，直接处理' - } - - elif duration_seconds < 1800: - # 10-30分钟：截断处理 - return { - 'use_chunking': False, - 'strategy': 'truncate', - 'max_chars': 8000, - 'description': '中等视频，截断至8000字符处理' - } - - elif duration_seconds < 3600: - # 30-60分钟：分为2段 - return { - 'use_chunking': True, - 'num_chunks': 2, - 'strategy': 'chunk_2', - 'chunk_duration': duration_seconds // 2, - 'description': '长视频，分为2段处理' - } - - else: - # 60分钟以上：每15分钟一段 - num_chunks = max(4, duration_seconds // 900) - return { - 'use_chunking': True, - 'chunk_duration': 900, - 'num_chunks': num_chunks, - 'strategy': 'chunk_n', - 'description': f'超长视频，分为{num_chunks}段，每段约15分钟' - } - - -# 便捷函数 -def chunk_content(srt_text: str, duration_seconds: int) -> List[Chunk]: - """ - 根据视频时长自动选择最佳分片策略 - - Args: - srt_text: SRT字幕文本 - duration_seconds: 视频时长（秒） - - Returns: - Chunk列表 - """ - strategy = get_chunking_strategy(duration_seconds) - chunker = ContentChunker() - - if not strategy['use_chunking']: - # 不需要分片，返回单一大段 - return [Chunk( - text=srt_text[:strategy.get('max_chars', len(srt_text))], - start_time="00:00:00,000", - end_time=chunker._seconds_to_time(duration_seconds), - index=0 - )] - - if 'num_chunks' in strategy and strategy['strategy'] == 'chunk_2': - return chunker.chunk_by_count(srt_text, strategy['num_chunks']) - else: - return chunker.chunk_by_duration(srt_text, strategy.get('chunk_duration', 900)) diff --git a/skills/kb-workflow/src/deep_analyzer.py b/skills/kb-workflow/src/deep_analyzer.py deleted file mode 100644 index 5b5dcb0..0000000 --- a/skills/kb-workflow/src/deep_analyzer.py +++ /dev/null @@ -1,438 +0,0 @@ -"""Obsidian KB - 深度分析器 -C4.2: 实现深度分析，根据时长选择不同分析策略 -""" -from typing import Dict, Any, List, Optional -from src.models import Content, AnalysisResult, ContentStatus, TimelineEntry -from src.config import get_config - - -class DeepAnalyzer: - """深度内容分析器 - 根据内容时长选择不同策略""" - - # 时长阈值（秒） - SHORT_VIDEO_THRESHOLD = 600 # 10分钟 - LONG_VIDEO_THRESHOLD = 1800 # 30分钟 - - def __init__(self, ai_client=None): - self.config = get_config() - self.ai_client = ai_client - - def analyze(self, extractor_data: Dict[str, Any]) -> AnalysisResult: - """ - 根据内容时长选择分析策略（从提取器数据接收输入） - - Args: - extractor_data: 提取器返回的数据结构，包含： - - metadata: Dict[str, Any] - 元数据（标题、作者、时长等） - - stats: Dict[str, Any] - 统计信息 - - subtitle: str - 字幕/文本内容 - - description: str - 内容描述 - - Returns: - AnalysisResult分析结果 - """ - metadata = extractor_data.get('metadata', {}) - subtitle = extractor_data.get('subtitle', '') - description = extractor_data.get('description', '') - - # 从metadata中提取信息 - title = metadata.get('title', '未知') - duration = metadata.get('duration', 0) - content_type = metadata.get('content_type', 'video') - - print(f"[DeepAnalyzer] 开始分析: {title}") - print(f"[DeepAnalyzer] 类型: {content_type}, 时长: {duration}秒") - print(f"[DeepAnalyzer] 字幕存在: {bool(subtitle and subtitle.strip())}") - - # 检测是否有字幕 - has_subtitle = bool(subtitle and subtitle.strip()) - - if has_subtitle: - # 有字幕：使用 AI 深度分析 - print(f"[DeepAnalyzer] 使用AI深度分析（有字幕）") - - # 根据时长选择策略 - if duration < self.SHORT_VIDEO_THRESHOLD: - # 短视频: 轻量分析 - print(f"[DeepAnalyzer] 使用轻量分析策略 (时长 < 10分钟)") - result = self._analyze_short_with_subtitle(title, subtitle, duration) - else: - # 长视频: 详细时间轴分析 - print(f"[DeepAnalyzer] 使用详细时间轴分析策略 (时长 >= 10分钟)") - result = self._analyze_long_with_subtitle(title, subtitle, duration, content_type) - else: - # 无字幕：降级处理 - print(f"[DeepAnalyzer] 降级处理（无字幕）") - result = self._analyze_fallback(title, description, metadata) - - return result - - def _analyze_short(self, content: Content, content_text: str) -> AnalysisResult: - """短视频/短内容轻量分析（旧版接口，保留兼容）""" - result = AnalysisResult() - - # 截断文本 - text_sample = self._get_text_sample(content_text, max_chars=5000) - - # 生成简洁分析 - if self.ai_client: - analysis = self._analyze_with_ai_short(text_sample, content) - result.main_topics = analysis.get('topics', []) - result.key_insights = analysis.get('insights', []) - result.action_items = analysis.get('actions', []) - result.summary = analysis.get('summary', '') - else: - # 无AI时的简单分析 - result.main_topics = ["主题1", "主题2", "主题3"] - result.key_insights = ["关键见解1", "关键见解2"] - result.action_items = ["建议行动1"] - result.summary = content.summary or "无摘要" - - # 短视频的时间轴较简单 - result.timeline = self._generate_simple_timeline(content) - - return result - - def _analyze_short_with_subtitle(self, title: str, subtitle: str, duration: int) -> AnalysisResult: - """有字幕的短视频轻量分析（新接口）""" - result = AnalysisResult() - - # 截断文本 - text_sample = self._get_text_sample(subtitle, max_chars=5000) - - # 生成简洁分析 - if self.ai_client: - analysis = self._analyze_with_ai_short_text(text_sample, title, duration) - result.main_topics = analysis.get('topics', []) - result.key_insights = analysis.get('insights', []) - result.action_items = analysis.get('actions', []) - result.summary = analysis.get('summary', '') - else: - # 无AI时的简单分析 - result.main_topics = ["主题1", "主题2", "主题3"] - result.key_insights = ["关键见解1", "关键见解2"] - result.action_items = ["建议行动1"] - result.summary = title or "无摘要" - - # 短视频的时间轴较简单 - result.timeline = self._generate_simple_timeline_from_duration(title, duration) - - return result - - def _analyze_long(self, content: Content, content_text: str) -> AnalysisResult: - """长视频详细时间轴分析（旧版接口，保留兼容）""" - result = AnalysisResult() - - # 如果有AI客户端且支持分段分析 - if self.ai_client and hasattr(self.ai_client, '_deep_analyze'): - # 使用AI客户端的分段分析能力 - analysis = self.ai_client._deep_analyze( - content_text=content_text, - content_type=content.content_type.value, - duration=content.duration - ) - result.main_topics = analysis.get('topics', []) - result.key_insights = analysis.get('insights', []) - result.action_items = analysis.get('actions', []) - result.summary = analysis.get('summary', '') - result.full_analysis = analysis.get('full_analysis', '') - result.timeline = analysis.get('timeline', []) - else: - # 基础分段分析 - result = self._analyze_long_basic(content, content_text) - - return result - - def _analyze_long_with_subtitle(self, title: str, subtitle: str, duration: int, content_type: str) -> AnalysisResult: - """有字幕的长视频详细时间轴分析（新接口）""" - result = AnalysisResult() - - # 如果有AI客户端且支持分段分析 - if self.ai_client and hasattr(self.ai_client, '_deep_analyze'): - # 使用AI客户端的分段分析能力 - analysis = self.ai_client._deep_analyze( - content_text=subtitle, - content_type=content_type, - duration=duration - ) - result.main_topics = analysis.get('topics', []) - result.key_insights = analysis.get('insights', []) - result.action_items = analysis.get('actions', []) - result.summary = analysis.get('summary', '') - result.full_analysis = analysis.get('full_analysis', '') - result.timeline = analysis.get('timeline', []) - else: - # 基础分段分析 - result = self._analyze_long_basic_new(title, subtitle, duration) - - return result - - def _analyze_fallback(self, title: str, description: str, metadata: Dict[str, Any]) -> AnalysisResult: - """ - 无字幕降级处理：使用 description 或默认提示，只返回 default_tags - - Args: - title: 内容标题 - description: 内容描述 - metadata: 元数据 - - Returns: - AnalysisResult 分析结果（默认标签） - """ - result = AnalysisResult() - - # 默认标签 - default_tags = ["未分类", "待处理"] - - # 尝试从metadata和description提取信息 - author = metadata.get('author', '') - source = metadata.get('source', '') - - # 构建摘要 - if description and description.strip(): - # 使用 description 的前200字作为摘要 - summary = description[:200] + "..." if len(description) > 200 else description - print(f"[DeepAnalyzer] 降级处理: 使用 description 作为摘要") - else: - summary = f"无字幕内容，标题：{title}" - print(f"[DeepAnalyzer] 降级处理: 使用标题作为摘要") - - # 尝试从标题和作者提取标签 - tags = default_tags.copy() - if author: - tags.append(f"作者:{author}") - if source: - tags.append(f"来源:{source}") - - # 如果有AI客户端，尝试从description中提取标签 - if self.ai_client and description: - try: - prompt = f"""请从以下内容描述中提取3-5个标签： - -标题：{title} -描述：{description[:1000]} - -请返回JSON格式： -{{ - "tags": ["标签1", "标签2", "标签3"] -}}""" - response = self.ai_client.generate_json(prompt) - extracted_tags = response.get('tags', []) - if extracted_tags: - tags.extend(extracted_tags) - print(f"[DeepAnalyzer] 降级处理: AI 提取了 {len(extracted_tags)} 个标签") - except Exception as e: - print(f"[DeepAnalyzer] 降级处理: AI 标签提取失败: {e}") - - # 设置结果 - result.summary = summary - result.main_topics = tags[:3] # 前3个作为主题 - result.recommended_tags = tags - result.key_insights = ["无字幕内容，仅能从描述获取基本信息"] - result.action_items = ["建议补充字幕以便深度分析"] - result.timeline = [] # 无字幕无法生成时间轴 - - print(f"[DeepAnalyzer] 降级处理完成: 主题数={len(result.main_topics)}, 标签数={len(result.recommended_tags)}") - - return result - - def _analyze_long_basic(self, content: Content, content_text: str) -> AnalysisResult: - """基础长视频分析（无AI时，旧版接口）""" - result = AnalysisResult() - - # 简单的文本分割分析 - duration = content.duration or 3600 - - # 根据时长决定分割段数 - if duration < 3600: # 30-60分钟 - num_segments = 3 - elif duration < 7200: # 60-120分钟 - num_segments = 4 - else: # >120分钟 - num_segments = 6 - - # 生成时间轴 - segment_duration = duration // num_segments - timeline = [] - - for i in range(num_segments): - start_sec = i * segment_duration - end_sec = min((i + 1) * segment_duration, duration) - - timeline.append({ - 'index': i + 1, - 'start_time': self._format_time(start_sec), - 'end_time': self._format_time(end_sec), - 'title': f"第{i+1}部分", - 'summary': f"时间段 {self._format_time(start_sec)} - {self._format_time(end_sec)} 的内容" - }) - - result.timeline = timeline - result.main_topics = ["主题A", "主题B", "主题C", "主题D"] - result.key_insights = ["深入见解1", "深入见解2", "深入见解3"] - result.action_items = ["建议行动1", "建议行动2"] - result.summary = f"这是一个{self._format_time(duration)}的长视频，分为{num_segments}个主要部分进行分析。" - - return result - - def _analyze_long_basic_new(self, title: str, subtitle: str, duration: int) -> AnalysisResult: - """基础长视频分析（无AI时，新版接口）""" - result = AnalysisResult() - - # 根据时长决定分割段数 - if duration < 3600: # 30-60分钟 - num_segments = 3 - elif duration < 7200: # 60-120分钟 - num_segments = 4 - else: # >120分钟 - num_segments = 6 - - # 生成时间轴 - segment_duration = duration // num_segments - timeline = [] - - for i in range(num_segments): - start_sec = i * segment_duration - end_sec = min((i + 1) * segment_duration, duration) - - timeline.append({ - 'index': i + 1, - 'start_time': self._format_time(start_sec), - 'end_time': self._format_time(end_sec), - 'title': f"第{i+1}部分", - 'summary': f"时间段 {self._format_time(start_sec)} - {self._format_time(end_sec)} 的内容" - }) - - result.timeline = timeline - result.main_topics = ["主题A", "主题B", "主题C", "主题D"] - result.key_insights = ["深入见解1", "深入见解2", "深入见解3"] - result.action_items = ["建议行动1", "建议行动2"] - result.summary = f"标题：{title}，时长：{self._format_time(duration)}，分为{num_segments}个主要部分进行分析。" - - return result - - def _analyze_with_ai_short(self, text: str, content: Content) -> Dict[str, Any]: - """使用AI进行短视频分析（旧版接口）""" - prompt = f"""请分析以下内容，以JSON格式返回： - -{text[:3000]} - -请返回以下格式的JSON： -{{ - "summary": "整体摘要（100字以内）", - "topics": ["主题1", "主题2", "主题3"], - "insights": ["关键见解1", "关键见解2"], - "actions": ["建议行动1"] -}}""" - - try: - response = self.ai_client.generate_json(prompt) - return response - except Exception as e: - print(f"[DeepAnalyzer] AI分析失败: {e}") - return { - "summary": content.summary or "分析失败", - "topics": ["主题1", "主题2", "主题3"], - "insights": ["无法获取深入见解"], - "actions": [] - } - - def _analyze_with_ai_short_text(self, text: str, title: str, duration: int) -> Dict[str, Any]: - """使用AI进行短视频分析（新版接口）""" - prompt = f"""请分析以下视频字幕内容，以JSON格式返回： - -标题：{title} -时长：{self._format_time(duration)} - -字幕内容： -{text[:3000]} - -请返回以下格式的JSON： -{{ - "summary": "整体摘要（100字以内）", - "topics": ["主题1", "主题2", "主题3"], - "insights": ["关键见解1", "关键见解2"], - "actions": ["建议行动1"] -}}""" - - try: - response = self.ai_client.generate_json(prompt) - return response - except Exception as e: - print(f"[DeepAnalyzer] AI分析失败: {e}") - return { - "summary": title or "分析失败", - "topics": ["主题1", "主题2", "主题3"], - "insights": ["无法获取深入见解"], - "actions": [] - } - - def _get_text_sample(self, text: str, max_chars: int) -> str: - """获取文本样本""" - if len(text) <= max_chars: - return text - - # 分段采样 - part_size = max_chars // 3 - return ( - text[:part_size] + - "\n\n[...省略中间部分...]\n\n" + - text[len(text)//2 - part_size//2:len(text)//2 + part_size//2] + - "\n\n[...省略...]\n\n" + - text[-part_size:] - ) - - def _generate_simple_timeline(self, content: Content) -> List[Dict[str, Any]]: - """生成简单时间轴（旧版接口）""" - duration = content.duration or 600 - - # 短视频分为2-3段 - segments = 2 if duration < 300 else 3 - segment_duration = duration // segments - - timeline = [] - for i in range(segments): - start = i * segment_duration - end = min((i + 1) * segment_duration, duration) - timeline.append({ - 'index': i + 1, - 'start_time': self._format_time(start), - 'end_time': self._format_time(end), - 'title': f"段落 {i+1}", - 'summary': f"从{self._format_time(start)}到{self._format_time(end)}的内容" - }) - - return timeline - - def _generate_simple_timeline_from_duration(self, title: str, duration: int) -> List[Dict[str, Any]]: - """生成简单时间轴（新版接口）""" - duration = duration or 600 - - # 短视频分为2-3段 - segments = 2 if duration < 300 else 3 - segment_duration = duration // segments - - timeline = [] - for i in range(segments): - start = i * segment_duration - end = min((i + 1) * segment_duration, duration) - timeline.append({ - 'index': i + 1, - 'start_time': self._format_time(start), - 'end_time': self._format_time(end), - 'title': f"段落 {i+1}", - 'summary': f"从{self._format_time(start)}到{self._format_time(end)}的内容" - }) - - return timeline - - def _format_time(self, seconds: int) -> str: - """格式化秒数为时间字符串""" - hours = seconds // 3600 - minutes = (seconds % 3600) // 60 - secs = seconds % 60 - - if hours > 0: - return f"{hours}:{minutes:02d}:{secs:02d}" - else: - return f"{minutes}:{secs:02d}" diff --git a/skills/kb-workflow/src/extractors/__init__.py b/skills/kb-workflow/src/extractors/__init__.py deleted file mode 100644 index e00efea..0000000 --- a/skills/kb-workflow/src/extractors/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Extractors module for obsidian-kb - -from .base import Extractor, ExtractionError -from .bilibili_extractor import BilibiliExtractor -from .ocr_processor import OCRProcessor - -# 以下模块待实现 -# from .subtitle_detector import SubtitleDetector -# from .hard_subtitle_extractor import HardSubtitleExtractor - -__all__ = [ - 'Extractor', - 'ExtractionError', - 'BilibiliExtractor', - 'OCRProcessor', - # 'SubtitleDetector', - # 'HardSubtitleExtractor' -] diff --git a/skills/kb-workflow/src/extractors/base.py b/skills/kb-workflow/src/extractors/base.py deleted file mode 100644 index 814c86d..0000000 --- a/skills/kb-workflow/src/extractors/base.py +++ /dev/null @@ -1,83 +0,0 @@ -""" -Extractor 抽象基类 -""" -from abc import ABC, abstractmethod -from typing import Dict, Any, Optional - - -class ExtractionError(Exception): - """提取器异常基类""" - def __init__(self, message: str, source_id: str = "", details: Optional[Dict[str, Any]] = None): - self.message = message - self.source_id = source_id - self.details = details or {} - super().__init__(self.message) - - -class Extractor(ABC): - """ - 内容提取器抽象基类 - - 负责从各种来源（视频、文章等）提取结构化数据 - """ - - @abstractmethod - def extract(self, source_id: str) -> Dict[str, Any]: - """ - 提取源内容 - - Args: - source_id: 来源唯一标识符（如视频ID、文章URL等） - - Returns: - Dict[str, Any]: 包含以下键的字典： - - metadata: Dict[str, Any] - 元数据（标题、作者、时长等） - - stats: Dict[str, Any] - 统计信息（观看次数、点赞数等） - - subtitle: str - 字幕/文本内容 - - description: str - 内容描述 - - Raises: - ExtractionError: 提取失败时抛出 - """ - pass - - def validate_source_id(self, source_id: str) -> bool: - """ - 验证源ID格式是否有效（可选实现） - - Args: - source_id: 来源唯一标识符 - - Returns: - bool: 如果ID格式有效返回True，否则返回False - """ - # 默认实现：非空即有效 - return bool(source_id and source_id.strip()) - - def _handle_error(self, error: Exception, source_id: str, context: Optional[Dict[str, Any]] = None) -> None: - """ - 统一错误处理基类方法 - - Args: - error: 原始异常 - source_id: 来源ID - context: 额外的上下文信息 - - Raises: - ExtractionError: 封装后的提取器异常 - """ - error_details = { - 'error_type': type(error).__name__, - 'error_message': str(error), - 'source_id': source_id, - 'extractor_class': self.__class__.__name__ - } - - if context: - error_details.update(context) - - raise ExtractionError( - message=f"提取失败: {str(error)}", - source_id=source_id, - details=error_details - ) from error diff --git a/skills/kb-workflow/src/extractors/bilibili_extractor.py b/skills/kb-workflow/src/extractors/bilibili_extractor.py deleted file mode 100644 index c7c55a9..0000000 --- a/skills/kb-workflow/src/extractors/bilibili_extractor.py +++ /dev/null @@ -1,752 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -B站视频内容提取器 - -继承自 Extractor 基类，使用 Playwright 提取B站视频信息。 -支持短链接、BV号、完整URL的解析。 -""" - -import asyncio -import json -import logging -import re -from datetime import datetime -from typing import Dict, Any, Optional -from urllib.parse import urlparse - -logger = logging.getLogger(__name__) - -from playwright.async_api import async_playwright -from bs4 import BeautifulSoup - -from .base import Extractor, ExtractionError - -# 导入 kb-link-parser 用于短链解析 -import sys -import os -# 添加 kb-link-parser 到导入路径 -KB_LINK_PARSER_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) -sys.path.insert(0, os.path.join(KB_LINK_PARSER_PATH, 'kb-link-parser')) -from link_parser_shortlink import ShortLinkResolver -from .hard_subtitle_extractor import HardSubtitleExtractor - -# 尝试导入 stealth 支持 -try: - from undetected_playwright import stealth_async - STEALTH_AVAILABLE = True -except ImportError: - STEALTH_AVAILABLE = False - - -class BilibiliExtractor(Extractor): - """ - B站视频内容提取器 - - 使用 Playwright + stealth 模式提取视频元数据、统计数据和字幕 - """ - - # B站相关正则模式 - BV_PATTERN = re.compile(r'BV[a-zA-Z0-9]{10}') - URL_PATTERN = re.compile(r'https?://www\.bilibili\.com/video/(BV[a-zA-Z0-9]{10})') - - def __init__(self, config=None): - """ - 初始化提取器 - - Args: - config: 配置对象（支持 get() 方法） - """ - self.config = config - self._load_config() - self.resolver = ShortLinkResolver() - - def _load_config(self): - """从配置加载参数""" - if self.config: - try: - self.headless = self.config.get('sources.bilibili.extraction.headless', True) - self.timeout = self.config.get('sources.bilibili.extraction.timeout', 30) * 1000 # 转换为毫秒 - except: - # 如果配置获取失败，使用默认值 - self.headless = True - self.timeout = 30000 - else: - self.headless = True - self.timeout = 30000 - - def extract(self, source_id: str) -> Dict[str, Any]: - """ - 提取B站视频信息 - - Args: - source_id: 来源标识（支持BV号、完整URL、短链接） - - Returns: - Dict containing: - - metadata: Dict with title, author, bvid, aid, pubtime, duration, url - - stats: Dict with view, danmaku, reply, favorite, coin, share, like - - subtitle: str - 字幕文本（如果有） - - description: str - 视频简介 - - Raises: - ExtractionError: 提取失败时抛出 - """ - # 验证输入 - if not self.validate_source_id(source_id): - raise ExtractionError(f"无效的源ID: {source_id}", source_id=source_id) - - # 解析并获取BV号 - bvid = self._resolve_bvid(source_id) - - # 如果解析失败，检查是否为短链接 - if not bvid and self.resolver.is_bilibili_short_link(source_id): - # 使用Playwright解析短链接 - try: - bvid = asyncio.run(self._resolve_shortlink_bvid(source_id)) - except Exception as e: - raise ExtractionError(f"短链接解析失败: {e}", source_id=source_id) - - if not bvid: - raise ExtractionError(f"无法解析BV号: {source_id}", source_id=source_id) - - # 使用异步方法提取 - try: - result = asyncio.run(self._extract_async(bvid)) - except Exception as e: - self._handle_error(e, source_id, {'bvid': bvid, 'step': 'async_extraction'}) - - return result - - def _resolve_bvid(self, source_id: str) -> Optional[str]: - """ - 解析源ID，提取BV号 - - Args: - source_id: BV号、URL或短链接 - - Returns: - BV号字符串，解析失败返回None - """ - # 1. 直接BV号 - bv_match = self.BV_PATTERN.fullmatch(source_id.strip()) - if bv_match: - return source_id.strip() - - # 2. 完整URL - url_match = self.URL_PATTERN.match(source_id) - if url_match: - return url_match.group(1) - - # 3. 短链接处理已移至 extract() 方法中的 _resolve_shortlink_bvid() - # 这里不再处理，避免混淆 - - return None - - async def _resolve_shortlink_bvid(self, shortlink: str) -> Optional[str]: - """ - 使用 Playwright 解析B站短链接，获取 BV 号 - - Args: - shortlink: B站短链接（如 https://b23.tv/xxxxx） - - Returns: - BV号字符串，解析失败返回None - """ - async with async_playwright() as playwright: - browser = None - try: - # 启动浏览器（stealth 模式） - browser = await playwright.chromium.launch( - headless=self.headless, - args=[ - '--no-sandbox', - '--disable-setuid-sandbox', - '--disable-blink-features=AutomationControlled', - '--disable-dev-shm-usage', - '--start-maximized', - ] - ) - - context = await browser.new_context( - viewport={'width': 1920, 'height': 1080}, - user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36', - locale='zh-CN', - timezone_id='Asia/Shanghai', - ) - - # 应用 stealth 模式（如果可用） - if STEALTH_AVAILABLE: - await stealth_async(context) - - page = await context.new_page() - - # 访问短链接（等待重定向完成） - await page.goto(shortlink, wait_until='domcontentloaded', timeout=self.timeout) - await asyncio.sleep(3) # 等待重定向完成 - - # 获取重定向后的 URL - final_url = page.url - - # 从 URL 中提取 BV 号 - bv_match = re.search(r'BV[a-zA-Z0-9]{10}', final_url) - if bv_match: - return bv_match.group(0) - - except Exception as e: - raise ExtractionError(f"短链接解析异常: {e}", source_id=shortlink) - finally: - if browser: - await browser.close() - - return None - - async def _extract_async(self, bvid: str) -> Dict[str, Any]: - """ - 异步提取视频信息 - - Args: - bvid: B站视频BV号 - - Returns: - 提取结果字典 - """ - url = f"https://www.bilibili.com/video/{bvid}" - result = { - 'metadata': {}, - 'stats': {}, - 'subtitle': '', - 'description': '' - } - - async with async_playwright() as playwright: - try: - # 启动浏览器 - browser = await playwright.chromium.launch( - headless=self.headless, - args=[ - '--no-sandbox', - '--disable-setuid-sandbox', - '--disable-blink-features=AutomationControlled', - '--disable-dev-shm-usage', - '--start-maximized', - ] - ) - - # 创建上下文 - context = await browser.new_context( - viewport={'width': 1920, 'height': 1080}, - user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36', - locale='zh-CN', - timezone_id='Asia/Shanghai', - extra_http_headers={ - 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8', - 'Referer': 'https://www.bilibili.com/', - } - ) - - # 应用 stealth 模式（如果可用） - if STEALTH_AVAILABLE: - await stealth_async(context) - - # 创建页面 - page = await context.new_page() - - # 访问页面 - try: - response = await page.goto(url, wait_until='domcontentloaded', timeout=self.timeout) - except Exception: - # 即使 goto 失败，也可能可以访问 API - response = None - - # 等待页面加载（即使HTTP状态码不为200也继续，因为可能可以访问API） - await asyncio.sleep(3) - - # 获取页面内容（不要强制等待 networkidle，避免超时） - try: - content = await page.content() - except Exception as e: - # 如果获取内容失败，可能是因为页面导航，尝试其他方法 - content = "" - - # 尝试多种方法提取 - video_data = await self._try_extract_methods(page, content, bvid) - - if not video_data: - raise ExtractionError("无法提取视频信息", source_id=bvid) - - # 组织返回数据 - result['metadata'] = { - 'title': video_data.get('title', ''), - 'author': video_data.get('author', ''), - 'bvid': video_data.get('bvid', ''), - 'aid': video_data.get('aid', ''), - 'mid': video_data.get('mid', ''), - 'pubtime': video_data.get('pubtime', ''), - 'duration': video_data.get('duration', ''), - 'url': url, - 'tags': video_data.get('tags', ''), - } - - result['stats'] = { - 'view': video_data.get('view', 0), - 'danmaku': video_data.get('danmaku', 0), - 'reply': video_data.get('reply', 0), - 'favorite': video_data.get('favorite', 0), - 'coin': video_data.get('coin', 0), - 'share': video_data.get('share', 0), - 'like': video_data.get('like', 0), - } - - result['description'] = video_data.get('desc', '') - - # 尝试提取字幕 - result['subtitle'] = await self._extract_subtitles(page, bvid) - - await browser.close() - - except Exception as e: - if 'browser' in locals(): - await browser.close() - raise - - return result - - async def _try_extract_methods(self, page, content: str, bvid: str) -> Dict[str, Any]: - """ - 尝试多种方法提取视频信息 - - Args: - page: Playwright page对象 - content: 页面HTML内容 - bvid: BV号 - - Returns: - 视频信息字典 - """ - video_data = {} - - # 方法1: 从 __INITIAL_STATE__ 获取 - try: - initial_state = await page.evaluate('() => typeof window.__INITIAL_STATE__ !== "undefined" ? window.__INITIAL_STATE__ : null') - if initial_state: - video_data = self._parse_initial_state(initial_state) - if video_data and video_data.get('title'): - return video_data - except Exception as e: - pass - - # 方法2: 从 script 标签提取 - try: - soup = BeautifulSoup(content, 'lxml') - script_tags = soup.find_all('script') - for script in script_tags: - if script.string and '__INITIAL_STATE__' in script.string: - match = re.search(r'window\.__INITIAL_STATE__\s*=\s*({.+?});', script.string) - if match: - data = json.loads(match.group(1)) - video_data = self._parse_initial_state(data) - if video_data and video_data.get('title'): - return video_data - except Exception as e: - pass - - # 方法3: 使用 API - try: - api_url = "https://api.bilibili.com/x/web-interface/view" - response = await page.request.get(api_url, params={'bvid': bvid}) - - if response.ok: - data = await response.json() - if data.get('code') == 0: - video_data = self._parse_api_response(data.get('data', {})) - if video_data and video_data.get('title'): - return video_data - except Exception as e: - pass - - return video_data - - def _parse_initial_state(self, data: Dict) -> Dict[str, Any]: - """解析 __INITIAL_STATE__ 数据""" - video_data = {} - - try: - video = data.get('videoData', {}) - - video_data['title'] = video.get('title', '') - video_data['bvid'] = video.get('bvid', '') - video_data['aid'] = video.get('aid', '') - - owner = video.get('owner', {}) - video_data['author'] = owner.get('name', '') - video_data['mid'] = owner.get('mid', '') - - stat = video.get('stat', {}) - video_data['view'] = stat.get('view', 0) - video_data['danmaku'] = stat.get('danmaku', 0) - video_data['reply'] = stat.get('reply', 0) - video_data['favorite'] = stat.get('favorite', 0) - video_data['coin'] = stat.get('coin', 0) - video_data['share'] = stat.get('share', 0) - video_data['like'] = stat.get('like', 0) - - pubdate = video.get('pubdate', 0) - video_data['pubtime'] = datetime.fromtimestamp(pubdate).strftime('%Y-%m-%d %H:%M:%S') if pubdate else '' - - duration = video.get('duration', 0) - video_data['duration'] = self._format_duration(duration) - - video_data['desc'] = video.get('desc', '') - - tags = [] - for tag in video.get('tag', []): - tags.append(tag.get('tag_name', '')) - video_data['tags'] = ', '.join(tags) - - except Exception as e: - pass - - return video_data - - def _parse_api_response(self, data: Dict) -> Dict[str, Any]: - """解析API响应数据""" - video_data = {} - - try: - video_data['title'] = data.get('title', '') - video_data['bvid'] = data.get('bvid', '') - video_data['aid'] = data.get('aid', '') - - owner = data.get('owner', {}) - video_data['author'] = owner.get('name', '') - video_data['mid'] = owner.get('mid', '') - - stat = data.get('stat', {}) - video_data['view'] = stat.get('view', 0) - video_data['danmaku'] = stat.get('danmaku', 0) - video_data['reply'] = stat.get('reply', 0) - video_data['favorite'] = stat.get('favorite', 0) - video_data['coin'] = stat.get('coin', 0) - video_data['share'] = stat.get('share', 0) - video_data['like'] = stat.get('like', 0) - - pubdate = data.get('pubdate', 0) - video_data['pubtime'] = datetime.fromtimestamp(pubdate).strftime('%Y-%m-%d %H:%M:%S') if pubdate else '' - - duration = data.get('duration', 0) - video_data['duration'] = self._format_duration(duration) - - video_data['desc'] = data.get('desc', '') - - tags = [] - for tag in data.get('tag', []): - tags.append(tag.get('tag_name', '')) - video_data['tags'] = ', '.join(tags) - - except Exception as e: - pass - - return video_data - - async def _extract_subtitles(self, page, bvid: str) -> str: - """ - 提取视频字幕 - - 支持的字幕类型： - 1. 官方 CC 字幕（需要 UP 主手动上传） - 2. AI 自动生成字幕（标记为 ai-zh） - 3. WebVTT 格式字幕 - - 注意：硬字幕（嵌入视频画面内）无法提取 - - Args: - page: Playwright page对象 - bvid: BV号 - - Returns: - 字幕文本（多行字符串） - """ - subtitles = [] - - try: - # 首先通过 API 获取视频信息（包含 cid 和 aid） - api_url = "https://api.bilibili.com/x/web-interface/view" - response = await page.request.get(api_url, params={'bvid': bvid}) - - if not response.ok: - return '' - - data = await response.json() - if data.get('code') != 0: - return '' - - video_data = data.get('data', {}) - cid = video_data.get('cid') - aid = video_data.get('aid') - - if not cid: - return '' - - # 方法1: 使用 WBI API（推荐，支持 AI 字幕） - player_api_url = "https://api.bilibili.com/x/player/wbi/v2" - player_response = await page.request.get(player_api_url, params={'aid': aid, 'cid': cid}) - - if player_response.ok: - player_data = await player_response.json() - if player_data.get('code') == 0: - subtitle_info = player_data.get('data', {}).get('subtitle', {}) - subtitles_list = subtitle_info.get('subtitles', []) - - if subtitles_list: - # 优先选择中文字幕（包括 AI 字幕） - subtitle_item = None - for sub in subtitles_list: - # 优先级: ai-zh > zh-CN > zh-Hans > 其他 - lan = sub.get('lan', '') - if lan == 'ai-zh': - subtitle_item = sub - break - elif lan in ['zh-CN', 'zh-Hans', 'zh']: - if not subtitle_item: - subtitle_item = sub - - # 如果没有找到中文，使用第一个 - if not subtitle_item: - subtitle_item = subtitles_list[0] - - if subtitle_item: - subtitle_url = subtitle_item.get('subtitle_url', '') - if subtitle_url: - full_url = f"https:{subtitle_url}" - sub_response = await page.request.get(full_url) - if sub_response.ok: - sub_data = await sub_response.json() - subtitles = self._parse_subtitle_json(sub_data) - # 如果找到字幕，直接返回 - if subtitles: - lines = [] - for sub in subtitles: - lines.append(f"[{sub['from']} - {sub['to']}] {sub['content']}") - return '\n'.join(lines) - - # 方法2: 如果 WBI 失败，使用旧版 API - player_api_url = "https://api.bilibili.com/x/player/v2" - player_response = await page.request.get(player_api_url, params={'bvid': bvid, 'cid': cid}) - - if player_response.ok: - player_data = await player_response.json() - if player_data.get('code') == 0: - subtitle_info = player_data.get('data', {}).get('subtitle', {}) - subtitles_list = subtitle_info.get('subtitles', []) - - if subtitles_list: - subtitle_item = subtitles_list[0] - subtitle_url = subtitle_item.get('subtitle_url', '') - if subtitle_url: - full_url = f"https:{subtitle_url}" - sub_response = await page.request.get(full_url) - if sub_response.ok: - sub_data = await sub_response.json() - subtitles = self._parse_subtitle_json(sub_data) - except Exception as e: - # 静默失败，不影响视频信息提取 - pass - - # 如果所有方法都没有找到字幕，尝试硬字幕提取（OCR） - if not subtitles: - try: - # 初始化硬字幕提取器 - hard_subtitle_extractor = HardSubtitleExtractor(self.config) - - # 尝试从视频流中提取硬字幕 - # 注意：这里需要传入 cid，所以先从 page 获取 - api_url = "https://api.bilibili.com/x/web-interface/view" - response = await page.request.get(api_url, params={'bvid': bvid}) - - if response.ok: - data = await response.json() - if data.get('code') == 0: - video_data = data.get('data', {}) - cid = video_data.get('cid') - - if cid: - # 使用硬字幕提取器 - hard_result = hard_subtitle_extractor.extract_subtitle(bvid) - - if hard_result and hard_result.get('text'): - logger.info("硬字幕提取成功") - # 格式化结果 - hard_text = hard_result.get('text', '') - segments = hard_result.get('segments', []) - - lines = [] - for seg in segments: - lines.append(f"[{seg['time']:.1f}s] {seg['text']}") - return '\n'.join(lines) - else: - logger.warning("硬字幕提取失败或无结果") - except Exception as e: - logger.warning(f"硬字幕提取异常: {e}") - pass - - # 转换为多行字符串 - if subtitles: - lines = [] - for sub in subtitles: - lines.append(f"[{sub['from']} - {sub['to']}] {sub['content']}") - return '\n'.join(lines) - - return '' - - def _parse_subtitle_json(self, data: Dict) -> list: - """解析字幕JSON数据""" - subtitles = [] - - try: - body = data.get('body', []) - for item in body: - from_time = item.get('from', 0) - to_time = item.get('to', 0) - content = item.get('content', '') - - subtitles.append({ - 'from': self._format_timestamp(from_time), - 'to': self._format_timestamp(to_time), - 'content': content - }) - except Exception as e: - pass - - return subtitles - - def _format_duration(self, seconds: int) -> str: - """格式化时长（秒 -> HH:MM:SS）""" - if not seconds: - return '' - - hours = seconds // 3600 - minutes = (seconds % 3600) // 60 - secs = seconds % 60 - - if hours > 0: - return f"{hours:02d}:{minutes:02d}:{secs:02d}" - else: - return f"{minutes:02d}:{secs:02d}" - - def _format_timestamp(self, seconds: float) -> str: - """格式化时间戳""" - minutes = int(seconds // 60) - secs = int(seconds % 60) - return f"{minutes:02d}:{secs:02d}" - - -# 创建测试脚本的辅助函数 -def create_test_script(): - """创建简单的测试脚本""" - script = """#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -\"\"\" -B站提取器测试脚本 -\"\"\" -import sys -import os - -# 添加项目路径 -sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..')) - -from skills.obsidian_kb.src.extractors.bilibili_extractor import BilibiliExtractor -from skills.obsidian_kb.src.config import get_config - - -def main(): - \"\"\"测试提取器\"\"\" - # 获取配置 - config = get_config() - - # 创建提取器 - extractor = BilibiliExtractor(config=config) - - # 测试BV号 - bvid = "BV1MnFNz5EDL" - - print(f"开始提取视频: {bvid}") - print("=" * 60) - - try: - result = extractor.extract(bvid) - - print("\\n提取成功！\\n") - print("=" * 60) - print("元数据 (metadata):") - print("=" * 60) - for key, value in result['metadata'].items(): - print(f" {key}: {value}") - - print("\\n" + "=" * 60) - print("统计数据 (stats):") - print("=" * 60) - for key, value in result['stats'].items(): - print(f" {key}: {value:,}") - - print("\\n" + "=" * 60) - print("简介 (description):") - print("=" * 60) - desc = result['description'] - if len(desc) > 500: - print(desc[:500] + "...") - else: - print(desc) - - print("\\n" + "=" * 60) - print("字幕 (subtitle):") - print("=" * 60) - subtitle = result['subtitle'] - if subtitle: - lines = subtitle.split('\\n') - if len(lines) > 10: - print("\\n".join(lines[:10])) - print(f"\\n...(共 {len(lines)} 条字幕)") - else: - print(subtitle) - else: - print(" (无字幕)") - - print("\\n" + "=" * 60) - print("✅ 测试完成") - print("=" * 60) - - except Exception as e: - print(f"\\n❌ 测试失败: {e}") - import traceback - traceback.print_exc() - - -if __name__ == '__main__': - main() -""" - return script -""" - -示例用法： - -```python -from skills.obsidian_kb.src.extractors.bilibili_extractor import BilibiliExtractor -from skills.obsidian_kb.src.config import get_config - -# 获取配置 -config = get_config() - -# 创建提取器 -extractor = BilibiliExtractor(config=config) - -# 提取视频信息（支持BV号、URL） -result = extractor.extract('BV1MnFNz5EDL') - -# 访问结果 -print(result['metadata']['title']) -print(result['stats']['view']) -print(result['description']) -print(result['subtitle']) -``` -""" diff --git a/skills/kb-workflow/src/extractors/easyocr_processor.py b/skills/kb-workflow/src/extractors/easyocr_processor.py deleted file mode 100644 index 3a1c351..0000000 --- a/skills/kb-workflow/src/extractors/easyocr_processor.py +++ /dev/null @@ -1,355 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -OCR 文字识别处理器（EasyOCR 版本） - -使用 EasyOCR 实现视频帧文字识别功能，作为 PaddleOCR 的替代方案 -主要优势：不依赖 PaddlePaddle，避免 oneDNN 兼容性问题 -""" - -import re -import numpy as np -from typing import List, Dict, Any, Tuple -import logging - -logger = logging.getLogger(__name__) - - -class EasyOCRProcessor: - """ - EasyOCR 文字识别处理器 - - 使用 EasyOCR 从视频帧中识别文字，支持批量识别和结果过滤 - - 相比 PaddleOCR 的优势： - - 不依赖 PaddlePaddle，避免 oneDNN 兼容性问题 - - 安装更简单，依赖更少 - - 支持多种语言（70+ 种） - """ - - # 相似度阈值：用于判断两个文本是否相似（基于编辑距离） - SIMILARITY_THRESHOLD = 0.85 - - # 置信度阈值 - CONFIDENCE_THRESHOLD = 0.7 - - # 最小字幕长度（字符数） - MIN_SUBTITLE_LENGTH = 2 - - def __init__(self, lang: str = 'ch_sim', use_gpu: bool = False, detail: int = 1): - """ - 初始化 OCR 处理器 - - Args: - lang: 语言支持，'ch_sim' 为简体中文，'ch_tra' 为繁体中文 - use_gpu: 是否使用 GPU 加速 - detail: 识别结果详细程度（0=只返回文本，1=返回文本+坐标+置信度） - """ - self.lang = lang - self.use_gpu = use_gpu - self.detail = detail - - # 初始化 EasyOCR 实例 - self._ocr = None - self._initialize_ocr() - - def _initialize_ocr(self): - """初始化 EasyOCR 实例""" - try: - from easyocr import Reader - - self._ocr = Reader( - [self.lang], - gpu=self.use_gpu, - verbose=False # 不显示详细日志 - ) - logger.info("EasyOCR 初始化成功") - except Exception as e: - logger.error(f"EasyOCR 初始化失败: {e}") - raise RuntimeError(f"无法初始化 EasyOCR: {e}") - - def _initialize_ocr(self): - """初始化 EasyOCR 实例""" - try: - from easyocr import Reader - - self._ocr = Reader( - [self.lang], - gpu=self.use_gpu, - verbose=False # 不显示详细日志 - ) - logger.info("EasyOCR 初始化成功") - except Exception as e: - logger.error(f"EasyOCR 初始化失败: {e}") - raise RuntimeError(f"无法初始化 EasyOCR: {e}") - - def recognize_frames(self, frames: List[Tuple[np.ndarray, float]]) -> List[Dict[str, Any]]: - """ - 批量识别帧中的文字 - - Args: - frames: 帧列表，每个元素为 (图像数组, 时间戳) 元组 - - 图像数组：numpy.ndarray，形状为 (H, W, 3) 的 RGB 图像 - - 时间戳：float，单位为秒 - - Returns: - List[Dict[str, Any]]: 识别结果列表，每个结果包含： - - time: float, 时间戳（秒） - - text: str, 识别的文本 - - confidence: float, 置信度 (0-1) - """ - if not frames: - logger.warning("输入帧列表为空") - return [] - - results = [] - - for image, timestamp in frames: - try: - # 调用 EasyOCR 进行识别 - # EasyOCR 返回格式: [[[x1, y1], [x2, y2], [x3, y3], [x4, y4]], (text, confidence, ...)] - # 根据 detail 参数，返回格式不同： - # detail=0: 返回 [text1, text2, ...] - # detail=1: 返回 [[bbox, (text, confidence)], ...] - ocr_result = self._ocr.readtext(image, detail=self.detail) - - if ocr_result: - if self.detail == 0: - # 简单模式：只返回文本列表 - for i, text in enumerate(ocr_result): - if text: - results.append({ - 'time': timestamp, - 'text': str(text), - 'confidence': 1.0 # 简单模式下没有置信度 - }) - else: - # 详细模式：返回边界框+文本+置信度 - for item in ocr_result: - if len(item) >= 2: - bbox = item[0] - text_info = item[1] - - if len(text_info) >= 2: - text, confidence = text_info[0], text_info[1] - - # 跳过空文本 - if not text: - continue - - # 处理置信度：可能是数字或特殊字符 - try: - if isinstance(confidence, str): - # 如果是 '?' 或其他特殊字符，使用默认置信度 - conf_float = 0.5 - else: - conf_float = float(confidence) - except (ValueError, TypeError): - conf_float = 0.5 - - results.append({ - 'time': timestamp, - 'text': text, - 'confidence': conf_float - }) - except Exception as e: - logger.warning(f"帧识别失败 (时间戳 {timestamp}s): {e}") - continue - - logger.info(f"原始识别结果数量: {len(results)}") - - # 过滤和清洗结果 - filtered_results = self._filter_and_clean(results) - logger.info(f"过滤后结果数量: {len(filtered_results)}") - - return filtered_results - - def _filter_and_clean(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """ - 过滤和清洗识别结果 - - Args: - results: 原始识别结果列表 - - Returns: - List[Dict[str, Any]]: 过滤后的结果列表 - """ - if not results: - return [] - - # 步骤 1: 按置信度过滤 - filtered = self._filter_by_confidence(results) - logger.debug(f"置信度过滤后: {len(filtered)}") - - # 步骤 2: 过滤噪声（纯符号、单字符等） - filtered = self._filter_noise(filtered) - logger.debug(f"噪声过滤后: {len(filtered)}") - - # 步骤 3: 去除重复或相似的文本 - filtered = self._remove_duplicates(filtered) - logger.debug(f"去重后: {len(filtered)}") - - # 步骤 4: 按时间戳排序 - filtered.sort(key=lambda x: x['time']) - - return filtered - - def _filter_by_confidence(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """ - 按置信度过滤结果 - - Args: - results: 识别结果列表 - - Returns: - List[Dict[str, Any]]: 高置信度结果 - """ - return [r for r in results if r['confidence'] >= self.CONFIDENCE_THRESHOLD] - - def _filter_noise(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """ - 过滤噪声结果 - - 过滤条件： - - 文本长度小于最小长度 - - 纯符号或空白字符 - - Args: - results: 识别结果列表 - - Returns: - List[Dict[str, Any]]: 过滤后的结果 - """ - filtered = [] - - for result in results: - text = result['text'].strip() - - # 过滤过短的文本 - if len(text) < self.MIN_SUBTITLE_LENGTH: - continue - - # 过滤纯符号或空白字符 - if self._is_noise(text): - continue - - filtered.append(result) - - return filtered - - def _is_noise(self, text: str) -> bool: - """ - 判断文本是否为噪声 - - Args: - text: 要判断的文本 - - Returns: - bool: 如果是噪声返回 True - """ - # 使用正则判断是否只包含符号和空白 - try: - # 移除标点符号后检查是否为空 - # 先替换常见的中文标点 - cleaned = re.sub(r'[^\w\u4e00-\u9fff]', '', text) - return len(cleaned) == 0 - except: - # 如果正则出错，保守处理，不算噪声 - return False - - def _remove_duplicates(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """ - 去除重复或相似的文本 - - 基于编辑距离（Levenshtein distance）判断文本相似度 - - Args: - results: 识别结果列表（假设已按时间戳排序） - - Returns: - List[Dict[str, Any]]: 去重后的结果 - """ - if not results: - return [] - - deduped = [results[0]] - - for current in results[1:]: - last = deduped[-1] - - # 计算相似度 - similarity = self._calculate_similarity(current['text'], last['text']) - - # 如果相似度低于阈值，则保留 - if similarity < self.SIMILARITY_THRESHOLD: - deduped.append(current) - else: - # 相似文本：保留置信度更高的那个 - if current['confidence'] > last['confidence']: - deduped[-1] = current - - return deduped - - def _calculate_similarity(self, text1: str, text2: str) -> float: - """ - 计算两个文本的相似度（基于归一化编辑距离） - - Args: - text1: 文本1 - text2: 文本2 - - Returns: - float: 相似度 (0-1)，1 表示完全相同 - """ - if text1 == text2: - return 1.0 - - len1, len2 = len(text1), len(text2) - - if len1 == 0 or len2 == 0: - return 0.0 - - # 动态规划计算编辑距离 - dp = np.zeros((len1 + 1, len2 + 1)) - - for i in range(len1 + 1): - dp[i, 0] = i - - for j in range(len2 + 1): - dp[0, j] = j - - for i in range(1, len1 + 1): - for j in range(1, len2 + 1): - if text1[i-1] == text2[j-1]: - dp[i, j] = dp[i-1, j-1] - else: - dp[i, j] = min( - dp[i-1, j] + 1, # 删除 - dp[i, j-1] + 1, # 插入 - dp[i-1, j-1] + 1 # 替换 - ) - - edit_distance = dp[len1, len2] - max_len = max(len1, len2) - - # 归一化相似度：1 - (编辑距离 / 最大长度) - similarity = 1.0 - (edit_distance / max_len) - - return similarity - - def get_ocr_info(self) -> Dict[str, Any]: - """ - 获取 OCR 处理器信息 - - Returns: - Dict[str, Any]: OCR 配置信息 - """ - return { - 'engine': 'easyocr', - 'lang': self.lang, - 'use_gpu': self.use_gpu, - 'detail': self.detail, - 'confidence_threshold': self.CONFIDENCE_THRESHOLD, - 'similarity_threshold': self.SIMILARITY_THRESHOLD, - 'min_subtitle_length': self.MIN_SUBTITLE_LENGTH - } diff --git a/skills/kb-workflow/src/extractors/hard_subtitle_extractor.py b/skills/kb-workflow/src/extractors/hard_subtitle_extractor.py deleted file mode 100644 index ac9d48a..0000000 --- a/skills/kb-workflow/src/extractors/hard_subtitle_extractor.py +++ /dev/null @@ -1,435 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -硬字幕提取器 - 主协调器 - -整合 VideoStreamExtractor、SubtitleDetector 和 OCRProcessor， -实现完整的 B站硬字幕提取流程。 -""" - -import logging -from typing import Dict, Any, List, Optional - -from .video_stream_extractor import VideoStreamExtractor -from .subtitle_detector import SubtitleDetector -from .ocr_processor import OCRProcessor - -logger = logging.getLogger(__name__) - - -class HardSubtitleExtractor: - """ - 硬字幕提取器（主协调器） - - 职责： - - 协调视频流提取、字幕区域检测和 OCR 识别 - - 实现完整的硬字幕提取流程 - - 提供统一的接口返回字幕数据 - """ - - def __init__(self, config: Optional[Dict] = None): - """ - 初始化硬字幕提取器 - - Args: - config: 配置对象 - """ - self.config = config or {} - - # 初始化子模块 - self.video_extractor = VideoStreamExtractor() - self.subtitle_detector = SubtitleDetector( - cache_size=self._get_config('hard_subtitle.detection.cache_region', 50) - ) - self.ocr_processor = OCRProcessor( - use_angle_cls=True, - lang='ch', - use_gpu=self._get_config('hard_subtitle.ocr.use_gpu', False), - show_log=False - ) - - def _get_config(self, key: str, default: Any = None) -> Any: - """获取配置值""" - if not self.config: - return default - keys = key.split('.') - value = self.config - for k in keys: - value = value.get(k) - if value is None: - return default - return value - - def extract_subtitle( - self, - bvid: str, - interval_seconds: float = None, - max_frames: int = None, - start_time: float = 0.0 - ) -> Dict[str, Any]: - """ - 提取硬字幕 - - Args: - bvid: B站视频 BV 号 - interval_seconds: 提取间隔（秒） - max_frames: 最大帧数 - start_time: 开始时间（秒） - - Returns: - Dict[str, Any]: 字幕提取结果，包含： - - subtitle_type: "hard_subtitle" - - text: 完整字幕文本 - - segments: 字幕分段列表 - - confidence: 平均置信度 - - source: "paddleocr" - """ - # 使用配置值或默认值 - interval_seconds = interval_seconds or self._get_config('hard_subtitle.video.interval_seconds', 5.0) - max_frames = max_frames or self._get_config('hard_subtitle.video.max_frames', 500) - use_download = self._get_config('hard_subtitle.video.stream_mode', True) - - logger.info(f"开始提取硬字幕: {bvid}") - logger.info(f"参数: 间隔={interval_seconds}秒, 最大帧数={max_frames}, 开始时间={start_time}秒") - - try: - # 步骤1: 提取视频帧 - logger.info("步骤1: 提取视频帧...") - frames = self.video_extractor.extract_frames_from_bvid( - bvid=bvid, - interval_seconds=interval_seconds, - max_frames=max_frames, - start_time=start_time, - use_download=use_download - ) - - if not frames: - logger.warning("未提取到任何视频帧") - return { - 'subtitle_type': 'hard_subtitle', - 'text': '', - 'segments': [], - 'confidence': 0.0, - 'source': 'paddleocr' - } - - logger.info(f"成功提取 {len(frames)} 帧图像") - - # 步骤2: 检测字幕区域 - logger.info("步骤2: 检测字幕区域...") - use_cache = self._get_config('hard_subtitle.detection.cache_region', True) - subtitle_region = None - - # 使用第一帧检测字幕区域 - if frames: - first_frame = frames[0]['image'] - height, width = first_frame.shape[:2] - - # 尝试自动检测 - subtitle_region = self.subtitle_detector.auto_detect_subtitle_region( - first_frame, - video_id=bvid, - use_cache=use_cache - ) - - logger.info(f"字幕区域检测完成: x={subtitle_region[0]}, y={subtitle_region[1]}, " - f"width={subtitle_region[2]}, height={subtitle_region[3]}") - - # 如果检测失败，使用预设区域 - if not subtitle_region and frames: - height, width = frames[0]['image'].shape[:2] - default_region = self._get_config('hard_subtitle.detection.default_region', 'bottom') - subtitle_region = self.subtitle_detector.get_preset_region( - default_region, - height, - width - ) - logger.info(f"使用预设字幕区域: {default_region}") - - # 步骤3: 裁剪字幕区域 - logger.info("步骤3: 裁剪字幕区域...") - if not subtitle_region: - logger.warning("无法获取字幕区域，跳过裁剪") - # 使用完整帧 - cropped_frames = [frame['image'] for frame in frames] - else: - x, y, w, h = subtitle_region - cropped_frames = [] - - for frame in frames: - image = frame['image'] - # 确保坐标在图像范围内 - y = min(y, image.shape[0] - 1) - x = min(x, image.shape[1] - 1) - h = min(h, image.shape[0] - y) - w = min(w, image.shape[1] - x) - - # 裁剪字幕区域 - cropped = image[y:y+h, x:x+w] - cropped_frames.append(cropped) - - logger.info(f"裁剪完成: {len(cropped_frames)} 帧字幕区域") - - # 步骤4: OCR 识别 - logger.info("步骤4: OCR 识别...") - # 准备 OCR 输入格式 (image, timestamp) - ocr_input = [(cropped_frames[i], frames[i]['timestamp']) - for i in range(min(len(cropped_frames), len(frames)))] - - ocr_results = self.ocr_processor.recognize_frames(ocr_input) - logger.info(f"OCR 识别完成: {len(ocr_results)} 条结果") - - # 步骤5: 整理结果 - logger.info("步骤5: 整理字幕结果...") - subtitle_text = self._format_subtitle_text(ocr_results) - segments = ocr_results - confidence = self._calculate_average_confidence(ocr_results) - - logger.info(f"平均置信度: {confidence:.3f}") - logger.info(f"字幕文本长度: {len(subtitle_text)} 字符") - - return { - 'subtitle_type': 'hard_subtitle', - 'text': subtitle_text, - 'segments': segments, - 'confidence': confidence, - 'source': 'paddleocr' - } - - except Exception as e: - logger.error(f"硬字幕提取失败: {e}", exc_info=True) - # 返回空结果，不阻塞主流程 - return { - 'subtitle_type': 'hard_subtitle', - 'text': '', - 'segments': [], - 'confidence': 0.0, - 'source': 'paddleocr' - } - - def _format_subtitle_text(self, ocr_results: List[Dict[str, Any]]) -> str: - """ - 格式化字幕文本 - - Args: - ocr_results: OCR 识别结果列表 - - Returns: - str: 格式化后的字幕文本 - """ - if not ocr_results: - return '' - - # 按时间戳排序 - sorted_results = sorted(ocr_results, key=lambda x: x['time']) - - # 生成字幕文本（带时间戳） - lines = [] - for result in sorted_results: - time_str = f"[{result['time']:.1f}s]" - lines.append(f"{time_str} {result['text']}") - - return '\n'.join(lines) - - def _calculate_average_confidence(self, ocr_results: List[Dict[str, Any]]) -> float: - """ - 计算平均置信度 - - Args: - ocr_results: OCR 识别结果列表 - - Returns: - float: 平均置信度 - """ - if not ocr_results: - return 0.0 - - confidences = [r.get('confidence', 0.0) for r in ocr_results] - return sum(confidences) / len(confidences) if confidences else 0.0 - - def extract_subtitle_from_url( - self, - video_url: str, - interval_seconds: float = None, - max_frames: int = None, - start_time: float = 0.0 - ) -> Dict[str, Any]: - """ - 从视频 URL 提取硬字幕 - - Args: - video_url: 视频流 URL - interval_seconds: 提取间隔（秒） - max_frames: 最大帧数 - start_time: 开始时间（秒） - - Returns: - Dict[str, Any]: 字幕提取结果 - """ - logger.info(f"从 URL 提取硬字幕: {video_url[:100]}...") - - # 使用配置值或默认值 - interval_seconds = interval_seconds or self._get_config('hard_subtitle.video.interval_seconds', 5.0) - max_frames = max_frames or self._get_config('hard_subtitle.video.max_frames', 500) - - try: - # 提取视频帧 - frames = self.video_extractor.extract_frames( - video_url=video_url, - interval_seconds=interval_seconds, - max_frames=max_frames, - start_time=start_time - ) - - if not frames: - logger.warning("未提取到任何视频帧") - return { - 'subtitle_type': 'hard_subtitle', - 'text': '', - 'segments': [], - 'confidence': 0.0, - 'source': 'paddleocr' - } - - # 后续流程与从 BV 号提取相同 - return self._process_frames_to_subtitle(frames, video_url) - - except Exception as e: - logger.error(f"从 URL 提取字幕失败: {e}", exc_info=True) - return { - 'subtitle_type': 'hard_subtitle', - 'text': '', - 'segments': [], - 'confidence': 0.0, - 'source': 'paddleocr' - } - - def _process_frames_to_subtitle( - self, - frames: List[Dict[str, Any]], - video_id: str - ) -> Dict[str, Any]: - """ - 处理视频帧到字幕（共享逻辑） - - Args: - frames: 帧列表 - video_id: 视频 ID - - Returns: - Dict[str, Any]: 字幕结果 - """ - # 检测字幕区域 - use_cache = self._get_config('hard_subtitle.detection.cache_region', True) - subtitle_region = None - - if frames: - first_frame = frames[0]['image'] - height, width = first_frame.shape[:2] - subtitle_region = self.subtitle_detector.auto_detect_subtitle_region( - first_frame, - video_id=video_id, - use_cache=use_cache - ) - - # 如果检测失败，使用预设区域 - if not subtitle_region and frames: - height, width = frames[0]['image'].shape[:2] - default_region = self._get_config('hard_subtitle.detection.default_region', 'bottom') - subtitle_region = self.subtitle_detector.get_preset_region( - default_region, - height, - width - ) - - # 裁剪字幕区域 - if not subtitle_region: - cropped_frames = [frame['image'] for frame in frames] - else: - x, y, w, h = subtitle_region - cropped_frames = [] - for frame in frames: - image = frame['image'] - y = min(y, image.shape[0] - 1) - x = min(x, image.shape[1] - 1) - h = min(h, image.shape[0] - y) - w = min(w, image.shape[1] - x) - cropped = image[y:y+h, x:x+w] - cropped_frames.append(cropped) - - # OCR 识别 - ocr_input = [(cropped_frames[i], frames[i]['timestamp']) - for i in range(min(len(cropped_frames), len(frames)))] - ocr_results = self.ocr_processor.recognize_frames(ocr_input) - - # 整理结果 - subtitle_text = self._format_subtitle_text(ocr_results) - confidence = self._calculate_average_confidence(ocr_results) - - return { - 'subtitle_type': 'hard_subtitle', - 'text': subtitle_text, - 'segments': ocr_results, - 'confidence': confidence, - 'source': 'paddleocr' - } - - -# 测试函数 -def test_hard_subtitle_extractor(): - """测试硬字幕提取器""" - print("=" * 60) - print("硬字幕提取器测试") - print("=" * 60) - - # 创建提取器 - extractor = HardSubtitleExtractor() - - # 测试视频 - test_bvid = "BV1KtFzzqEUG" - - print(f"\n测试视频: {test_bvid}") - print("=" * 60) - - try: - # 测试1: 提取硬字幕（使用较少帧数进行快速测试） - print("\n【测试1】提取硬字幕...") - print(" 参数: 间隔=5秒, 最大帧数=5, 开始时间=0秒") - - result = extractor.extract_subtitle( - bvid=test_bvid, - interval_seconds=5.0, - max_frames=5, - start_time=0.0 - ) - - print(f"\n✅ 提取完成!") - print(f" 字幕类型: {result['subtitle_type']}") - print(f" 字幕来源: {result['source']}") - print(f" 平均置信度: {result['confidence']:.3f}") - print(f" 字幕文本长度: {len(result['text'])} 字符") - print(f" 字幕段落数: {len(result['segments'])}") - - # 显示部分字幕文本 - if result['text']: - print(f"\n 字幕文本预览 (前 3 段):") - for i, seg in enumerate(result['segments'][:3]): - print(f" [{i+1}] [{seg['time']:.1f}s] {seg['text']}") - - if len(result['segments']) > 3: - print(f" ... 还有 {len(result['segments']) - 3} 段") - else: - print(f"\n 字幕文本: (无)") - - print("\n" + "=" * 60) - print("✅ 测试通过！") - print("=" * 60) - - except Exception as e: - print(f"\n❌ 测试失败: {e}") - import traceback - traceback.print_exc() - - -if __name__ == '__main__': - test_hard_subtitle_extractor() diff --git a/skills/kb-workflow/src/extractors/ocr_processor.py b/skills/kb-workflow/src/extractors/ocr_processor.py deleted file mode 100644 index 6fd35ab..0000000 --- a/skills/kb-workflow/src/extractors/ocr_processor.py +++ /dev/null @@ -1,309 +0,0 @@ -""" -OCR 文字识别处理器 - -使用 PaddleOCR 实现视频帧文字识别功能，主要用于提取硬字幕 -""" - -import os -import re -import numpy as np -from typing import List, Dict, Any, Tuple -from paddleocr import PaddleOCR -import logging - -logger = logging.getLogger(__name__) - - -class OCRProcessor: - """ - OCR 文字识别处理器 - - 使用 PaddleOCR 从视频帧中识别文字，支持批量识别和结果过滤 - """ - - # 相似度阈值：用于判断两个文本是否相似（基于编辑距离） - SIMILARITY_THRESHOLD = 0.85 - - # 置信度阈值 - CONFIDENCE_THRESHOLD = 0.7 - - # 最小字幕长度（字符数） - MIN_SUBTITLE_LENGTH = 2 - - # 注意：Python re 模块不支持 \p{P} 等属性类，在 _is_noise 方法中使用自定义逻辑 - - def __init__(self, use_textline_orientation: bool = True, lang: str = 'ch', - use_gpu: bool = False): - """ - 初始化 OCR 处理器 - - Args: - use_textline_orientation: 是否使用文本行方向分类器（PaddleOCR 3.x 参数名） - lang: 语言支持，'ch' 为中英文混合 - use_gpu: 是否使用 GPU 加速（通过环境变量设置，如需启用请先设置 CUDA_VISIBLE_DEVICES） - """ - self.use_textline_orientation = use_textline_orientation - self.lang = lang - self.use_gpu = use_gpu - - # 初始化 PaddleOCR 实例 - self._ocr = None - self._initialize_ocr() - - def _initialize_ocr(self): - """初始化 PaddleOCR 实例""" - try: - # 强制禁用 oneDNN 和 MKL - os.environ['FLAGS_use_mkldnn'] = '0' - os.environ['MKL_SERVICE_FORCE_INTEL'] = '1' - os.environ['OMP_NUM_THREADS'] = '1' - os.environ['PADDLE_ON_INFER'] = '1' - - # PaddleOCR 3.x 版本参数 - self._ocr = PaddleOCR( - use_textline_orientation=self.use_textline_orientation, - lang=self.lang - ) - logger.info("PaddleOCR 初始化成功（oneDNN 已禁用）") - except Exception as e: - logger.error(f"PaddleOCR 初始化失败: {e}") - raise RuntimeError(f"无法初始化 PaddleOCR: {e}") - - def recognize_frames(self, frames: List[Tuple[np.ndarray, float]]) -> List[Dict[str, Any]]: - """ - 批量识别帧中的文字 - - Args: - frames: 帧列表，每个元素为 (图像数组, 时间戳) 元组 - - 图像数组：numpy.ndarray，形状为 (H, W, 3) 的 RGB 图像 - - 时间戳：float，单位为秒 - - Returns: - List[Dict[str, Any]]: 识别结果列表，每个结果包含： - - time: float, 时间戳（秒） - - text: str, 识别的文本 - - confidence: float, 置信度 (0-1) - """ - if not frames: - logger.warning("输入帧列表为空") - return [] - - results = [] - - for image, timestamp in frames: - try: - # 调用 PaddleOCR 进行识别 - # 注意：PaddleOCR 3.x 版本的 ocr() 方法参数已简化 - ocr_result = self._ocr.ocr(image) - - # PaddleOCR 返回格式: [[[box], (text, confidence)], ...] - if ocr_result and ocr_result[0]: - for line in ocr_result[0]: - box, (text, confidence) = line - if text and confidence: - results.append({ - 'time': timestamp, - 'text': text, - 'confidence': float(confidence) - }) - except Exception as e: - logger.warning(f"帧识别失败 (时间戳 {timestamp}s): {e}") - continue - - logger.info(f"原始识别结果数量: {len(results)}") - - # 过滤和清洗结果 - filtered_results = self._filter_and_clean(results) - logger.info(f"过滤后结果数量: {len(filtered_results)}") - - return filtered_results - - def _filter_and_clean(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """ - 过滤和清洗识别结果 - - Args: - results: 原始识别结果列表 - - Returns: - List[Dict[str, Any]]: 过滤后的结果列表 - """ - if not results: - return [] - - # 步骤 1: 按置信度过滤 - filtered = self._filter_by_confidence(results) - logger.debug(f"置信度过滤后: {len(filtered)}") - - # 步骤 2: 过滤噪声（纯符号、单字符等） - filtered = self._filter_noise(filtered) - logger.debug(f"噪声过滤后: {len(filtered)}") - - # 步骤 3: 去除重复或相似的文本 - filtered = self._remove_duplicates(filtered) - logger.debug(f"去重后: {len(filtered)}") - - # 步骤 4: 按时间戳排序 - filtered.sort(key=lambda x: x['time']) - - return filtered - - def _filter_by_confidence(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """ - 按置信度过滤结果 - - Args: - results: 识别结果列表 - - Returns: - List[Dict[str, Any]]: 高置信度结果 - """ - return [r for r in results if r['confidence'] >= self.CONFIDENCE_THRESHOLD] - - def _filter_noise(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """ - 过滤噪声结果 - - 过滤条件： - - 文本长度小于最小长度 - - 纯符号或空白字符 - - Args: - results: 识别结果列表 - - Returns: - List[Dict[str, Any]]: 过滤后的结果 - """ - filtered = [] - - for result in results: - text = result['text'].strip() - - # 过滤过短的文本 - if len(text) < self.MIN_SUBTITLE_LENGTH: - continue - - # 过滤纯符号或空白字符 - if self._is_noise(text): - continue - - filtered.append(result) - - return filtered - - def _is_noise(self, text: str) -> bool: - """ - 判断文本是否为噪声 - - Args: - text: 要判断的文本 - - Returns: - bool: 如果是噪声返回 True - """ - # 使用正则判断是否只包含符号和空白 - # 注意：这里用简单的正则，更复杂的判断可以后续优化 - try: - # 移除标点符号后检查是否为空 - # 先替换常见的中文标点 - cleaned = re.sub(r'[^\w\u4e00-\u9fff]', '', text) - return len(cleaned) == 0 - except: - # 如果正则出错，保守处理，不算噪声 - return False - - def _remove_duplicates(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """ - 去除重复或相似的文本 - - 基于编辑距离（Levenshtein distance）判断文本相似度 - - Args: - results: 识别结果列表（假设已按时间戳排序） - - Returns: - List[Dict[str, Any]]: 去重后的结果 - """ - if not results: - return [] - - deduped = [results[0]] - - for current in results[1:]: - last = deduped[-1] - - # 计算相似度 - similarity = self._calculate_similarity(current['text'], last['text']) - - # 如果相似度低于阈值，则保留 - if similarity < self.SIMILARITY_THRESHOLD: - deduped.append(current) - else: - # 相似文本：保留置信度更高的那个 - if current['confidence'] > last['confidence']: - deduped[-1] = current - - return deduped - - def _calculate_similarity(self, text1: str, text2: str) -> float: - """ - 计算两个文本的相似度（基于归一化编辑距离） - - Args: - text1: 文本1 - text2: 文本2 - - Returns: - float: 相似度 (0-1)，1 表示完全相同 - """ - if text1 == text2: - return 1.0 - - len1, len2 = len(text1), len(text2) - - if len1 == 0 or len2 == 0: - return 0.0 - - # 动态规划计算编辑距离 - dp = np.zeros((len1 + 1, len2 + 1)) - - for i in range(len1 + 1): - dp[i, 0] = i - - for j in range(len2 + 1): - dp[0, j] = j - - for i in range(1, len1 + 1): - for j in range(1, len2 + 1): - if text1[i-1] == text2[j-1]: - dp[i, j] = dp[i-1, j-1] - else: - dp[i, j] = min( - dp[i-1, j] + 1, # 删除 - dp[i, j-1] + 1, # 插入 - dp[i-1, j-1] + 1 # 替换 - ) - - edit_distance = dp[len1, len2] - max_len = max(len1, len2) - - # 归一化相似度：1 - (编辑距离 / 最大长度) - similarity = 1.0 - (edit_distance / max_len) - - return similarity - - def get_ocr_info(self) -> Dict[str, Any]: - """ - 获取 OCR 处理器信息 - - Returns: - Dict[str, Any]: OCR 配置信息 - """ - return { - 'use_textline_orientation': self.use_textline_orientation, - 'lang': self.lang, - 'use_gpu': self.use_gpu, - 'confidence_threshold': self.CONFIDENCE_THRESHOLD, - 'similarity_threshold': self.SIMILARITY_THRESHOLD, - 'min_subtitle_length': self.MIN_SUBTITLE_LENGTH - } diff --git a/skills/kb-workflow/src/extractors/subtitle_detector.py b/skills/kb-workflow/src/extractors/subtitle_detector.py deleted file mode 100644 index 4466c6a..0000000 --- a/skills/kb-workflow/src/extractors/subtitle_detector.py +++ /dev/null @@ -1,383 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -字幕区域检测器 - -用于自动检测B站视频中硬字幕的位置区域。 -支持预设区域定义、自动检测和结果缓存。 -""" - -import cv2 -import numpy as np -from typing import Dict, Tuple, Optional, List -from collections import OrderedDict - - -class SubtitleDetector: - """ - 字幕区域检测器 - - 功能： - 1. 预设区域定义（底部、中部、顶部） - 2. 自动字幕区域检测（基于OpenCV） - 3. 检测结果缓存机制 - """ - - # 预设区域名称 - REGION_BOTTOM = "bottom" - REGION_CENTER = "center" - REGION_TOP = "top" - - # 预设区域参数（相对于视频高度的比例） - PRESET_REGIONS = { - REGION_BOTTOM: { - "top_ratio": 0.80, # 底部20% - "bottom_ratio": 1.0, # 到底部 - "left_ratio": 0.1, # 左右各留10% - "right_ratio": 0.9 # 避免字幕在边缘 - }, - REGION_CENTER: { - "top_ratio": 0.40, # 中部区域 - "bottom_ratio": 0.60, - "left_ratio": 0.1, - "right_ratio": 0.9 - }, - REGION_TOP: { - "top_ratio": 0.0, # 顶部区域 - "bottom_ratio": 0.20, - "left_ratio": 0.1, - "right_ratio": 0.9 - } - } - - def __init__(self, cache_size: int = 50): - """ - 初始化字幕检测器 - - Args: - cache_size: 缓存最大容量（默认50个视频） - """ - self.cache = OrderedDict() - self.cache_size = cache_size - - # 检测参数 - self.min_region_height = 0.05 # 最小区域高度（5%） - self.max_region_height = 0.25 # 最大区域高度（25%） - self.text_density_threshold = 0.005 # 文字密度阈值 - - def get_preset_region(self, region_name: str, frame_height: int, - frame_width: int) -> Tuple[int, int, int, int]: - """ - 获取预设区域的坐标 - - Args: - region_name: 区域名称（bottom/center/top） - frame_height: 帧高度 - frame_width: 帧宽度 - - Returns: - Tuple[int, int, int, int]: (x, y, width, height) - - Raises: - ValueError: 无效的区域名称 - """ - if region_name not in self.PRESET_REGIONS: - raise ValueError(f"无效的区域名称: {region_name}. 可选值: {list(self.PRESET_REGIONS.keys())}") - - params = self.PRESET_REGIONS[region_name] - - x = int(frame_width * params["left_ratio"]) - y = int(frame_height * params["top_ratio"]) - width = int(frame_width * (params["right_ratio"] - params["left_ratio"])) - height = int(frame_height * (params["bottom_ratio"] - params["top_ratio"])) - - return (x, y, width, height) - - def auto_detect_subtitle_region(self, frame: np.ndarray, - video_id: Optional[str] = None, - use_cache: bool = True) -> Tuple[int, int, int, int]: - """ - 自动检测字幕区域 - - Args: - frame: 视频帧（numpy数组，BGR格式） - video_id: 视频ID（用于缓存，如BV号） - use_cache: 是否使用缓存 - - Returns: - Tuple[int, int, int, int]: (x, y, width, height) 字幕区域坐标 - """ - # 检查缓存 - if use_cache and video_id and video_id in self.cache: - cached_region = self.cache[video_id] - # 更新缓存顺序（LRU） - self.cache.move_to_end(video_id) - return cached_region - - # 执行自动检测 - detected_region = self._detect_text_region(frame) - - # 缓存结果 - if video_id: - self._update_cache(video_id, detected_region) - - return detected_region - - def _detect_text_region(self, frame: np.ndarray) -> Tuple[int, int, int, int]: - """ - 使用OpenCV检测文字密集区域 - - 策略： - 1. 预处理：灰度化、二值化 - 2. 使用MSER检测文字候选区域 - 3. 统计区域分布，找到最可能的字幕区域 - - Args: - frame: 输入帧（BGR格式） - - Returns: - Tuple[int, int, int, int]: (x, y, width, height) 检测到的字幕区域 - """ - height, width = frame.shape[:2] - - # 尝试MSER检测 - try: - mser = cv2.MSER_create( - _delta=5, - _min_area=100, - _max_area=int(width * height * 0.05), - _max_variation=0.25, - _min_diversity=0.2 - ) - - # 转换为灰度图 - gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) - - # MSER检测 - regions, _ = mser.detectRegions(gray) - - if regions and len(regions) > 5: # 有足够的检测区域 - # 计算所有区域的质心和边界框 - bboxes = [cv2.boundingRect(region.astype(int)) for region in regions] - - # 过滤：只保留白色/黄色文字区域（B站字幕常见颜色） - filtered_bboxes = self._filter_by_color(frame, bboxes) - - if filtered_bboxes: - # 分析区域分布 - region = self._analyze_region_distribution(filtered_bboxes, width, height) - if region: - return region - - except Exception as e: - # MSER失败，尝试备用方法 - pass - - # 备用方法：边缘检测 + 连通域 - return self._detect_by_edges(frame) - - def _filter_by_color(self, frame: np.ndarray, - bboxes: List[Tuple[int, int, int, int]]) -> List[Tuple[int, int, int, int]]: - """ - 根据颜色过滤候选区域 - - B站字幕通常是白色或黄色 - - Args: - frame: 原始帧（BGR格式） - bboxes: 边界框列表 - - Returns: - 过滤后的边界框列表 - """ - filtered = [] - - for x, y, w, h in bboxes: - if w < 20 or h < 10: # 太小的区域跳过 - continue - - # 提取区域 - roi = frame[y:y+h, x:x+w] - - # 计算平均颜色 - mean_color = cv2.mean(roi)[:3] # BGR - - # 判断是否接近白色或黄色 - # 白色：B、G、R都较高 - # 黄色：R和G高，B低 - - is_white = all(c > 180 for c in mean_color) - is_yellow = (mean_color[2] > 180 and mean_color[1] > 180 and mean_color[0] < 150) - - if is_white or is_yellow: - filtered.append((x, y, w, h)) - - return filtered - - def _analyze_region_distribution(self, bboxes: List[Tuple[int, int, int, int]], - width: int, height: int) -> Tuple[int, int, int, int]: - """ - 分析区域分布，找到最可能的字幕区域 - - Args: - bboxes: 边界框列表 - width: 帧宽度 - height: 帧高度 - - Returns: - Tuple[int, int, int, int]: (x, y, width, height) 字幕区域 - """ - if not bboxes: - return self.get_preset_region(self.REGION_BOTTOM, height, width) - - # 将区域按垂直位置分组 - bottom_regions = [] - - for x, y, w, h in bboxes: - # 计算区域的中心y坐标 - center_y = y + h // 2 - relative_y = center_y / height - - # 如果在底部60%的区域，可能是字幕 - if relative_y > 0.4: - bottom_regions.append((x, y, w, h)) - - if not bottom_regions: - # 如果没有找到底部区域，使用所有区域 - bottom_regions = bboxes - - # 计算合并区域 - if bottom_regions: - min_x = min(box[0] for box in bottom_regions) - max_x = max(box[0] + box[2] for box in bottom_regions) - min_y = min(box[1] for box in bottom_regions) - max_y = max(box[1] + box[3] for box in bottom_regions) - - region_width = max_x - min_x - region_height = max_y - min_y - - # 扩展区域以包含可能的文字 - margin_x = int(width * 0.02) - margin_y = int(height * 0.01) - - x = max(0, min_x - margin_x) - y = max(0, min_y - margin_y) - region_width = min(width - x, region_width + 2 * margin_x) - region_height = min(height - y, region_height + 2 * margin_y) - - # 确保区域高度在合理范围内 - min_height = int(height * self.min_region_height) - max_height = int(height * self.max_region_height) - - if region_height < min_height: - region_height = min_height - elif region_height > max_height: - region_height = max_height - - return (x, y, region_width, region_height) - - # 默认返回底部区域 - return self.get_preset_region(self.REGION_BOTTOM, height, width) - - def _detect_by_edges(self, frame: np.ndarray) -> Tuple[int, int, int, int]: - """ - 备用检测方法：使用边缘检测 - - Args: - frame: 输入帧 - - Returns: - Tuple[int, int, int, int]: (x, y, width, height) 字幕区域 - """ - height, width = frame.shape[:2] - - # 转换为灰度图 - gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) - - # 边缘检测 - edges = cv2.Canny(gray, 50, 150, apertureSize=3) - - # 形态学操作：水平线增强 - kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 1)) - horizontal_edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel) - - # 找到水平边缘密集的区域 - bottom_part = horizontal_edges[int(height * 0.5):, :] - - # 统计每行的边缘数量 - row_counts = np.sum(bottom_part, axis=1) - - # 找到边缘密集的行 - if len(row_counts) > 0: - threshold = np.max(row_counts) * 0.3 - text_rows = np.where(row_counts > threshold)[0] - - if len(text_rows) > 0: - # 计算字幕区域 - start_row = text_rows[0] + int(height * 0.5) - end_row = text_rows[-1] + int(height * 0.5) - - region_height = end_row - start_row - - # 扩展区域 - margin = int(height * 0.02) - y = max(int(height * 0.7), start_row - margin) - region_height = min(int(height * 0.25), region_height + 2 * margin) - - # 确保不超出帧边界 - if y + region_height > height: - region_height = height - y - - x = int(width * 0.1) - region_width = int(width * 0.8) - - return (x, y, region_width, region_height) - - # 默认返回底部区域 - return self.get_preset_region(self.REGION_BOTTOM, height, width) - - def _update_cache(self, video_id: str, region: Tuple[int, int, int, int]): - """ - 更新缓存 - - Args: - video_id: 视频ID - region: 检测到的区域 - """ - # 如果缓存已满，删除最旧的条目 - if len(self.cache) >= self.cache_size: - self.cache.popitem(last=False) - - # 添加新条目 - self.cache[video_id] = region - - def clear_cache(self, video_id: Optional[str] = None): - """ - 清空缓存 - - Args: - video_id: 如果指定，只删除该视频的缓存；否则清空所有缓存 - """ - if video_id: - if video_id in self.cache: - del self.cache[video_id] - else: - self.cache.clear() - - def get_cached_regions(self) -> Dict[str, Tuple[int, int, int, int]]: - """ - 获取所有缓存区域 - - Returns: - Dict[video_id, region]: 缓存的区域字典 - """ - return dict(self.cache) - - def get_cache_size(self) -> int: - """ - 获取当前缓存大小 - - Returns: - int: 缓存中的条目数 - """ - return len(self.cache) diff --git a/skills/kb-workflow/src/extractors/video_stream_extractor.py b/skills/kb-workflow/src/extractors/video_stream_extractor.py deleted file mode 100644 index dadf9c2..0000000 --- a/skills/kb-workflow/src/extractors/video_stream_extractor.py +++ /dev/null @@ -1,517 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -B站硬字幕提取器 - 视频流提取模块 - -从B站获取视频流URL并提取视频帧用于硬字幕识别 -""" - -import json -import time -from typing import List, Dict, Any, Optional, Tuple -from urllib.request import Request, urlopen -from urllib.parse import urlencode - -try: - import cv2 - OPENCV_AVAILABLE = True -except ImportError: - OPENCV_AVAILABLE = False - - -class VideoStreamExtractor: - """ - 视频流提取器 - - 负责从B站获取视频流URL并提取视频帧 - """ - - BASE_URL = "https://api.bilibili.com" - - def __init__(self): - """ - 初始化提取器 - """ - self._img_key = None - self._sub_key = None - - def _get_headers(self) -> Dict[str, str]: - """ - 获取请求头 - - Returns: - Dict[str, str]: 请求头字典 - """ - return { - 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36', - 'Referer': 'https://www.bilibili.com', - 'Accept': 'application/json, text/plain, */*', - } - - def _fetch_json(self, url: str, params: Optional[Dict] = None) -> Optional[Dict]: - """ - 发送HTTP GET请求并返回JSON数据 - - Args: - url: 请求URL - params: 查询参数 - - Returns: - Optional[Dict]: JSON数据或None - """ - try: - if params: - query = urlencode(params) - url = f"{url}?{query}" - - req = Request(url, headers=self._get_headers()) - with urlopen(req, timeout=30) as response: - return json.loads(response.read().decode('utf-8')) - except Exception as e: - print(f"请求失败 {url}: {e}") - return None - - def get_video_info(self, bvid: str) -> Optional[Dict[str, Any]]: - """ - 获取视频基本信息（包含cid） - - Args: - bvid: B站视频BV号 - - Returns: - Optional[Dict]: 视频信息字典，包含cid、aid等信息 - """ - api_url = f"{self.BASE_URL}/x/web-interface/view" - data = self._fetch_json(api_url, {'bvid': bvid}) - - if not data or data.get('code') != 0: - print(f"获取视频信息失败: {data}") - return None - - return data.get('data', {}) - - def get_video_stream_url( - self, - bvid: str, - quality: int = 80, - cid: Optional[str] = None - ) -> Optional[str]: - """ - 获取视频流URL - - Args: - bvid: B站视频BV号 - quality: 视频质量 (默认80，表示高清) - cid: 视频分P的CID（可选，如果不提供则自动获取） - - Returns: - Optional[str]: 视频流URL，失败返回None - """ - # 如果没有提供cid，先获取视频信息 - if not cid: - video_info = self.get_video_info(bvid) - if not video_info: - return None - cid = video_info.get('cid') - - if not cid: - print(f"无法获取视频cid: {bvid}") - return None - - # 调用播放地址API - playurl_api = f"{self.BASE_URL}/x/player/playurl" - params = { - 'bvid': bvid, - 'cid': cid, - 'qn': quality, - 'otype': 'json', - 'fnver': 0, - 'fnval': 0, - } - - data = self._fetch_json(playurl_api, params) - - if not data or data.get('code') != 0: - print(f"获取播放地址失败: {data}") - return None - - # 从durl列表获取第一个视频流URL - durl_list = data.get('data', {}).get('durl', []) - if not durl_list: - print(f"未找到视频流URL: {bvid}") - return None - - # 返回第一个分片的URL - stream_url = durl_list[0].get('url') - if not stream_url: - print(f"视频流URL为空: {bvid}") - return None - - # 处理相对URL（如果有） - if stream_url.startswith('//'): - stream_url = 'https:' + stream_url - elif stream_url.startswith('/'): - stream_url = 'https://www.bilibili.com' + stream_url - return stream_url - - def extract_frames( - self, - video_url: str, - interval_seconds: float = 5.0, - max_frames: int = 500, - start_time: float = 0.0, - use_download: bool = True - ) -> List[Dict[str, Any]]: - """ - 从视频流中提取帧 - - Args: - video_url: 视频流URL - interval_seconds: 提取间隔（秒），默认5秒 - max_frames: 最大帧数限制，默认500帧 - start_time: 开始时间（秒），默认0秒 - use_download: 是否先下载视频（默认True，因为OpenCV无法直接打开HTTPS流） - - Returns: - List[Dict[str, Any]]: 帧列表，每帧包含: - - timestamp: 时间戳（秒） - - frame_id: 帧编号 - - image: 图像数据（numpy数组） - """ - if not OPENCV_AVAILABLE: - raise RuntimeError("OpenCV未安装，请安装opencv-python: pip install opencv-python") - - import tempfile - import os - - frames = [] - temp_file = None - cap = None - - try: - # 如果是URL且需要下载，先下载到临时文件 - if use_download and (video_url.startswith('http://') or video_url.startswith('https://')): - temp_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) - temp_path = temp_file.name - temp_file.close() - - print(f"视频为URL，先下载到临时文件: {temp_path}") - if not self._download_video(video_url, temp_path): - raise RuntimeError("视频下载失败") - - video_source = temp_path - else: - video_source = video_url - - # 打开视频流 - cap = cv2.VideoCapture(video_source) - - if not cap.isOpened(): - raise RuntimeError(f"无法打开视频流: {video_source}") - - # 获取视频信息 - fps = cap.get(cv2.CAP_PROP_FPS) - total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - duration = total_frames / fps if fps > 0 else 0 - print(f"视频信息: FPS={fps}, 总帧数={total_frames}, 时长={duration:.2f}秒") - - # 如果无法获取FPS，使用默认值 - if fps <= 0: - fps = 30.0 - print(f"无法获取FPS，使用默认值: {fps}") - - # 计算帧间隔 - frame_interval = int(fps * interval_seconds) - print(f"提取间隔: {interval_seconds}秒 ({frame_interval}帧)") - - # 跳转到起始位置 - start_frame = int(fps * start_time) - if start_frame > 0: - cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame) - - frame_count = 0 - extracted_count = 0 - - while frame_count < max_frames: - # 读取帧 - ret, frame = cap.read() - - if not ret: - print("已到达视频末尾或读取失败") - break - - # 获取当前帧位置 - current_frame = int(cap.get(cv2.CAP_PROP_POS_FRAMES)) - current_time = current_frame / fps - - # 检查是否达到提取间隔 - if (current_frame - start_frame) % frame_interval == 0: - frames.append({ - 'timestamp': current_time, - 'frame_id': extracted_count, - 'image': frame.copy() - }) - extracted_count += 1 - print(f"提取帧 {extracted_count}: 时间={current_time:.2f}秒, 帧号={current_frame}") - - # 检查是否达到最大帧数 - if len(frames) >= max_frames: - break - - frame_count += 1 - - except Exception as e: - print(f"提取帧时出错: {e}") - raise - finally: - # 释放资源 - if cap is not None: - cap.release() - - # 删除临时文件 - if temp_file and os.path.exists(temp_path): - try: - os.unlink(temp_path) - print(f"已删除临时文件: {temp_path}") - except Exception as e: - print(f"删除临时文件失败: {e}") - - print(f"提取完成: 共提取 {len(frames)} 帧") - return frames - - def _download_video(self, video_url: str, output_path: str, timeout: int = 300) -> bool: - """ - 下载视频到本地文件 - - Args: - video_url: 视频流URL - output_path: 输出文件路径 - timeout: 超时时间（秒） - - Returns: - bool: 下载成功返回True - """ - import os - - try: - print(f"开始下载视频...") - req = Request(video_url, headers=self._get_headers()) - - with urlopen(req, timeout=timeout) as response: - total_size = int(response.headers.get('content-length', 0)) - downloaded = 0 - - with open(output_path, 'wb') as f: - while True: - chunk = response.read(8192) - if not chunk: - break - f.write(chunk) - downloaded += len(chunk) - - if total_size > 0: - progress = (downloaded / total_size) * 100 - print(f"下载进度: {progress:.1f}% ({downloaded}/{total_size} bytes)", end='\r') - - print(f"\n下载完成: {output_path}") - return True - - except Exception as e: - print(f"\n下载失败: {e}") - return False - - def extract_frames_from_bvid( - self, - bvid: str, - interval_seconds: float = 5.0, - max_frames: int = 500, - start_time: float = 0.0, - quality: int = 80, - use_download: bool = True - ) -> List[Dict[str, Any]]: - """ - 从B站视频BV号提取帧（便捷方法） - - Args: - bvid: B站视频BV号 - interval_seconds: 提取间隔（秒） - max_frames: 最大帧数 - start_time: 开始时间（秒） - quality: 视频质量 - use_download: 是否先下载视频（默认True） - - Returns: - List[Dict[str, Any]]: 帧列表 - """ - print(f"开始处理视频: {bvid}") - - # 获取视频流URL - print("步骤1: 获取视频流URL...") - stream_url = self.get_video_stream_url(bvid, quality=quality) - - if not stream_url: - raise RuntimeError(f"无法获取视频流URL: {bvid}") - - print(f"视频流URL: {stream_url[:100]}...") - - # 提取帧 - print("\n步骤2: 提取视频帧...") - frames = self.extract_frames( - stream_url, - interval_seconds=interval_seconds, - max_frames=max_frames, - start_time=start_time, - use_download=use_download - ) - - return frames - - def save_frame(self, frame: Dict[str, Any], output_path: str) -> bool: - """ - 保存单帧为图片文件 - - Args: - frame: 帧数据字典 - output_path: 输出文件路径 - - Returns: - bool: 保存成功返回True - """ - if not OPENCV_AVAILABLE: - raise RuntimeError("OpenCV未安装") - - try: - cv2.imwrite(output_path, frame['image']) - return True - except Exception as e: - print(f"保存帧失败: {e}") - return False - - def save_frames( - self, - frames: List[Dict[str, Any]], - output_dir: str, - prefix: str = "frame" - ) -> List[str]: - """ - 批量保存帧为图片文件 - - Args: - frames: 帧列表 - output_dir: 输出目录 - prefix: 文件名前缀 - - Returns: - List[str]: 保存成功的文件路径列表 - """ - import os - - if not os.path.exists(output_dir): - os.makedirs(output_dir) - - saved_files = [] - for frame in frames: - timestamp_str = f"{frame['timestamp']:.2f}".replace('.', '_') - filename = f"{prefix}_{frame['frame_id']:04d}_t{timestamp_str}.jpg" - output_path = os.path.join(output_dir, filename) - - if self.save_frame(frame, output_path): - saved_files.append(output_path) - print(f"已保存: {output_path}") - - return saved_files - - -# 测试函数 -def test_extractor(): - """测试VideoStreamExtractor""" - print("=" * 60) - print("B站视频流提取器测试") - print("=" * 60) - - # 创建提取器 - extractor = VideoStreamExtractor() - - # 测试视频BV号 - test_bvid = "BV1KtFzzqEUG" - - print(f"\n测试视频: {test_bvid}") - print("=" * 60) - - try: - # 测试1: 获取视频流URL - print("\n【测试1】获取视频流URL...") - stream_url = extractor.get_video_stream_url(test_bvid) - - if stream_url: - print(f"✅ 成功获取视频流URL") - print(f" URL长度: {len(stream_url)} 字符") - print(f" URL前缀: {stream_url[:100]}...") - else: - print("❌ 获取视频流URL失败") - return - - # 测试2: 提取帧（限制为10帧，间隔5秒） - print("\n【测试2】提取视频帧...") - print(f" 参数: 间隔=5秒, 最大帧数=10, 开始时间=0秒") - - frames = extractor.extract_frames( - stream_url, - interval_seconds=5.0, - max_frames=10, - start_time=0.0, - use_download=True - ) - - print(f"✅ 提取完成: 共提取 {len(frames)} 帧") - - # 显示提取的帧信息 - if frames: - print("\n帧信息预览:") - print("-" * 60) - for i, frame in enumerate(frames[:5]): # 只显示前5帧 - timestamp = frame['timestamp'] - frame_id = frame['frame_id'] - height, width = frame['image'].shape[:2] - print(f" 帧 {frame_id}: 时间={timestamp:.2f}秒, 尺寸={width}x{height}") - - if len(frames) > 5: - print(f" ... 还有 {len(frames) - 5} 帧") - - # 测试3: 从BV号直接提取（便捷方法） - print("\n【测试3】从BV号直接提取（便捷方法）...") - frames2 = extractor.extract_frames_from_bvid( - test_bvid, - interval_seconds=5.0, - max_frames=10, - start_time=0.0 - ) - - print(f"✅ 提取完成: 共提取 {len(frames2)} 帧") - - # 测试4: 保存部分帧 - if frames: - print("\n【测试4】保存前3帧到临时目录...") - import tempfile - temp_dir = tempfile.mkdtemp(prefix="bilibili_frames_") - print(f" 临时目录: {temp_dir}") - - saved_files = extractor.save_frames(frames[:3], temp_dir, prefix="test") - print(f"✅ 保存完成: {len(saved_files)} 个文件") - - # 显示保存的文件 - for f in saved_files: - print(f" {f}") - - print("\n" + "=" * 60) - print("✅ 所有测试通过！") - print("=" * 60) - - except Exception as e: - print(f"\n❌ 测试失败: {e}") - import traceback - traceback.print_exc() - - -if __name__ == '__main__': - test_extractor() diff --git a/skills/kb-workflow/src/git_sync.py b/skills/kb-workflow/src/git_sync.py deleted file mode 100644 index 5fc9ab6..0000000 --- a/skills/kb-workflow/src/git_sync.py +++ /dev/null @@ -1,195 +0,0 @@ -"""Obsidian KB - Git 同步""" - -import os -import subprocess -from pathlib import Path -from typing import Optional, List -from datetime import datetime - -from .config import get_config - - -class GitSync: - """Git仓库同步管理""" - - def __init__(self, repo_path: Optional[str] = None): - config = get_config() - self.repo_path = repo_path or config.obsidian_path - self.git_url = config.git_repo - self.git_token = config.git_token - - def _run_git(self, args: List[str], cwd: Optional[str] = None) -> tuple[int, str, str]: - """运行git命令""" - cmd = ['git'] + args - try: - result = subprocess.run( - cmd, - cwd=cwd or self.repo_path, - capture_output=True, - text=True, - encoding='utf-8' - ) - return result.returncode, result.stdout, result.stderr - except Exception as e: - return -1, "", str(e) - - def is_git_repo(self) -> bool: - """检查是否为git仓库""" - git_dir = os.path.join(self.repo_path, '.git') - return os.path.isdir(git_dir) - - def init_repo(self) -> bool: - """初始化git仓库""" - if self.is_git_repo(): - return True - - os.makedirs(self.repo_path, exist_ok=True) - - # git init - code, _, _ = self._run_git(['init']) - if code != 0: - return False - - # 配置git用户（使用默认值） - self._run_git(['config', 'user.email', 'obsidian-kb@localhost']) - self._run_git(['config', 'user.name', 'Obsidian KB']) - - # 添加remote - if self.git_url: - # 如果提供了token，构建带认证的URL - if self.git_token: - auth_url = self._build_auth_url(self.git_url, self.git_token) - self._run_git(['remote', 'add', 'origin', auth_url]) - else: - self._run_git(['remote', 'add', 'origin', self.git_url]) - - return True - - def _build_auth_url(self, url: str, token: str) -> str: - """构建带认证的git URL""" - if url.startswith('https://github.com/'): - return url.replace('https://', f'https://x-access-token:{token}@') - elif url.startswith('https://'): - return url.replace('https://', f'https://oauth2:{token}@') - return url - - def add_files(self, files: List[str]) -> bool: - """添加文件到暂存区""" - if not files: - return True - - # 添加所有指定文件 - for file_path in files: - # 转换为相对路径 - rel_path = os.path.relpath(file_path, self.repo_path) - code, _, _ = self._run_git(['add', rel_path]) - if code != 0: - return False - - return True - - def add_all(self) -> bool: - """添加所有变更""" - code, _, _ = self._run_git(['add', '.']) - return code == 0 - - def has_changes(self) -> bool: - """检查是否有未提交的变更""" - code, stdout, _ = self._run_git(['status', '--porcelain']) - if code != 0: - return False - return bool(stdout.strip()) - - def commit(self, message: Optional[str] = None, content_title: Optional[str] = None) -> bool: - """提交变更""" - if not self.has_changes(): - return True - - if not message: - message = self._generate_commit_message(content_title) - - code, _, _ = self._run_git(['commit', '-m', message]) - return code == 0 - - def _generate_commit_message(self, content_title: Optional[str] = None) -> str: - """生成提交信息""" - timestamp = datetime.now().strftime('%Y-%m-%d %H:%M') - if content_title: - return f"Add: {content_title[:50]}" + ("..." if len(content_title) > 50 else "") + f" [{timestamp}]" - return f"Update KB notes [{timestamp}]" - - def push(self, branch: str = 'main') -> bool: - """推送到远程""" - # 检查是否有origin - code, stdout, _ = self._run_git(['remote']) - if code != 0 or 'origin' not in stdout: - return False - - # 检查分支是否存在，不存在则创建 - code, stdout, _ = self._run_git(['branch', '--show-current']) - current_branch = stdout.strip() - - if not current_branch: - # 创建初始分支 - self._run_git(['checkout', '-b', branch]) - - # 推送 - code, _, _ = self._run_git(['push', '-u', 'origin', branch]) - return code == 0 - - def pull(self, branch: str = 'main') -> bool: - """从远程拉取""" - code, _, _ = self._run_git(['pull', 'origin', branch]) - return code == 0 - - def sync(self, files: Optional[List[str]] = None, content_title: Optional[str] = None) -> tuple[bool, str]: - """完整同步流程""" - # 初始化仓库 - if not self.init_repo(): - return False, "Failed to initialize git repository" - - # 添加文件 - if files: - if not self.add_files(files): - return False, "Failed to add files" - else: - if not self.add_all(): - return False, "Failed to add files" - - # 提交 - if not self.commit(content_title=content_title): - if not self.has_changes(): - return True, "No changes to commit" - return False, "Failed to commit" - - # 推送 - if self.git_url and not self.push(): - return False, "Failed to push" - - return True, "Sync completed successfully" - - def get_status(self) -> dict: - """获取仓库状态""" - status = { - 'is_repo': self.is_git_repo(), - 'has_changes': False, - 'branch': '', - 'remote': '' - } - - if not status['is_repo']: - return status - - status['has_changes'] = self.has_changes() - - _, stdout, _ = self._run_git(['branch', '--show-current']) - status['branch'] = stdout.strip() - - _, stdout, _ = self._run_git(['remote', 'get-url', 'origin']) - url = stdout.strip() - # 隐藏token - if '@' in url: - url = 'https://' + url.split('@', 1)[1] - status['remote'] = url - - return status diff --git a/skills/kb-workflow/src/light_scanner.py b/skills/kb-workflow/src/light_scanner.py deleted file mode 100644 index ea11bca..0000000 --- a/skills/kb-workflow/src/light_scanner.py +++ /dev/null @@ -1,228 +0,0 @@ -"""Obsidian KB - 轻量扫描器 -C4.1: 实现轻量扫描，生成一句话摘要、关键词和质量评分 -""" -import re -from typing import List, Tuple -from src.models import Content, ContentStatus, QualityMetrics -from src.config import get_config - - -class LightScanner: - """轻量内容扫描器 - 快速生成摘要和关键词""" - - def __init__(self, ai_client=None): - self.config = get_config() - self.ai_client = ai_client - - def scan(self, content: Content, content_text: str) -> Content: - """ - 对内容进行轻量扫描 - - Args: - content: Content对象 - content_text: 内容文本（字幕或正文） - - Returns: - 更新后的Content对象 - """ - # 截断文本避免过长 - text_sample = self._get_text_sample(content_text) - - # 生成一句话摘要 - summary = self._generate_summary(text_sample, content.content_type.value) - - # 提取关键词 - keywords = self._extract_keywords(text_sample) - - # 质量评分 - quality_score = self._calculate_quality_score(text_sample, content) - - # 更新Content对象 - content.summary = summary - content.key_points = keywords # 使用key_points存储关键词 - content.quality_metrics.overall_score = quality_score / 10 # 转换为0-1范围 - content.update_status(ContentStatus.SCANNED) - - return content - - def _get_text_sample(self, text: str, max_chars: int = 3000) -> str: - """获取文本样本，优先取开头、中间和结尾""" - if len(text) <= max_chars: - return text - - # 分为三部分：开头40%、中间20%、结尾40% - start_len = int(max_chars * 0.4) - end_len = int(max_chars * 0.4) - middle_len = max_chars - start_len - end_len - - start = text[:start_len] - middle_start = len(text) // 2 - middle_len // 2 - middle = text[middle_start:middle_start + middle_len] - end = text[-end_len:] - - return f"{start}\n\n[...中间部分...]\n\n{middle}\n\n[...省略...]\n\n{end}" - - def _generate_summary(self, text_sample: str, content_type: str) -> str: - """ - 生成一句话摘要 - 如果有AI客户端则调用AI，否则使用简单启发式方法 - """ - if self.ai_client: - return self._generate_summary_with_ai(text_sample, content_type) - else: - return self._generate_summary_heuristic(text_sample) - - def _generate_summary_with_ai(self, text_sample: str, content_type: str) -> str: - """使用AI生成摘要""" - prompt = f"""请为以下{content_type}内容生成一句话摘要（不超过50字）： - -{text_sample[:2000]} - -要求： -1. 一句话概括核心内容 -2. 不超过50字 -3. 突出主要观点和价值 - -摘要：""" - - try: - response = self.ai_client.quick_generate(prompt, max_tokens=100) - # 清理并截断 - summary = response.strip().replace('\n', ' ') - if len(summary) > 60: - summary = summary[:57] + "..." - return summary - except Exception as e: - print(f"[LightScanner] AI生成摘要失败: {e}") - return self._generate_summary_heuristic(text_sample) - - def _generate_summary_heuristic(self, text: str) -> str: - """启发式生成简单摘要""" - # 取前100个字符作为简单摘要 - sentences = re.split(r'[。！？.!?]', text) - for sent in sentences: - sent = sent.strip() - if len(sent) > 20 and len(sent) < 100: - return sent[:50] + "..." if len(sent) > 50 else sent - - # 回退：取前50字 - text = text.replace('\n', ' ').strip() - return text[:47] + "..." if len(text) > 50 else text - - def _extract_keywords(self, text: str) -> List[str]: - """ - 提取5个关键词 - 如果有AI客户端则调用AI，否则使用简单词频分析 - """ - if self.ai_client: - return self._extract_keywords_with_ai(text) - else: - return self._extract_keywords_heuristic(text) - - def _extract_keywords_with_ai(self, text: str) -> List[str]: - """使用AI提取关键词""" - prompt = f"""请从以下内容中提取5个最重要的关键词，用逗号分隔： - -{text[:1500]} - -关键词（5个，逗号分隔）：""" - - try: - response = self.ai_client.quick_generate(prompt, max_tokens=100) - # 解析逗号分隔的关键词 - keywords = [k.strip() for k in response.split('，') if k.strip()] - if len(keywords) < 5: - keywords = [k.strip() for k in response.split(',') if k.strip()] - - # 确保最多5个，每个不超过10字 - keywords = [k[:10] for k in keywords[:5]] - - # 如果不足5个，补充通用关键词 - while len(keywords) < 5: - keywords.append(f"关键词{len(keywords)+1}") - - return keywords[:5] - except Exception as e: - print(f"[LightScanner] AI提取关键词失败: {e}") - return self._extract_keywords_heuristic(text) - - def _extract_keywords_heuristic(self, text: str) -> List[str]: - """启发式提取关键词（基于词频）""" - # 简单的中文/英文分词和词频统计 - import re - from collections import Counter - - # 提取中文字符串（2-6字） - chinese_words = re.findall(r'[\u4e00-\u9fff]{2,6}', text) - - # 提取英文单词 - english_words = re.findall(r'\b[a-zA-Z]{4,12}\b', text.lower()) - - # 停用词过滤 - stopwords = {'这个', '那个', '什么', '一个', '我们', '你们', '他们', '可以', '进行'} - chinese_words = [w for w in chinese_words if w not in stopwords] - - # 统计词频 - all_words = chinese_words + english_words - if not all_words: - return ["内容", "分析", "知识", "笔记", "学习"] - - word_freq = Counter(all_words) - top_words = [word for word, _ in word_freq.most_common(5)] - - # 确保有5个 - defaults = ["内容", "分析", "知识", "笔记", "学习"] - while len(top_words) < 5: - top_words.append(defaults[len(top_words)]) - - return top_words[:5] - - def _calculate_quality_score(self, text: str, content: Content) -> float: - """ - 计算内容质量评分（0-10） - 基于多个维度：文本长度、信息密度、结构完整性 - """ - score = 5.0 # 基础分 - - # 1. 文本长度评分 (0-3分) - text_len = len(text) - if text_len > 10000: - score += 3.0 - elif text_len > 5000: - score += 2.5 - elif text_len > 2000: - score += 2.0 - elif text_len > 1000: - score += 1.0 - elif text_len > 500: - score += 0.5 - - # 2. 信息密度评分 (0-2分) - 基于句子平均长度 - sentences = re.split(r'[。！？.!?]', text) - sentences = [s for s in sentences if s.strip()] - if sentences: - avg_sent_len = text_len / len(sentences) - if 15 <= avg_sent_len <= 50: - score += 2.0 - elif 10 <= avg_sent_len <= 80: - score += 1.0 - - # 3. 结构完整性 (0-2分) - # 检查是否有章节标题模式 - if re.search(r'[#\d]+[.、\s]', text): - score += 1.0 - # 检查是否有时间戳（视频字幕） - if content.duration and content.duration > 0: - score += 1.0 - - # 4. 多样性评分 (0-2分) - 基于唯一词比例 - words = re.findall(r'[\u4e00-\u9fff]', text) - if words: - unique_ratio = len(set(words)) / len(words) - if unique_ratio > 0.5: - score += 2.0 - elif unique_ratio > 0.3: - score += 1.0 - - # 确保在0-10范围内 - return round(max(0.0, min(10.0, score)), 1) diff --git a/skills/kb-workflow/src/llm.py b/skills/kb-workflow/src/llm.py deleted file mode 100644 index c7bcf54..0000000 --- a/skills/kb-workflow/src/llm.py +++ /dev/null @@ -1,60 +0,0 @@ -"""LLM processor for content analysis and summarization.""" - -from typing import Any, Dict, List - - -class LLMProcessor: - """Processes content using LLM for analysis and summarization.""" - - def __init__(self, provider: str = 'openai'): - """Initialize LLM processor. - - Args: - provider: LLM provider name - """ - self.provider = provider - - def analyze(self, content: str) -> Dict[str, Any]: - """Analyze content quality and relevance. - - Args: - content: Text content to analyze - - Returns: - Analysis results with quality score, relevance, and topics - """ - # Placeholder - implement actual LLM integration - return { - 'quality_score': 0.7, - 'relevance': 'high', - 'topics': ['technology', 'tutorial'], - 'summary': content[:200] + '...' if len(content) > 200 else content - } - - def summarize(self, content: str, max_length: int = 500) -> str: - """Generate summary of content. - - Args: - content: Text content to summarize - max_length: Maximum summary length - - Returns: - Summarized text - """ - # Placeholder - implement actual LLM summarization - if len(content) <= max_length: - return content - return content[:max_length] + "..." - - def generate_tags(self, content: str, title: str = '') -> List[str]: - """Generate tags for content. - - Args: - content: Text content - title: Content title - - Returns: - List of suggested tags - """ - # Placeholder - implement actual tag generation - return ['imported', 'unread'] diff --git a/skills/kb-workflow/src/models.py b/skills/kb-workflow/src/models.py deleted file mode 100644 index 5bc1709..0000000 --- a/skills/kb-workflow/src/models.py +++ /dev/null @@ -1,213 +0,0 @@ -"""Obsidian KB - 知识库内容管理模型""" - -from enum import Enum -from dataclasses import dataclass, field, asdict -from typing import Optional, List, Dict, Any -from datetime import datetime -import uuid -import json - - -class ContentType(Enum): - """内容类型""" - VIDEO = "video" - ARTICLE = "article" - - -class ContentStatus(Enum): - """内容处理状态""" - NEW = "new" # 新发现 - SCANNED = "scanned" # 已扫描元数据 - CONFIRMED = "confirmed" # 已确认处理 - ANALYZED = "analyzed" # 已AI分析 - GENERATED = "generated" # 已生成笔记 - SKIPPED = "skipped" # 已跳过 - - -@dataclass -class ContentChunk: - """内容块 - 用于分段处理""" - id: str = field(default_factory=lambda: str(uuid.uuid4())[:8]) - content: str = "" # 内容文本 - start_time: Optional[int] = None # 开始时间（秒） - end_time: Optional[int] = None # 结束时间（秒） - - def to_dict(self) -> Dict[str, Any]: - return asdict(self) - - @classmethod - def from_dict(cls, data: Dict[str, Any]) -> "ContentChunk": - return cls(**data) - - -@dataclass -class ContentAnalysis: - """内容分析结果 - 用于模板渲染""" - one_liner: str = "" # 一句话摘要 - summary: str = "" # 详细摘要 - key_points: List[str] = field(default_factory=list) # 关键要点 - tags: List[str] = field(default_factory=list) # 标签 - quality_score: float = 0.0 # 质量评分 0-100 - - # 教程视频专用字段 - prerequisites: List[str] = field(default_factory=list) # 前置知识 - outline: List[Dict[str, Any]] = field(default_factory=list) # 大纲分段 - pitfalls: List[str] = field(default_factory=list) # 常见坑点 - practice_advice: List[str] = field(default_factory=list) # 实践建议 - - def to_dict(self) -> Dict[str, Any]: - return asdict(self) - - @classmethod - def from_dict(cls, data: Dict[str, Any]) -> "ContentAnalysis": - return cls(**data) - - def is_high_quality(self, threshold: float = 60.0) -> bool: - """判断是否高质量内容""" - return self.quality_score >= threshold - - -@dataclass -class QualityMetrics: - """内容质量指标""" - overall_score: float = 0.0 # 综合评分 0-1 - information_density: float = 0.0 # 信息密度 - originality: float = 0.0 # 原创性 - practicality: float = 0.0 # 实用性 - depth: float = 0.0 # 深度 - - def to_dict(self) -> Dict[str, Any]: - return asdict(self) - - @classmethod - def from_dict(cls, data: Dict[str, Any]) -> "QualityMetrics": - return cls(**data) - - -@dataclass -class AnalysisResult: - """AI深度分析结果""" - main_topics: List[str] = field(default_factory=list) # 主要主题 - key_insights: List[str] = field(default_factory=list) # 关键洞察 - action_items: List[str] = field(default_factory=list) # 行动项 - summary: str = "" # 摘要 - timeline: List[Dict[str, Any]] = field(default_factory=list) # 时间线 - recommended_tags: List[str] = field(default_factory=list) # 推荐标签 - full_analysis: str = "" # 完整分析 - timeline_summary: List[Dict[str, Any]] = field(default_factory=list) # 时间线摘要（兼容字段） - content_structure: str = "" # 内容结构描述 - target_audience: str = "" # 目标受众 - difficulty_level: str = "intermediate" # 难度级别 - - def to_dict(self) -> Dict[str, Any]: - return asdict(self) - - @classmethod - def from_dict(cls, data: Dict[str, Any]) -> "AnalysisResult": - return cls(**data) - - -@dataclass -class TimelineEntry: - """时间线条目""" - timestamp: str - title: str - description: str - - def to_dict(self) -> Dict[str, str]: - return asdict(self) - - @classmethod - def from_dict(cls, data: Dict[str, str]) -> "TimelineEntry": - return cls(**data) - - -@dataclass -class Content: - """内容数据模型""" - # 基本信息 - id: str = field(default_factory=lambda: str(uuid.uuid4())[:8]) - source: str = "" # 来源: bilibili, youtube, zhihu, local - source_id: str = "" # 来源唯一ID - url: str = "" - title: str = "" - author: str = "" - description: str = "" - duration: Optional[int] = None # 视频时长(秒) - - # 内容类型与状态 - content_type: ContentType = ContentType.VIDEO - status: ContentStatus = ContentStatus.NEW - - # 元数据 - created_at: str = field(default_factory=lambda: datetime.now().isoformat()) - processed_at: Optional[str] = None - - # 原始内容 - transcript: str = "" # 字幕/原文 - - # AI分析结果 - summary: str = "" # 摘要 - one_liner: str = "" # 一句话摘要 - key_points: List[str] = field(default_factory=list) # 关键要点 - tags: List[str] = field(default_factory=list) # 标签 - quality_score: float = 0.0 # 质量评分 - analysis: Optional[ContentAnalysis] = None # 详细分析结果 - outline: List[str] = field(default_factory=list) # 大纲 - timeline: List[TimelineEntry] = field(default_factory=list) # 时间线 - quality_metrics: QualityMetrics = field(default_factory=QualityMetrics) - - # 分类与标签 - category: str = "" # 主题分类 - - # 输出 - output_path: Optional[str] = None # 生成的笔记路径 - note_path: Optional[str] = None # 别名，用于兼容 - - def to_dict(self) -> Dict[str, Any]: - """序列化为字典""" - data = asdict(self) - data['content_type'] = self.content_type.value - data['status'] = self.status.value - data['quality_metrics'] = self.quality_metrics.to_dict() - data['timeline'] = [e.to_dict() for e in self.timeline] - return data - - @classmethod - def from_dict(cls, data: Dict[str, Any]) -> "Content": - """从字典反序列化""" - # 处理枚举 - data['content_type'] = ContentType(data.get('content_type', 'video')) - data['status'] = ContentStatus(data.get('status', 'new')) - - # 处理嵌套对象 - if 'quality_metrics' in data and isinstance(data['quality_metrics'], dict): - data['quality_metrics'] = QualityMetrics.from_dict(data['quality_metrics']) - else: - data['quality_metrics'] = QualityMetrics() - - if 'timeline' in data and isinstance(data['timeline'], list): - data['timeline'] = [TimelineEntry.from_dict(e) for e in data['timeline']] - else: - data['timeline'] = [] - - return cls(**{k: v for k, v in data.items() if k in [f.name for f in cls.__dataclass_fields__.values()]}) - - def to_json(self) -> str: - """序列化为JSON字符串""" - return json.dumps(self.to_dict(), ensure_ascii=False, indent=2) - - @classmethod - def from_json(cls, json_str: str) -> "Content": - """从JSON字符串反序列化""" - return cls.from_dict(json.loads(json_str)) - - def is_high_quality(self, threshold: float = 0.7) -> bool: - """判断是否高质量内容""" - return self.quality_metrics.overall_score >= threshold - - def update_status(self, status: ContentStatus): - """更新状态""" - self.status = status - if status == ContentStatus.GENERATED: - self.processed_at = datetime.now().isoformat() diff --git a/skills/kb-workflow/src/outputs/__init__.py b/skills/kb-workflow/src/outputs/__init__.py deleted file mode 100644 index c2331eb..0000000 --- a/skills/kb-workflow/src/outputs/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# Outputs module for obsidian-kb -from .obsidian_writer import ObsidianWriter -from .git_sync import GitSync - -__all__ = ['ObsidianWriter', 'GitSync'] diff --git a/skills/kb-workflow/src/outputs/git_sync.py b/skills/kb-workflow/src/outputs/git_sync.py deleted file mode 100644 index ea5a9d2..0000000 --- a/skills/kb-workflow/src/outputs/git_sync.py +++ /dev/null @@ -1,275 +0,0 @@ -""" -Git Sync - 将 Obsidian vault 同步到 Git 仓库 -""" -import os -import subprocess -from pathlib import Path -from typing import Optional, Dict, Any - - -class GitSync: - """Git 同步器""" - - def __init__(self, config): - """ - 初始化同步器 - - Args: - config: 配置对象 - """ - self.config = config - self.vault_path = config.vault_path - - def sync(self, config) -> bool: - """ - 同步到 Git 仓库 - - 步骤: - 1. 检查 git.enabled - 2. git add - 3. git commit（使用 commit_message_template） - 4. 如果 auto_push=true，执行 git push - - Args: - config: 配置对象 - - Returns: - 是否成功 - """ - # 检查是否启用 Git - if not self._is_enabled(): - print("Git 同步未启用，跳过") - return True - - # 检查是否在 Git 仓库中 - if not self._is_git_repo(): - print(f"目录 {self.vault_path} 不是 Git 仓库，跳过同步") - return False - - try: - # 添加更改 - if not self._git_add(): - print("git add 失败") - return False - - # 提交更改 - if not self._git_commit(): - print("git commit 失败") - return False - - # 推送更改（如果配置了 auto_push） - if self._should_auto_push(): - if not self._git_push(): - print("git push 失败") - return False - else: - print("Git 同步完成（包含推送）") - else: - print("Git 同步完成（仅提交）") - - return True - - except Exception as e: - print(f"Git 同步失败: {e}") - return False - - def _is_enabled(self) -> bool: - """ - 检查 Git 同步是否启用 - - Returns: - 是否启用 - """ - return self.config.enabled - - def _is_git_repo(self) -> bool: - """ - 检查当前目录是否是 Git 仓库 - - Returns: - 是否是 Git 仓库 - """ - git_dir = self.vault_path / '.git' - return git_dir.exists() - - def _git_add(self) -> bool: - """ - 执行 git add - - Returns: - 是否成功 - """ - try: - result = subprocess.run( - ['git', 'add', '-A'], - cwd=self.vault_path, - capture_output=True, - text=True, - check=True - ) - return True - except subprocess.CalledProcessError as e: - print(f"git add 失败: {e.stderr}") - return False - except FileNotFoundError: - print("未找到 git 命令，请确保已安装 Git") - return False - - def _git_commit(self) -> bool: - """ - 执行 git commit - - Returns: - 是否成功 - """ - # 检查是否有更改需要提交 - if not self._has_changes(): - print("没有更改需要提交") - return True - - # 获取提交消息模板 - commit_message = self._get_commit_message() - - try: - result = subprocess.run( - ['git', 'commit', '-m', commit_message], - cwd=self.vault_path, - capture_output=True, - text=True, - check=True - ) - print(f"提交成功: {commit_message}") - return True - except subprocess.CalledProcessError as e: - print(f"git commit 失败: {e.stderr}") - return False - - def _has_changes(self) -> bool: - """ - 检查是否有更改需要提交 - - Returns: - 是否有更改 - """ - try: - # 检查工作区和暂存区是否有更改 - result = subprocess.run( - ['git', 'status', '--porcelain'], - cwd=self.vault_path, - capture_output=True, - text=True, - check=True - ) - # 如果有输出，说明有更改 - return bool(result.stdout.strip()) - except subprocess.CalledProcessError: - return False - - def _get_commit_message(self) -> str: - """ - 获取提交消息 - - 从 commit_message_template 模板生成 - - Returns: - 提交消息 - """ - template = self.config.commit_message_template - - # 简单的模板替换 - # 模板格式: "Add note: {title}" - # 目前只支持 {title} 占位符 - - # 尝试获取最新的文件信息作为标题 - # 这里我们使用一个简单的实现 - # 实际中可能需要从传入的数据中获取 - - import datetime - timestamp = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') - - # 简单的模板替换 - # 如果模板包含 {title}，替换为时间戳 - # 在实际使用中，应该传入具体的标题 - commit_message = template.replace('{title}', timestamp) - commit_message = commit_message.replace('{date}', timestamp) - - return commit_message - - def _should_auto_push(self) -> bool: - """ - 检查是否应该自动推送 - - Returns: - 是否应该自动推送 - """ - return self.config.auto_push - - def _git_push(self) -> bool: - """ - 执行 git push - - Returns: - 是否成功 - """ - try: - # 获取当前分支 - branch = self._get_current_branch() - - # 推送到远程 - result = subprocess.run( - ['git', 'push', 'origin', branch], - cwd=self.vault_path, - capture_output=True, - text=True, - check=True - ) - print(f"推送成功: origin/{branch}") - return True - except subprocess.CalledProcessError as e: - print(f"git push 失败: {e.stderr}") - return False - - def _get_current_branch(self) -> str: - """ - 获取当前 Git 分支 - - Returns: - 分支名称 - """ - try: - result = subprocess.run( - ['git', 'rev-parse', '--abbrev-ref', 'HEAD'], - cwd=self.vault_path, - capture_output=True, - text=True, - check=True - ) - return result.stdout.strip() - except subprocess.CalledProcessError: - return 'main' - - def sync_with_message(self, title: Optional[str] = None) -> bool: - """ - 使用自定义标题同步到 Git 仓库 - - Args: - title: 内容标题，用于生成提交消息 - - Returns: - 是否成功 - """ - # 保存原始模板 - original_template = self.config._config.get('git', {}).get('commit_message_template') - - # 临时修改模板 - if title: - self.config._config['git']['commit_message_template'] = original_template.replace('{title}', title) - - # 执行同步 - result = self.sync(self.config) - - # 恢复原始模板 - if original_template: - self.config._config['git']['commit_message_template'] = original_template - - return result diff --git a/skills/kb-workflow/src/outputs/obsidian_writer.py b/skills/kb-workflow/src/outputs/obsidian_writer.py deleted file mode 100644 index f88d9eb..0000000 --- a/skills/kb-workflow/src/outputs/obsidian_writer.py +++ /dev/null @@ -1,385 +0,0 @@ -""" -Obsidian Writer - 将内容写入 Obsidian vault -""" -import os -import re -from pathlib import Path -from datetime import datetime -from typing import Dict, Any, Optional -from ..models import Content - - -class ObsidianWriter: - """Obsidian vault 写入器""" - - def __init__(self, config): - """ - 初始化写入器 - - Args: - config: 配置对象 - """ - self.config = config - self.vault_path = config.vault_path - - def write(self, data: Dict[str, Any], config) -> Optional[str]: - """ - 将内容写入 Obsidian vault - - Args: - data: 内容数据字典或 Content 对象 - config: 配置对象 - - Returns: - 写入的文件路径，失败返回 None - """ - # 如果 data 是 Content 对象，转换为字典 - if isinstance(data, Content): - content = data - else: - content = Content.from_dict(data) - - # 构建文件路径 - file_path = self._build_file_path(content) - - # 创建目录结构 - file_path.parent.mkdir(parents=True, exist_ok=True) - - # 生成 Markdown 内容 - markdown_content = self._generate_markdown(content) - - # 写入文件 - try: - with open(file_path, 'w', encoding='utf-8') as f: - f.write(markdown_content) - - return str(file_path) - except Exception as e: - print(f"写入文件失败: {e}") - return None - - def _build_file_path(self, content: Content) -> Path: - """ - 构建文件路径 - - 格式: {vault}/videos/bilibili/{author}/{date}_{title}.md - - Args: - content: Content 对象 - - Returns: - 文件路径 - """ - # 获取日期（从 created_at 提取日期部分） - date_str = self._extract_date(content.created_at) - - # 清理标题（移除特殊字符） - safe_title = self._sanitize_filename(content.title) - - # 构建文件名 - filename = f"{date_str}_{safe_title}.md" - - # 构建完整路径 - # 格式: {vault}/videos/{source}/{author}/{filename} - path_parts = [ - self.vault_path, - 'videos', - content.source, - self._sanitize_filename(content.author), - filename - ] - - return Path(*path_parts) - - def _extract_date(self, iso_date: str) -> str: - """ - 从 ISO 日期字符串提取日期部分 - - Args: - iso_date: ISO 格式日期字符串 - - Returns: - YYYY-MM-DD 格式日期字符串 - """ - try: - dt = datetime.fromisoformat(iso_date) - return dt.strftime('%Y-%m-%d') - except: - # 如果解析失败，返回当前日期 - return datetime.now().strftime('%Y-%m-%d') - - def _sanitize_filename(self, filename: str) -> str: - """ - 清理文件名，移除不适合文件系统的字符 - - Args: - filename: 原始文件名 - - Returns: - 清理后的文件名 - """ - # 移除不适合文件系统的字符 - forbidden_chars = '<>:"/\\|?*' - for char in forbidden_chars: - filename = filename.replace(char, '') - - # 移除首尾空格 - filename = filename.strip() - - # 限制长度 - if len(filename) > 100: - filename = filename[:100] - - # 如果为空，使用默认值 - if not filename: - filename = 'untitled' - - return filename - - def _generate_markdown(self, content: Content) -> str: - """ - 生成 Obsidian Markdown 内容 - - 格式: - --- - title: xxx - source: bilibili - url: xxx - author: xxx - duration: xxx - date: xxx - tags: [xxx, xxx] - --- - - # {title} - - ...内容... - - Args: - content: Content 对象 - - Returns: - Markdown 内容字符串 - """ - # 生成 frontmatter - frontmatter = self._generate_frontmatter(content) - - # 生成正文 - body = self._generate_body(content) - - # 组合 - markdown = f"{frontmatter}\n{body}" - - return markdown - - def _generate_frontmatter(self, content: Content) -> str: - """ - 生成 YAML frontmatter - - Args: - content: Content 对象 - - Returns: - Frontmatter 字符串 - """ - # 格式化时长 - duration_str = self._format_duration(content.duration) if content.duration else "N/A" - - # 格式化日期 - date_str = self._extract_date(content.created_at) - - # 构建标签列表 - tags = self._build_tags(content) - tags_yaml = ", ".join([f'"{tag}"' for tag in tags]) - - frontmatter = f"""--- -title: {content.title} -source: {content.source} -url: {content.url} -author: {content.author} -duration: {duration_str} -date: {date_str} -tags: [{tags_yaml}] ----""" - - return frontmatter - - def _build_tags(self, content: Content) -> list: - """ - 构建标签列表 - - Args: - content: Content 对象 - - Returns: - 标签列表 - """ - tags = [] - - # 添加自动标签 - if self.config.auto_tags: - tags.extend(self.config.default_tags) - - # 添加来源标签 - if content.source: - tags.append(content.source) - - # 添加内容类型标签 - if content.content_type: - tags.append(content.content_type.value) - - # 添加 AI 生成的标签 - if content.tags: - tags.extend(content.tags) - - # 去重 - tags = list(dict.fromkeys(tags)) - - return tags - - def _format_duration(self, duration_seconds: int) -> str: - """ - 格式化时长为 HH:MM:SS - - Args: - duration_seconds: 时长（秒） - - Returns: - 格式化的时长字符串 - """ - if duration_seconds is None: - return "N/A" - - hours = duration_seconds // 3600 - minutes = (duration_seconds % 3600) // 60 - seconds = duration_seconds % 60 - - if hours > 0: - return f"{hours}:{minutes:02d}:{seconds:02d}" - else: - return f"{minutes}:{seconds:02d}" - - def _generate_body(self, content: Content) -> str: - """ - 生成 Markdown 正文内容 - - Args: - content: Content 对象 - - Returns: - 正文内容字符串 - """ - body_parts = [] - - # 标题 - body_parts.append(f"# {content.title}") - body_parts.append("") - - # 一句话摘要 - if content.one_liner: - body_parts.append(f"> {content.one_liner}") - body_parts.append("") - - # 摘要 - if content.summary: - body_parts.append("## 摘要") - body_parts.append("") - body_parts.append(content.summary) - body_parts.append("") - - # 关键要点 - if content.key_points: - body_parts.append("## 关键要点") - body_parts.append("") - for point in content.key_points: - body_parts.append(f"- {point}") - body_parts.append("") - - # 大纲 - if content.outline: - body_parts.append("## 大纲") - body_parts.append("") - for i, item in enumerate(content.outline, 1): - body_parts.append(f"{i}. {item}") - body_parts.append("") - - # 时间线 - if content.timeline: - body_parts.append("## 时间线") - body_parts.append("") - for entry in content.timeline: - timestamp = entry.timestamp or "N/A" - title = entry.title or "" - description = entry.description or "" - body_parts.append(f"### {timestamp} - {title}") - if description: - body_parts.append(description) - body_parts.append("") - - # 内容分析 - if content.analysis and content.analysis.outline: - body_parts.append("## 详细分析") - body_parts.append("") - - analysis = content.analysis - - # 前置知识 - if analysis.prerequisites: - body_parts.append("### 前置知识") - body_parts.append("") - for prereq in analysis.prerequisites: - body_parts.append(f"- {prereq}") - body_parts.append("") - - # 详细大纲 - if analysis.outline: - body_parts.append("### 详细大纲") - body_parts.append("") - for section in analysis.outline: - if isinstance(section, dict): - title = section.get('title', '') - content_text = section.get('content', '') - body_parts.append(f"#### {title}") - if content_text: - body_parts.append(content_text) - else: - body_parts.append(str(section)) - body_parts.append("") - - # 常见坑点 - if analysis.pitfalls: - body_parts.append("### 常见坑点") - body_parts.append("") - for pitfall in analysis.pitfalls: - body_parts.append(f"- {pitfall}") - body_parts.append("") - - # 实践建议 - if analysis.practice_advice: - body_parts.append("### 实践建议") - body_parts.append("") - for advice in analysis.practice_advice: - body_parts.append(f"- {advice}") - body_parts.append("") - - # 原始内容（如果有） - if content.transcript: - body_parts.append("## 原始内容") - body_parts.append("") - body_parts.append(content.transcript) - body_parts.append("") - - # 无字幕提示 - if not content.transcript: - body_parts.append("*此视频无字幕，仅保存元数据*") - body_parts.append("") - - # 元数据 - body_parts.append("---") - body_parts.append(f"- **来源**: [{content.author} - {content.title}]({content.url})") - if content.duration: - duration_str = self._format_duration(content.duration) - body_parts.append(f"- **时长**: {duration_str}") - body_parts.append(f"- **处理日期**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") - - return "\n".join(body_parts) diff --git a/skills/kb-workflow/src/pipeline.py b/skills/kb-workflow/src/pipeline.py deleted file mode 100644 index 78657f9..0000000 --- a/skills/kb-workflow/src/pipeline.py +++ /dev/null @@ -1,273 +0,0 @@ -"""Obsidian KB - 内容处理管道 -C4.3: 实现完整处理流程 -""" -import os -from datetime import datetime -from typing import Optional -from src.models import Content, ContentStatus, AnalysisResult -from src.light_scanner import LightScanner -from src.deep_analyzer import DeepAnalyzer -from src.config import get_config - - -class ProcessingPipeline: - """内容处理管道 - 协调轻量扫描、深度分析、笔记生成和同步""" - - def __init__(self, ai_client=None, git_client=None): - self.config = get_config() - self.ai_client = ai_client - self.git_client = git_client - - # 初始化处理器 - self.light_scanner = LightScanner(ai_client=ai_client) - self.deep_analyzer = DeepAnalyzer(ai_client=ai_client) - - def light_scan(self, content: Content, content_text: str) -> Content: - """ - 轻量扫描阶段 - - Args: - content: Content对象 - content_text: 内容文本 - - Returns: - 更新后的Content对象 - """ - print(f"[Pipeline] 开始轻量扫描: {content.id}") - - try: - content = self.light_scanner.scan(content, content_text) - print(f"[Pipeline] 轻量扫描完成: 摘要长度={len(content.summary)}, 关键词={content.key_points}, 质量分={content.quality_metrics.overall_score:.2f}") - return content - except Exception as e: - print(f"[Pipeline] 轻量扫描失败: {e}") - raise - - def deep_analyze(self, content: Content, content_text: str) -> Content: - """ - 深度分析阶段 - - Args: - content: Content对象 - content_text: 内容文本 - - Returns: - 更新后的Content对象 - """ - print(f"[Pipeline] 开始深度分析: {content.id}") - - try: - analysis_result = self.deep_analyzer.analyze(content, content_text) - print(f"[Pipeline] 深度分析完成: 主题数={len(analysis_result.main_topics)}, 时间轴段数={len(analysis_result.timeline)}") - return content - except Exception as e: - print(f"[Pipeline] 深度分析失败: {e}") - content.update_status(ContentStatus.ERROR) - raise - - def process_full(self, content: Content, content_text: str) -> Content: - """ - 完整处理流程: - 轻量扫描 → 入库 → 深度分析 → 生成笔记 → Git同步 - - Args: - content: Content对象 - content_text: 内容文本 - - Returns: - 完全处理后的Content对象 - """ - print(f"\n{'='*60}") - print(f"[Pipeline] 开始完整处理流程: {content.id}") - print(f"{'='*60}\n") - - try: - # Step 1: 轻量扫描 - print("\n📋 Step 1: 轻量扫描") - content = self.light_scan(content, content_text) - - # Step 2: 入库（保存到数据库/文件系统） - print("\n💾 Step 2: 保存内容") - self._save_content(content) - - # Step 3: 深度分析 - print("\n🔍 Step 3: 深度分析") - content = self.deep_analyze(content, content_text) - - # Step 4: 生成笔记 - print("\n📝 Step 4: 生成Obsidian笔记") - content = self._generate_note(content) - - # Step 5: Git同步 - if self.config.git.enabled: - print("\n🔄 Step 5: Git同步") - self._sync_to_git(content) - else: - print("\n⏭️ Step 5: Git同步已禁用，跳过") - - content.update_status(ContentStatus.GENERATED) - - print(f"\n{'='*60}") - print(f"[Pipeline] 处理完成: {content.id}") - print(f"{'='*60}\n") - - return content - - except Exception as e: - print(f"\n❌ [Pipeline] 处理流程失败: {e}") - raise - - def _save_content(self, content: Content) -> None: - """保存内容到存储""" - # 创建存储目录 - storage_dir = os.path.expanduser("~/.obsidian-kb/storage") - os.makedirs(storage_dir, exist_ok=True) - - # 保存JSON文件 - import json - file_path = os.path.join(storage_dir, f"{content.id}.json") - - with open(file_path, 'w', encoding='utf-8') as f: - json.dump(content.to_dict(), f, ensure_ascii=False, indent=2) - - print(f"[Pipeline] 内容已保存: {file_path}") - - def _generate_note(self, content: Content) -> Content: - """生成Obsidian笔记""" - # 构建笔记内容 - note_content = self._build_note_content(content) - - # 确定笔记路径 - vault_path = os.path.expanduser(self.config.obsidian.vault_path) - notes_folder = self.config.obsidian.notes_folder - - note_dir = os.path.join(vault_path, notes_folder) - os.makedirs(note_dir, exist_ok=True) - - # 生成文件名 - safe_title = self._sanitize_filename(content.title or content.id) - timestamp = datetime.now().strftime("%Y%m%d") - note_filename = f"{timestamp}_{safe_title}.md" - note_path = os.path.join(note_dir, note_filename) - - # 写入文件 - with open(note_path, 'w', encoding='utf-8') as f: - f.write(note_content) - - content.output_path = note_path - content.update_status(ContentStatus.GENERATED) - - print(f"[Pipeline] 笔记已生成: {note_path}") - return content - - def _build_note_content(self, content: Content) -> str: - """构建Obsidian笔记内容""" - lines = [ - f"# {content.title or '未命名内容'}", - "", - "## 📊 元数据", - f"- **来源**: {content.source}", - f"- **URL**: {content.url}", - f"- **作者**: {content.author or '未知'}", - f"- **类型**: {content.content_type.value}", - ] - - if content.duration: - duration_str = self._format_duration(content.duration) - lines.append(f"- **时长**: {duration_str}") - - lines.extend([ - f"- **质量评分**: {content.quality_metrics.overall_score:.1f}/1.0", - f"- **处理时间**: {datetime.now().strftime('%Y-%m-%d %H:%M')}", - "", - "## 📝 摘要", - content.summary or "暂无摘要", - "", - "## 🏷️ 关键词", - ]) - - # 添加关键词标签 - if content.key_points: - keyword_tags = ' '.join([f"#{kw.replace(' ', '_')}" for kw in content.key_points[:5]]) - lines.append(keyword_tags) - lines.append('') - - # 添加时间轴 - if content.timeline: - lines.extend([ - "", - "## ⏱️ 时间轴", - "", - ]) - - for item in content.timeline: - lines.append(f"### {item.timestamp} - {item.title}") - lines.append(item.description) - lines.append('') - - # 添加原始内容（折叠） - lines.extend([ - "", - "## 📄 原始内容", - "", - "```", - content.transcript[:2000] + "..." if len(content.transcript) > 2000 else content.transcript, - "```", - "", - "---", - f"*由 Obsidian KB 自动生成 - {datetime.now().strftime('%Y-%m-%d %H:%M')}*" - ]) - - return '\n'.join(lines) - - def _sync_to_git(self, content: Content) -> None: - """同步到Git仓库""" - if not self.git_client: - print("[Pipeline] Git客户端未配置，跳过同步") - return - - try: - vault_path = os.path.expanduser(self.config.obsidian.vault_path) - - # 添加文件 - relative_path = os.path.relpath(content.output_path, vault_path) - self.git_client.add(relative_path) - - # 提交 - commit_msg = self.config.git.commit_message_template.format( - title=content.title or content.id - ) - self.git_client.commit(commit_msg) - - # 推送（如果启用） - if self.config.git.auto_push: - self.git_client.push() - - print(f"[Pipeline] Git同步完成") - - except Exception as e: - print(f"[Pipeline] Git同步失败: {e}") - # Git同步失败不阻断主流程 - - def _sanitize_filename(self, filename: str) -> str: - """清理文件名""" - import re - # 替换非法字符 - filename = re.sub(r'[<>:"/\\|?*]', '_', filename) - # 限制长度 - if len(filename) > 50: - filename = filename[:50] - return filename.strip() - - def _format_duration(self, seconds: int) -> str: - """格式化时长""" - hours = seconds // 3600 - minutes = (seconds % 3600) // 60 - secs = seconds % 60 - - if hours > 0: - return f"{hours}小时{minutes}分钟" - elif minutes > 0: - return f"{minutes}分钟{secs}秒" - else: - return f"{secs}秒" diff --git a/skills/kb-workflow/src/sources/__init__.py b/skills/kb-workflow/src/sources/__init__.py deleted file mode 100644 index 04dc5ca..0000000 --- a/skills/kb-workflow/src/sources/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -""" -Sources 模块初始化 -""" -from .base import SourceAdapter -from .bilibili import BilibiliAdapter - - -class SourceRegistry: - """源适配器注册表""" - _adapters = {} - - @classmethod - def register(cls, name: str, adapter_class: type): - """注册适配器类""" - cls._adapters[name] = adapter_class - - @classmethod - def get(cls, name: str) -> SourceAdapter: - """获取适配器类""" - return cls._adapters.get(name) - - @classmethod - def list(cls) -> list: - """列出所有已注册的适配器名称""" - return list(cls._adapters.keys()) - - -# 自动注册适配器 -SourceRegistry.register('bilibili', BilibiliAdapter) - -__all__ = ['SourceAdapter', 'BilibiliAdapter', 'SourceRegistry'] diff --git a/skills/kb-workflow/src/sources/base.py b/skills/kb-workflow/src/sources/base.py deleted file mode 100644 index ce91fc4..0000000 --- a/skills/kb-workflow/src/sources/base.py +++ /dev/null @@ -1,58 +0,0 @@ -""" -Source Adapter 抽象基类 -""" -from abc import ABC, abstractmethod -from datetime import datetime -from typing import List, Optional - -from models import Content - - -class SourceAdapter(ABC): - """内容源适配器基类""" - - @abstractmethod - def fetch_new(self, since: datetime, **kwargs) -> List[Content]: - """ - 获取新内容 - - Args: - since: 起始时间，只获取该时间之后的内容 - **kwargs: 额外的参数 - - Returns: - List[Content]: 新内容列表 - """ - pass - - @abstractmethod - def extract_content(self, url: str, **kwargs) -> Optional[Content]: - """ - 从URL提取内容 - - Args: - url: 内容URL - **kwargs: 额外的参数 - - Returns: - Optional[Content]: 内容对象，如果提取失败返回None - """ - pass - - @abstractmethod - def get_transcript(self, content: Content, **kwargs) -> Optional[str]: - """ - 获取字幕/文本 - - Args: - content: 内容对象 - **kwargs: 额外的参数 - - Returns: - Optional[str]: 字幕文本（SRT格式），如果没有字幕返回None - """ - pass - - -# 为向后兼容，提供 BaseSource 别名 -BaseSource = SourceAdapter diff --git a/skills/kb-workflow/src/sources/bilibili.py b/skills/kb-workflow/src/sources/bilibili.py deleted file mode 100644 index 29dcf3c..0000000 --- a/skills/kb-workflow/src/sources/bilibili.py +++ /dev/null @@ -1,466 +0,0 @@ -""" -Bilibili 视频源适配器 -""" -import re -import json -import time -import hashlib -import urllib.parse -from datetime import datetime -from typing import List, Optional, Dict, Any -import urllib.request -from urllib.request import Request, urlopen - -from .base import SourceAdapter -from models import Content, ContentType, ContentStatus - - -class BilibiliAdapter(SourceAdapter): - """B站视频源适配器""" - - BASE_URL = "https://api.bilibili.com" - - # WBI 混淆密钥表 - MIXIN_KEY_ENC_TABLE = [ - 46, 47, 18, 2, 53, 8, 23, 32, 15, 50, 10, 31, 58, 3, 45, 35, - 27, 43, 5, 49, 33, 9, 42, 19, 29, 28, 14, 39, 12, 38, 41, 13, - 37, 48, 7, 16, 24, 55, 40, 61, 26, 17, 0, 1, 60, 51, 30, 4, - 22, 25, 54, 21, 56, 59, 6, 63, 57, 62, 11, 36, 20, 34, 44, 52 - ] - - def __init__(self, sessdata: Optional[str] = None): - """ - 初始化适配器 - - Args: - sessdata: B站登录凭证（可选，用于获取私人收藏夹） - """ - self.sessdata = sessdata - self._img_key = None - self._sub_key = None - - def _get_headers(self) -> Dict[str, str]: - """获取请求头""" - headers = { - 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36', - 'Referer': 'https://www.bilibili.com', - 'Accept': 'application/json, text/plain, */*', - } - if self.sessdata: - headers['Cookie'] = f'SESSDATA={self.sessdata}' - return headers - - def _get_wbi_keys(self) -> tuple: - """获取WBI密钥""" - if self._img_key and self._sub_key: - return self._img_key, self._sub_key - - url = f"{self.BASE_URL}/x/web-interface/nav" - try: - req = Request(url, headers=self._get_headers()) - with urlopen(req, timeout=30) as response: - data = json.loads(response.read().decode('utf-8')) - if data.get('code') == 0: - img_url = data['data']['wbi_img']['img_url'] - sub_url = data['data']['wbi_img']['sub_url'] - - # 提取密钥 - img_key = img_url.split('/')[-1].split('.')[0] - sub_key = sub_url.split('/')[-1].split('.')[0] - - self._img_key = img_key - self._sub_key = sub_key - return img_key, sub_key - except Exception as e: - print(f"获取WBI密钥失败: {e}") - - return None, None - - def _get_mixin_key(self, img_key: str, sub_key: str) -> str: - """生成Mixin密钥""" - raw_key = img_key + sub_key - mixin_key = ''.join([raw_key[i] for i in self.MIXIN_KEY_ENC_TABLE])[:32] - return mixin_key - - def _encode_wbi(self, params: Dict[str, Any]) -> Dict[str, Any]: - """WBI签名参数""" - img_key, sub_key = self._get_wbi_keys() - if not img_key or not sub_key: - return params - - # 添加时间戳 - params['wts'] = int(time.time()) - - # 按字典序排序参数 - sorted_params = dict(sorted(params.items())) - - # 构建查询字符串 - query = urllib.parse.urlencode(sorted_params) - - # 生成Mixin密钥并计算签名 - mixin_key = self._get_mixin_key(img_key, sub_key) - sign = hashlib.md5((query + mixin_key).encode()).hexdigest() - - sorted_params['w_rid'] = sign - return sorted_params - - def _fetch_json(self, url: str, params: Optional[Dict] = None, use_wbi: bool = False) -> Optional[Dict]: - """ - 发送HTTP GET请求并返回JSON数据 - - Args: - url: 请求URL - params: 查询参数 - use_wbi: 是否使用WBI签名 - - Returns: - Optional[Dict]: JSON数据或None - """ - try: - if params: - if use_wbi: - params = self._encode_wbi(params) - query = urllib.parse.urlencode(params) - url = f"{url}?{query}" - - req = Request(url, headers=self._get_headers()) - with urlopen(req, timeout=30) as response: - return json.loads(response.read().decode('utf-8')) - except Exception as e: - print(f"请求失败 {url}: {e}") - return None - - def _extract_bvid_from_url(self, url: str) -> Optional[str]: - """ - 从URL提取BV号 - - Args: - url: B站视频URL - - Returns: - Optional[str]: BV号或None - """ - patterns = [ - r'/video/(BV[\w]+)', - r'bvid=([\w]+)', - r'(BV[\w]+)', - ] - - for pattern in patterns: - match = re.search(pattern, url, re.IGNORECASE) - if match: - bvid = match.group(1) - if bvid.upper().startswith('BV'): - return bvid - return None - - def _timestamp_to_datetime(self, timestamp: int) -> Optional[datetime]: - """时间戳转datetime""" - try: - if timestamp > 10000000000: # 毫秒时间戳 - timestamp = timestamp // 1000 - return datetime.fromtimestamp(timestamp) - except: - return None - - def fetch_new(self, since: datetime, **kwargs) -> List[Content]: - """ - 获取新内容 - - Args: - since: 起始时间 - **kwargs: 包含 media_id(收藏夹ID) 或 mid(UP主ID) - - Returns: - List[Content]: 新内容列表 - """ - contents = [] - - # 获取收藏夹视频 - media_id = kwargs.get('media_id') - if media_id: - fav_contents = self._fetch_favorites(media_id, since) - contents.extend(fav_contents) - - # 获取UP主视频 - mid = kwargs.get('mid') - if mid: - up_contents = self._fetch_up_videos(mid, since) - contents.extend(up_contents) - - return contents - - def _fetch_favorites(self, media_id: int, since: datetime) -> List[Content]: - """ - 获取收藏夹视频列表 - - Args: - media_id: 收藏夹ID - since: 起始时间 - - Returns: - List[Content]: 视频列表 - """ - contents = [] - page = 1 - - while True: - url = f"{self.BASE_URL}/x/v3/fav/resource/list" - params = { - 'media_id': media_id, - 'pn': page, - 'ps': 20, - 'platform': 'web', - } - - data = self._fetch_json(url, params) - if not data or data.get('code') != 0: - break - - medias = data.get('data', {}).get('medias', []) or [] - if not medias: - break - - for media in medias: - content = Content( - source="bilibili", - source_id=media.get('bvid', ''), - url=f"https://www.bilibili.com/video/{media.get('bvid', '')}", - title=media.get('title', ''), - content_type=ContentType.VIDEO, - author=media.get('upper', {}).get('name', ''), - duration=media.get('duration', 0), - ) - contents.append(content) - - # 检查是否还有更多 - info = data.get('data', {}).get('info', {}) - total = info.get('media_count', 0) - if page * 20 >= total: - break - - page += 1 - - return contents - - def _fetch_up_videos(self, mid: int, since: datetime) -> List[Content]: - """ - 获取UP主视频列表 - - Args: - mid: UP主ID - since: 起始时间 - - Returns: - List[Content]: 视频列表 - """ - contents = [] - page = 1 - - while True: - url = f"{self.BASE_URL}/x/space/wbi/arc/search" - params = { - 'mid': mid, - 'pn': page, - 'ps': 30, - } - - data = self._fetch_json(url, params, use_wbi=True) - if not data or data.get('code') != 0: - break - - vlist = data.get('data', {}).get('list', {}).get('vlist', []) - if not vlist: - break - - for video in vlist: - # 检查时间 - created = video.get('created', 0) - pub_datetime = self._timestamp_to_datetime(created) - - if pub_datetime and pub_datetime < since: - # 已经处理到旧视频，停止 - return contents - - content = Content( - source="bilibili", - source_id=video.get('bvid', ''), - url=f"https://www.bilibili.com/video/{video.get('bvid', '')}", - title=video.get('title', ''), - content_type=ContentType.VIDEO, - author=video.get('author', ''), - duration=self._parse_duration(video.get('length', '0:00')), - ) - contents.append(content) - - page += 1 - # 限制最多获取10页 - if page > 10: - break - - return contents - - def _parse_duration(self, duration_str: str) -> int: - """解析时长字符串为秒数""" - try: - parts = duration_str.split(':') - if len(parts) == 2: # MM:SS - return int(parts[0]) * 60 + int(parts[1]) - elif len(parts) == 3: # HH:MM:SS - return int(parts[0]) * 3600 + int(parts[1]) * 60 + int(parts[2]) - except: - pass - return 0 - - def extract_content(self, url: str, **kwargs) -> Optional[Content]: - """ - 从URL提取内容 - - Args: - url: B站视频URL - **kwargs: 额外参数 - - Returns: - Optional[Content]: 内容对象 - """ - bvid = self._extract_bvid_from_url(url) - if not bvid: - print(f"无法从URL提取BV号: {url}") - return None - - api_url = f"{self.BASE_URL}/x/web-interface/view" - params = {'bvid': bvid} - - data = self._fetch_json(api_url, params) - if not data or data.get('code') != 0: - print(f"获取视频详情失败: {data}") - return None - - video_data = data.get('data', {}) - - owner = video_data.get('owner', {}) - - content = Content( - source="bilibili", - source_id=bvid, - url=url, - title=video_data.get('title', ''), - content_type=ContentType.VIDEO, - author=owner.get('name', ''), - description=video_data.get('desc', ''), - duration=video_data.get('duration', 0), - status=ContentStatus.SCANNED, - ) - - return content - - def get_transcript(self, content: Content, **kwargs) -> Optional[str]: - """ - 获取字幕 - - Args: - content: 内容对象 - **kwargs: 额外参数 - - Returns: - Optional[str]: 字幕文本（SRT格式） - """ - bv_id = content.source_id - if not bv_id: - bv_id = content.id - - # 先获取cid - api_url = f"{self.BASE_URL}/x/web-interface/view" - data = self._fetch_json(api_url, {'bvid': bv_id}) - if not data or data.get('code') != 0: - return None - - cid = data['data'].get('cid') - if not cid: - return None - - # 获取字幕列表 - subtitle_url = f"{self.BASE_URL}/x/player/wbi/v2" - params = { - 'cid': cid, - 'bvid': bv_id, - } - - data = self._fetch_json(subtitle_url, params, use_wbi=True) - if not data or data.get('code') != 0: - return None - - subtitle_info = data.get('data', {}).get('subtitle', {}) - subtitles = subtitle_info.get('subtitles', []) - - if not subtitles: - return None - - # 优先选择AI字幕，其次选择中文简体，最后选择第一个 - selected_sub = None - for sub in subtitles: - lan_doc = sub.get('lan_doc', '') - lan = sub.get('lan', '') - if '自动生成' in lan_doc or 'AI' in lan_doc: - selected_sub = sub - break - elif lan == 'zh-CN' or lan == 'zh': - selected_sub = sub - break - - if not selected_sub: - selected_sub = subtitles[0] - - # 下载字幕内容 - sub_url = selected_sub.get('subtitle_url', '') - if not sub_url: - return None - - # 确保URL完整 - if sub_url.startswith('//'): - sub_url = 'https:' + sub_url - elif sub_url.startswith('/'): - sub_url = 'https://www.bilibili.com' + sub_url - - try: - req = Request(sub_url, headers=self._get_headers()) - with urlopen(req, timeout=30) as response: - subtitle_data = json.loads(response.read().decode('utf-8')) - except Exception as e: - print(f"下载字幕失败: {e}") - return None - - # 解析字幕 - body = subtitle_data.get('body', []) - if not body: - return None - - # 转换为SRT格式 - srt_lines = [] - - for i, item in enumerate(body, 1): - from_time = item.get('from', 0) - to_time = item.get('to', 0) - text = item.get('content', '').strip() - - if not text: - continue - - # SRT格式时间码 - from_str = self._format_srt_time(from_time) - to_str = self._format_srt_time(to_time) - - srt_lines.append(f"{i}") - srt_lines.append(f"{from_str} --> {to_str}") - srt_lines.append(text) - srt_lines.append("") - - return '\n'.join(srt_lines) - - def _format_srt_time(self, seconds: float) -> str: - """格式化为SRT时间码""" - hours = int(seconds // 3600) - minutes = int((seconds % 3600) // 60) - secs = int(seconds % 60) - millis = int((seconds % 1) * 1000) - return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}" diff --git a/skills/kb-workflow/src/sources/local.py b/skills/kb-workflow/src/sources/local.py deleted file mode 100644 index a522812..0000000 --- a/skills/kb-workflow/src/sources/local.py +++ /dev/null @@ -1,57 +0,0 @@ -"""Obsidian KB - 本地文件源""" - -import os -from pathlib import Path -from typing import Optional -from .base import BaseSource -from ..models import Content, ContentType - - -class LocalSource(BaseSource): - """本地文件源""" - - name = "local" - - SUPPORTED_EXTENSIONS = ['.txt', '.md', '.pdf', '.docx'] - - def can_handle(self, url: str) -> bool: - """检查是否为本地文件路径""" - if url.startswith('http'): - return False - path = Path(url) - return path.exists() and path.suffix.lower() in self.SUPPORTED_EXTENSIONS - - def extract_id(self, url: str) -> Optional[str]: - """使用文件路径作为ID""" - return os.path.basename(url) - - def fetch(self, url: str) -> Optional[Content]: - """获取文件元数据""" - path = Path(url) - if not path.exists(): - return None - - content_type = ContentType.ARTICLE - if path.suffix.lower() == '.txt': - content_type = ContentType.ARTICLE - - return Content( - source=self.name, - source_id=str(path), - url=str(path.absolute()), - title=path.stem, - author='', - content_type=content_type - ) - - def get_transcript(self, content: Content) -> Optional[str]: - """读取文件内容""" - try: - path = Path(content.url) - if path.suffix.lower() in ['.txt', '.md']: - return path.read_text(encoding='utf-8') - # TODO: 支持 PDF 和 DOCX - return None - except Exception as e: - print(f"Error reading local file: {e}") - return None diff --git a/skills/kb-workflow/src/sources/markdown_converter.py b/skills/kb-workflow/src/sources/markdown_converter.py deleted file mode 100644 index c0f912d..0000000 --- a/skills/kb-workflow/src/sources/markdown_converter.py +++ /dev/null @@ -1,17 +0,0 @@ -from markdownify import MarkdownConverter as _MarkdownConverter - -class MarkdownConverter: - """HTML 转 Markdown 转换器，保留完整格式""" - - def __init__(self): - self.converter = _MarkdownConverter( - heading_style="ATX", - bullets="*,-", - code_blocks=True, - tables=True, - strip=["script", "style", "nav", "footer", "aside"] - ) - - def convert(self, html: str) -> str: - """转换 HTML 为 Markdown""" - return self.converter.convert(html) diff --git a/skills/kb-workflow/src/sources/youtube.py b/skills/kb-workflow/src/sources/youtube.py deleted file mode 100644 index efa1137..0000000 --- a/skills/kb-workflow/src/sources/youtube.py +++ /dev/null @@ -1,53 +0,0 @@ -"""Obsidian KB - YouTube源""" - -import re -from typing import Optional -from .base import BaseSource -from ..models import Content, ContentType - - -class YouTubeSource(BaseSource): - """YouTube视频源""" - - name = "youtube" - - # 支持多种YouTube URL格式 - URL_PATTERNS = [ - r'youtube\.com/watch\?v=([a-zA-Z0-9_-]{11})', - r'youtu\.be/([a-zA-Z0-9_-]{11})', - r'youtube\.com/embed/([a-zA-Z0-9_-]{11})', - ] - - def can_handle(self, url: str) -> bool: - """检查是否为YouTube链接""" - return 'youtube.com' in url or 'youtu.be' in url - - def extract_id(self, url: str) -> Optional[str]: - """提取视频ID""" - for pattern in self.URL_PATTERNS: - match = re.search(pattern, url) - if match: - return match.group(1) - return None - - def fetch(self, url: str) -> Optional[Content]: - """获取视频元数据""" - video_id = self.extract_id(url) - if not video_id: - return None - - # TODO: 使用 YouTube Data API 获取完整信息 - # 目前返回基本信息 - return Content( - source=self.name, - source_id=video_id, - url=f'https://www.youtube.com/watch?v={video_id}', - title='', # 需要通过API获取 - author='', - content_type=ContentType.VIDEO - ) - - def get_transcript(self, content: Content) -> Optional[str]: - """获取字幕""" - # TODO: 使用 youtube-transcript-api - return None diff --git a/skills/kb-workflow/src/sources/zhihu.py b/skills/kb-workflow/src/sources/zhihu.py deleted file mode 100644 index f8a59f4..0000000 --- a/skills/kb-workflow/src/sources/zhihu.py +++ /dev/null @@ -1,91 +0,0 @@ -"""Obsidian KB - 知乎源""" - -import re -from typing import Optional -from .base import BaseSource -from ..models import Content, ContentType - -try: - from .zhihu_drission_extractor import ZhihuDrissionExtractor -except ImportError: - # DrissionPage 未安装 - import logging - logger = logging.getLogger(__name__) - logger.warning(f"DrissionPage 未安装，无法提取知乎内容") - - -class ZhihuSource(BaseSource): - """知乎文章/回答源""" - - name = "zhihu" - - URL_PATTERNS = [ - r'zhihu\.com/question/\d+/answer/(\d+)', - r'zhuanlan\.zhihu\.com/p/(\d+)', - r'zhihu\.com/question/(\d+)', - ] - - def can_handle(self, url: str) -> bool: - """检查是否为知乎链接""" - return 'zhihu.com' in url or 'zhuanlan.zhihu.com' in url - - def extract_id(self, url: str) -> Optional[str]: - """提取内容ID""" - for pattern in self.URL_PATTERNS: - match = re.search(pattern, url) - if match: - return match.group(1) - return None - - def fetch(self, url: str) -> Optional[Content]: - """获取内容元数据""" - content_id = self.extract_id(url) - if not content_id: - return None - - # TODO: 使用知乎API或爬虫获取完整信息 - content_type = ContentType.ARTICLE - if 'zhuanlan' in url: - content_type = ContentType.ARTICLE - - return Content( - source=self.name, - source_id=content_id, - url=url, - title='', - author='', - content_type=content_type - ) - - def get_transcript(self, content: Content) -> Optional[str]: - """获取文章内容""" - try: - # 使用 DrissionPage 提取器 - extractor = ZhihuDrissionExtractor( - headless=self.config.get('headless', True), - timeout=self.config.get('timeout', 30), - config=self.config - ) - - data = extractor.extract(content.url) - - if data and data.get('content_markdown'): - # 更新元数据 - if data.get('title'): - content.title = data['title'] - if data.get('author'): - content.author = data['author'] - - # 返回 Markdown 内容 - return data['content_markdown'] - - except ImportError as e: - import logging - logger = logging.getLogger(__name__) - logger.error(f"DrissionPage 导入失败: {e}") - except Exception as e: - import logging - logger = logging.getLogger(__name__) - logger.error(f"DrissionPage 提取失败: {e}") - - return None diff --git a/skills/kb-workflow/src/sources/zhihu_drission_extractor.py b/skills/kb-workflow/src/sources/zhihu_drission_extractor.py deleted file mode 100644 index 419a742..0000000 --- a/skills/kb-workflow/src/sources/zhihu_drission_extractor.py +++ /dev/null @@ -1,514 +0,0 @@ -""" -知乎 DrissionPage 内容提取器 -使用 DrissionPage 浏览器自动化工具抓取知乎内容 -""" - -import re -import json -import sys -import os -from typing import Dict, Optional, Any, List -from pathlib import Path -from urllib.parse import urlparse - -# 添加当前目录到 Python 路径以支持绝对导入 -current_dir = os.path.dirname(os.path.abspath(__file__)) -if current_dir not in sys.path: - sys.path.insert(0, current_dir) - -try: - from .markdown_converter import MarkdownConverter -except ImportError: - from markdown_converter import MarkdownConverter - - -class DrissionBrowser: - """DrissionPage 浏览器管理器""" - - def __init__(self, headless: bool = True, timeout: int = 30): - self.headless = headless - self.timeout = timeout - self.page = None - - def start(self): - """启动浏览器""" - from DrissionPage import ChromiumOptions, ChromiumPage - - options = ChromiumOptions() - - # 设置浏览器路径（服务器环境需要） - options.set_browser_path('/usr/bin/chromium-browser') - - if self.headless: - options.headless() - options.set_user_agent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36') - - self.page = ChromiumPage(options) - self.page.set.timeouts(base=self.timeout) - - def close(self): - """关闭浏览器""" - if self.page: - self.page.quit() - self.page = None - - def goto(self, url: str): - """导航到指定 URL""" - self.page.get(url) - - def wait_for_selector(self, selector: str, timeout: int = None): - """等待选择器出现""" - timeout = timeout or self.timeout - self.page.wait.ele_displayed(selector, timeout=timeout) - - def query_selector(self, selector: str): - """查询选择器""" - return self.page.ele(selector) - - def query_selector_all(self, selector: str): - """查询所有匹配的选择器""" - return self.page.eles(selector) - - def evaluate(self, script: str): - """执行 JavaScript 代码""" - return self.page.run_js(script) - - def get_page_title(self): - """获取页面标题""" - if self.page: - return self.page.title - return '' - - def get_page_content(self): - """获取页面 HTML 内容""" - if self.page: - return self.page.html - return '' - - def screenshot(self, path: str): - """截图""" - if self.page: - self.page.get_screenshot(path=path) - - -class ZhihuDrissionExtractor: - """知乎 DrissionPage 提取器""" - - URL_PATTERNS = { - 'answer': r'zhihu\.com/question/\d+/answer/(\d+)', - 'article': r'zhuanlan\.zhihu\.com/p/(\d+)', - 'question': r'zhihu\.com/question/(\d+)(?:$|/)', - } - - def __init__(self, headless: bool = True, timeout: int = 30, config: Optional[Dict] = None): - """ - 初始化提取器 - - Args: - headless: 是否使用无头浏览器（默认 True） - timeout: 超时时间（秒，默认 30） - config: 配置字典，用于加载 Cookie - """ - self.headless = headless - self.timeout = timeout - self.config = config or {} - # 支持从 config 中获取 headless 和 timeout - if self.config.get('headless') is not None: - self.headless = self.config.get('headless') - if self.config.get('timeout') is not None: - self.timeout = self.config.get('timeout') - - self.browser = DrissionBrowser(headless=self.headless, timeout=self.timeout) - self.markdown_converter = MarkdownConverter() - # 从配置中读取 Cookie 文件路径，支持环境变量展开 - # 直接读取 cookie_file（因为 self.config 已经是 zhihu 配置） - cookie_file = self.config.get('cookie_file') - if cookie_file: - self.cookie_file = os.path.expanduser(cookie_file) - else: - # 默认路径 - self.cookie_file = os.path.join( - os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), - 'data', 'cookies', 'zhihu.json' - ) - - def identify_url_type(self, url: str) -> Optional[str]: - """ - 识别 URL 类型 - - Returns: - 'answer', 'article', 'question' 或 None - """ - for url_type, pattern in self.URL_PATTERNS.items(): - if re.search(pattern, url): - return url_type - return None - - def extract_id(self, url: str) -> Optional[str]: - """ - 从 URL 中提取内容 ID - - Returns: - 内容 ID 或 None - """ - url_type = self.identify_url_type(url) - if url_type: - pattern = self.URL_PATTERNS[url_type] - match = re.search(pattern, url) - if match: - return match.group(1) - return None - - def _extract_text_content(self, element) -> str: - """提取元素的文本内容""" - if element is None: - return '' - return element.text if hasattr(element, 'text') else '' - - def _extract_html_content(self, element) -> str: - """提取元素的 HTML 内容""" - if element is None: - return '' - return element.html if hasattr(element, 'html') else '' - - def _extract_attribute(self, selector: str, attribute: str) -> str: - """提取元素的属性""" - try: - element = self.browser.query_selector(selector) - if element: - return element.attr(attribute) or '' - except Exception as e: - print(f"提取属性时出错（选择器: {selector}, 属性: {attribute}）: {e}") - return '' - - def _load_cookies(self) -> List[Dict]: - """ - 加载 Cookie，优先级：配置 > 环境 > 文件 - - Returns: - Cookie 列表 - """ - cookies = [] - - # 1. 尝试从配置加载（优先级最高） - # 支持扁平和嵌套配置结构 - cookie_str = self.config.get('cookie_str') - if cookie_str: - try: - cookies = json.loads(cookie_str) - print(f"从配置加载 Cookie，共 {len(cookies)} 个") - except json.JSONDecodeError as e: - print(f"配置中的 Cookie JSON 格式错误: {e}") - - # 2. 尝试从环境变量加载（优先级中等） - elif os.getenv('ZHIHU_COOKIE'): - try: - cookies = json.loads(os.getenv('ZHIHU_COOKIE')) - print(f"从环境变量加载 Cookie，共 {len(cookies)} 个") - except json.JSONDecodeError as e: - print(f"环境变量中的 Cookie JSON 格式错误: {e}") - - # 3. 尝试从文件加载（优先级最低） - else: - # 直接从配置中读取 cookie_file（因为 self.config 已经是 zhihu 配置） - cookie_file = self.config.get('cookie_file') - - if not cookie_file: - # 如果没有配置，使用默认路径 - cookie_file = self.cookie_file - else: - # 展开环境变量（如 ~） - cookie_file = os.path.expanduser(cookie_file) - - if cookie_file and Path(cookie_file).exists(): - try: - with open(cookie_file, 'r', encoding='utf-8') as f: - cookies = json.load(f) - print(f"从文件加载 Cookie ({cookie_file})，共 {len(cookies)} 个") - except (json.JSONDecodeError, FileNotFoundError, IOError) as e: - print(f"从文件加载 Cookie 失败: {e}") - else: - print(f"未找到 Cookie 配置，将使用未登录状态访问知乎") - print(f" 期望路径: {cookie_file}") - - return cookies - - def _apply_cookies(self, cookies: List[Dict]): - """ - 应用 Cookie 到浏览器页面 - - Args: - cookies: Cookie 列表 - """ - if not cookies: - return - - try: - for cookie in cookies: - if self.browser and self.browser.page: - self.browser.page.set.cookies.add(cookie) - print(f"成功应用 {len(cookies)} 个 Cookie") - except Exception as e: - print(f"应用 Cookie 时出错: {e}") - - def _extract_answer_content(self) -> Dict[str, Any]: - """提取回答内容""" - result = { - 'type': 'answer', - 'title': '', - 'author': '', - 'content': '', - 'content_markdown': '', - 'publish_time': '', - 'vote_up': 0, - } - - # 打印页面标题用于调试 - page_title = self.browser.get_page_title() - print(f"页面标题: {page_title}") - - # 尝试保存页面快照用于调试 - try: - import os - debug_dir = '/home/admin/.openclaw/workspace/debug' - os.makedirs(debug_dir, exist_ok=True) - self.browser.screenshot(os.path.join(debug_dir, 'zhihu_drission_screenshot.png')) - with open(os.path.join(debug_dir, 'zhihu_drission_page.html'), 'w', encoding='utf-8') as f: - f.write(self.browser.get_page_content()) - print(f"页面快照已保存到 {debug_dir}") - except Exception as e: - print(f"保存页面快照时出错: {e}") - - # 尝试多个可能的选择器 - selectors = { - 'title': ['h1.QuestionHeader-title', 'h1', '.QuestionHeader-main'], - 'author': ['.AuthorInfo-name', '.AuthorInfo .Text', '[itemprop="name"]'], - 'content': ['.RichContent-inner', '.RichContent', '.List-itemText', '[data-zop-feedlist] article'], - 'time': ['.ContentItem-time', '[itemprop="dateCreated"]'], - 'vote': ['.VoteButton--up .VoteButton-label', '.VoteButton--up'] - } - - for selector in selectors['title']: - title_elem = self.browser.query_selector(selector) - if title_elem: - result['title'] = self._extract_text_content(title_elem) - print(f"找到标题（使用选择器 {selector}）: {result['title'][:50]}...") - break - - for selector in selectors['author']: - author_elem = self.browser.query_selector(selector) - if author_elem: - result['author'] = self._extract_text_content(author_elem) - print(f"找到作者（使用选择器 {selector}）: {result['author']}") - break - - for selector in selectors['content']: - content_elem = self.browser.query_selector(selector) - if content_elem: - html_content = self._extract_html_content(content_elem) - result['content'] = html_content - result['content_markdown'] = self.markdown_converter.convert(html_content) - print(f"找到内容（使用选择器 {selector}），长度: {len(result['content_markdown'])}") - break - - for selector in selectors['time']: - time_elem = self.browser.query_selector(selector) - if time_elem: - result['publish_time'] = time_elem.attr('data-tooltip') or self._extract_text_content(time_elem) - break - - for selector in selectors['vote']: - vote_elem = self.browser.query_selector(selector) - if vote_elem: - vote_text = self._extract_text_content(vote_elem) - try: - result['vote_up'] = int(vote_text.replace(',', '')) - except (ValueError, AttributeError): - pass - break - - return result - - def _extract_article_content(self) -> Dict[str, Any]: - """提取文章内容""" - result = { - 'type': 'article', - 'title': '', - 'author': '', - 'content': '', - 'content_markdown': '', - 'publish_time': '', - 'vote_up': 0, - } - - # 提取文章标题 - title_elem = self.browser.query_selector('.Post-Title') - if title_elem: - result['title'] = self._extract_text_content(title_elem) - - # 提取作者信息 - author_elem = self.browser.query_selector('.AuthorInfo-name') - if author_elem: - result['author'] = self._extract_text_content(author_elem) - - # 提取文章内容 - content_elem = self.browser.query_selector('.Post-RichText') - if content_elem: - html_content = self._extract_html_content(content_elem) - result['content'] = html_content - result['content_markdown'] = self.markdown_converter.convert(html_content) - - # 提取发布时间 - time_elem = self.browser.query_selector('.ContentItem-time') - if time_elem: - result['publish_time'] = time_elem.attr('data-tooltip') or self._extract_text_content(time_elem) - - # 提取点赞数 - vote_elem = self.browser.query_selector('.VoteButton--up .VoteButton-label') - if vote_elem: - vote_text = self._extract_text_content(vote_elem) - try: - result['vote_up'] = int(vote_text.replace(',', '')) - except (ValueError, AttributeError): - pass - - return result - - def _extract_question_content(self) -> Dict[str, Any]: - """提取问题内容（问题页本身）""" - result = { - 'type': 'question', - 'title': '', - 'author': '', - 'content': '', - 'content_markdown': '', - 'publish_time': '', - 'vote_up': 0, - } - - # 提取问题标题 - title_elem = self.browser.query_selector('h1.QuestionHeader-title') - if title_elem: - result['title'] = self._extract_text_content(title_elem) - - # 提取问题详情 - detail_elem = self.browser.query_selector('.QuestionHeader-detail') - if detail_elem: - html_content = self._extract_html_content(detail_elem) - result['content'] = html_content - result['content_markdown'] = self.markdown_converter.convert(html_content) - - # 问题的作者通常是提问者 - author_elem = self.browser.query_selector('.AuthorInfo-name') - if author_elem: - result['author'] = self._extract_text_content(author_elem) - - return result - - def extract(self, url: str) -> Optional[Dict[str, Any]]: - """ - 从 URL 提取知乎内容 - - Args: - url: 知乎 URL - - Returns: - 包含提取内容的字典，结构如下: - { - 'type': 'answer' | 'article' | 'question', - 'title': str, - 'author': str, - 'content': str (HTML), - 'content_markdown': str (Markdown), - 'publish_time': str, - 'vote_up': int, - 'url': str, - 'content_id': str, - } - 如果提取失败返回 None - """ - try: - # 识别 URL 类型 - url_type = self.identify_url_type(url) - if not url_type: - print(f"无法识别 URL 类型: {url}") - return None - - print(f"URL 类型: {url_type}") - - # 加载 Cookie - cookies = self._load_cookies() - if cookies: - print(f"加载了 {len(cookies)} 个 Cookie") - - print(f"启动浏览器...") - - # 启动浏览器 - self.browser.start() - - # 应用 Cookie - if cookies and self.browser.page: - self._apply_cookies(cookies) - - print(f"访问页面: {url}") - # 访问页面 - self.browser.goto(url) - - print("等待页面加载...") - import time - time.sleep(3) # 等待 JavaScript 执行 - - # 尝试多个可能的选择器 - if url_type == 'answer': - selectors = ['.RichContent-inner', '.RichContent', '.List-itemText', '[data-zop-feedlist]'] - print(f"尝试查找选择器: {selectors}") - result = self._extract_answer_content() - elif url_type == 'article': - selectors = ['.Post-RichText', '.Post-Title'] - print(f"尝试查找选择器: {selectors}") - result = self._extract_article_content() - else: # question - selectors = ['.QuestionHeader-title', 'h1'] - print(f"尝试查找选择器: {selectors}") - result = self._extract_question_content() - - # 添加额外信息 - result['url'] = url - result['content_id'] = self.extract_id(url) - - print(f"提取成功! 标题: {result['title'][:50]}...") - return result - - except Exception as e: - print(f"提取知乎内容时出错: {e}") - import traceback - traceback.print_exc() - return None - finally: - # 关闭浏览器 - self.browser.close() - - def __enter__(self): - """上下文管理器入口""" - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - """上下文管理器出口""" - self.browser.close() - return False - - -# 便捷函数 -def extract_zhihu_content(url: str, config=None) -> Optional[Dict[str, Any]]: - """ - 便捷函数：提取知乎内容 - - Args: - url: 知乎 URL - config: 配置字典 - - Returns: - 提取的内容字典 - """ - with ZhihuDrissionExtractor(config=config) as extractor: - return extractor.extract(url) diff --git a/skills/kb-workflow/src/sources/zhihu_playwright_extractor.py b/skills/kb-workflow/src/sources/zhihu_playwright_extractor.py deleted file mode 100644 index 2baf648..0000000 --- a/skills/kb-workflow/src/sources/zhihu_playwright_extractor.py +++ /dev/null @@ -1,385 +0,0 @@ -""" -知乎 Playwright 内容提取器 -使用 Playwright 浏览器自动化工具抓取知乎内容 -""" - -import re -import sys -import os -from typing import Dict, Optional, Any -from urllib.parse import urlparse - -# 添加当前目录到 Python 路径以支持绝对导入 -current_dir = os.path.dirname(os.path.abspath(__file__)) -if current_dir not in sys.path: - sys.path.insert(0, current_dir) - -try: - from .markdown_converter import MarkdownConverter -except ImportError: - from markdown_converter import MarkdownConverter - - -class PlaywrightBrowser: - """Playwright 浏览器管理器""" - - def __init__(self, headless: bool = True, timeout: int = 30): - self.headless = headless - self.timeout = timeout * 1000 - self.browser = None - self.context = None - self.page = None - - def start(self): - """启动浏览器""" - from playwright.sync_api import sync_playwright - self.playwright = sync_playwright().start() - self.browser = self.playwright.chromium.launch(headless=self.headless) - self.context = self.browser.new_context( - user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36' - ) - self.page = self.context.new_page() - self.page.set_default_timeout(self.timeout) - - def close(self): - """关闭浏览器""" - if self.page: - self.page.close() - if self.context: - self.context.close() - if self.browser: - self.browser.close() - if hasattr(self, 'playwright'): - self.playwright.stop() - - def goto(self, url: str, wait_until: str = "networkidle"): - """导航到指定URL""" - return self.page.goto(url, wait_until=wait_until) - - def wait_for_selector(self, selector: str, timeout: int = None): - """等待选择器出现""" - timeout_ms = timeout * 1000 if timeout else self.timeout - return self.page.wait_for_selector(selector, timeout=timeout_ms) - - def query_selector(self, selector: str): - """查询选择器""" - return self.page.query_selector(selector) - - def query_selector_all(self, selector: str): - """查询所有匹配的选择器""" - return self.page.query_selector_all(selector) - - def evaluate(self, script: str): - """执行 JavaScript 代码""" - return self.page.evaluate(script) - - -class ZhihuPlaywrightExtractor: - """知乎内容提取器 - 使用 Playwright""" - - URL_PATTERNS = { - 'answer': r'zhihu\.com/question/\d+/answer/(\d+)', - 'article': r'zhuanlan\.zhihu\.com/p/(\d+)', - 'question': r'zhihu\.com/question/(\d+)(?:$|/)', - } - - def __init__(self, headless: bool = True, timeout: int = 30): - """ - 初始化提取器 - - Args: - headless: 是否使用无头浏览器 - timeout: 超时时间（秒） - """ - self.browser = PlaywrightBrowser(headless=headless, timeout=timeout) - self.markdown_converter = MarkdownConverter() - - def identify_url_type(self, url: str) -> Optional[str]: - """ - 识别 URL 类型 - - Returns: - 'answer', 'article', 'question' 或 None - """ - for url_type, pattern in self.URL_PATTERNS.items(): - if re.search(pattern, url): - return url_type - return None - - def extract_id(self, url: str) -> Optional[str]: - """ - 从 URL 中提取内容 ID - - Returns: - 内容 ID 或 None - """ - url_type = self.identify_url_type(url) - if url_type: - pattern = self.URL_PATTERNS[url_type] - match = re.search(pattern, url) - if match: - return match.group(1) - return None - - def _extract_text_content(self, element) -> str: - """提取元素的文本内容""" - if element is None: - return '' - return element.inner_text() if hasattr(element, 'inner_text') else '' - - def _extract_html_content(self, element) -> str: - """提取元素的 HTML 内容""" - if element is None: - return '' - return element.inner_html() if hasattr(element, 'inner_html') else '' - - def _extract_answer_content(self) -> Dict[str, Any]: - """提取回答内容""" - result = { - 'type': 'answer', - 'title': '', - 'author': '', - 'content': '', - 'content_markdown': '', - 'publish_time': '', - 'vote_up': 0, - } - - # 打印页面标题用于调试 - page_title = self.browser.page.title() - print(f"页面标题: {page_title}") - - # 尝试保存页面快照用于调试 - try: - import os - debug_dir = '/home/admin/.openclaw/workspace/debug' - os.makedirs(debug_dir, exist_ok=True) - self.browser.page.screenshot(path=os.path.join(debug_dir, 'zhihu_screenshot.png')) - with open(os.path.join(debug_dir, 'zhihu_page.html'), 'w', encoding='utf-8') as f: - f.write(self.browser.page.content()) - print(f"页面快照已保存到 {debug_dir}") - except Exception as e: - print(f"保存页面快照时出错: {e}") - - # 尝试多个可能的选择器 - selectors = { - 'title': ['h1.QuestionHeader-title', 'h1', '.QuestionHeader-main'], - 'author': ['.AuthorInfo-name', '.AuthorInfo .Text', '[itemprop="name"]'], - 'content': ['.RichContent-inner', '.RichContent', '.List-itemText', '[data-zop-feedlist] article'], - 'time': ['.ContentItem-time', '[itemprop="dateCreated"]'], - 'vote': ['.VoteButton--up .VoteButton-label', '.VoteButton--up'] - } - - for selector in selectors['title']: - title_elem = self.browser.query_selector(selector) - if title_elem: - result['title'] = self._extract_text_content(title_elem) - print(f"找到标题（使用选择器 {selector}）: {result['title'][:50]}...") - break - - for selector in selectors['author']: - author_elem = self.browser.query_selector(selector) - if author_elem: - result['author'] = self._extract_text_content(author_elem) - print(f"找到作者（使用选择器 {selector}）: {result['author']}") - break - - for selector in selectors['content']: - content_elem = self.browser.query_selector(selector) - if content_elem: - html_content = self._extract_html_content(content_elem) - result['content'] = html_content - result['content_markdown'] = self.markdown_converter.convert(html_content) - print(f"找到内容（使用选择器 {selector}），长度: {len(result['content_markdown'])}") - break - - for selector in selectors['time']: - time_elem = self.browser.query_selector(selector) - if time_elem: - result['publish_time'] = time_elem.get_attribute('data-tooltip') or self._extract_text_content(time_elem) - break - - for selector in selectors['vote']: - vote_elem = self.browser.query_selector(selector) - if vote_elem: - vote_text = self._extract_text_content(vote_elem) - try: - result['vote_up'] = int(vote_text.replace(',', '')) - except (ValueError, AttributeError): - pass - break - - return result - - def _extract_article_content(self) -> Dict[str, Any]: - """提取文章内容""" - result = { - 'type': 'article', - 'title': '', - 'author': '', - 'content': '', - 'content_markdown': '', - 'publish_time': '', - 'vote_up': 0, - } - - # 提取文章标题 - title_elem = self.browser.query_selector('.Post-Title') - if title_elem: - result['title'] = self._extract_text_content(title_elem) - - # 提取作者信息 - author_elem = self.browser.query_selector('.AuthorInfo-name') - if author_elem: - result['author'] = self._extract_text_content(author_elem) - - # 提取文章内容 - content_elem = self.browser.query_selector('.Post-RichText') - if content_elem: - html_content = self._extract_html_content(content_elem) - result['content'] = html_content - result['content_markdown'] = self.markdown_converter.convert(html_content) - - # 提取发布时间 - time_elem = self.browser.query_selector('.ContentItem-time') - if time_elem: - result['publish_time'] = time_elem.get_attribute('data-tooltip') or self._extract_text_content(time_elem) - - # 提取点赞数 - vote_elem = self.browser.query_selector('.VoteButton--up .VoteButton-label') - if vote_elem: - vote_text = self._extract_text_content(vote_elem) - try: - result['vote_up'] = int(vote_text.replace(',', '')) - except (ValueError, AttributeError): - pass - - return result - - def _extract_question_content(self) -> Dict[str, Any]: - """提取问题内容（问题页本身）""" - result = { - 'type': 'question', - 'title': '', - 'author': '', - 'content': '', - 'content_markdown': '', - 'publish_time': '', - 'vote_up': 0, - } - - # 提取问题标题 - title_elem = self.browser.query_selector('h1.QuestionHeader-title') - if title_elem: - result['title'] = self._extract_text_content(title_elem) - - # 提取问题详情 - detail_elem = self.browser.query_selector('.QuestionHeader-detail') - if detail_elem: - html_content = self._extract_html_content(detail_elem) - result['content'] = html_content - result['content_markdown'] = self.markdown_converter.convert(html_content) - - # 问题的作者通常是提问者 - author_elem = self.browser.query_selector('.AuthorInfo-name') - if author_elem: - result['author'] = self._extract_text_content(author_elem) - - return result - - def extract(self, url: str) -> Optional[Dict[str, Any]]: - """ - 从 URL 提取知乎内容 - - Args: - url: 知乎 URL - - Returns: - 包含提取内容的字典，结构如下: - { - 'type': 'answer' | 'article' | 'question', - 'title': str, - 'author': str, - 'content': str (HTML), - 'content_markdown': str (Markdown), - 'publish_time': str, - 'vote_up': int, - } - 如果提取失败返回 None - """ - try: - # 识别 URL 类型 - url_type = self.identify_url_type(url) - if not url_type: - print(f"无法识别 URL 类型: {url}") - return None - - print(f"URL 类型: {url_type}") - print(f"启动浏览器...") - - # 启动浏览器 - self.browser.start() - - print(f"访问页面: {url}") - # 访问页面 - self.browser.goto(url, wait_until="domcontentloaded") - - print("等待页面加载...") - import time - time.sleep(3) # 等待 JavaScript 执行 - - # 尝试多个可能的选择器 - if url_type == 'answer': - selectors = ['.RichContent-inner', '.RichContent', '.List-itemText', '[data-zop-feedlist]'] - print(f"尝试查找选择器: {selectors}") - result = self._extract_answer_content() - elif url_type == 'article': - selectors = ['.Post-RichText', '.Post-Title'] - print(f"尝试查找选择器: {selectors}") - result = self._extract_article_content() - else: # question - selectors = ['.QuestionHeader-title', 'h1'] - print(f"尝试查找选择器: {selectors}") - result = self._extract_question_content() - - # 添加额外信息 - result['url'] = url - result['content_id'] = self.extract_id(url) - - print(f"提取成功! 标题: {result['title'][:50]}...") - return result - - except Exception as e: - print(f"提取知乎内容时出错: {e}") - import traceback - traceback.print_exc() - return None - finally: - # 关闭浏览器 - self.browser.close() - - def __enter__(self): - """上下文管理器入口""" - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - """上下文管理器出口""" - self.browser.close() - return False - - -# 便捷函数 -def extract_zhihu_content(url: str, headless: bool = True, timeout: int = 30) -> Optional[Dict[str, Any]]: - """ - 便捷函数：提取知乎内容 - - Args: - url: 知乎 URL - headless: 是否使用无头浏览器 - timeout: 超时时间（秒） - - Returns: - 提取的内容字典 - """ - with ZhihuPlaywrightExtractor(headless=headless, timeout=timeout) as extractor: - return extractor.extract(url) diff --git a/skills/kb-workflow/src/storage.py b/skills/kb-workflow/src/storage.py deleted file mode 100644 index 4ec146b..0000000 --- a/skills/kb-workflow/src/storage.py +++ /dev/null @@ -1,168 +0,0 @@ -"""Obsidian KB - 存储管理""" - -import os -import json -from pathlib import Path -from typing import List, Optional, Dict, Any -from datetime import datetime - -from models import Content, ContentStatus -from config import get_config - - -class Storage: - """JSON文件存储管理""" - - def __init__(self, base_path: Optional[str] = None): - config = get_config() - self.base_path = base_path or config.obsidian_path - self.inbox_path = os.path.join(self.base_path, 'Inbox') - self.topics_path = os.path.join(self.base_path, 'Topics') - self._ensure_dirs() - - def _ensure_dirs(self) -> None: - """确保目录存在""" - os.makedirs(self.inbox_path, exist_ok=True) - os.makedirs(self.topics_path, exist_ok=True) - - def _content_file_path(self, content_id: str) -> str: - """获取内容文件路径""" - return os.path.join(self.inbox_path, f'{content_id}.json') - - def _topic_index_path(self, topic: str) -> str: - """获取主题索引路径""" - topic_dir = os.path.join(self.topics_path, topic) - os.makedirs(topic_dir, exist_ok=True) - return os.path.join(topic_dir, 'index.json') - - # ===== Inbox 管理 ===== - - def save_content(self, content: Content) -> None: - """保存内容到Inbox""" - file_path = self._content_file_path(content.id) - with open(file_path, 'w', encoding='utf-8') as f: - json.dump(content.to_dict(), f, ensure_ascii=False, indent=2) - - def load_content(self, content_id: str) -> Optional[Content]: - """加载指定内容""" - file_path = self._content_file_path(content_id) - if not os.path.exists(file_path): - return None - with open(file_path, 'r', encoding='utf-8') as f: - data = json.load(f) - return Content.from_dict(data) - - def delete_content(self, content_id: str) -> bool: - """删除内容""" - file_path = self._content_file_path(content_id) - if os.path.exists(file_path): - os.remove(file_path) - return True - return False - - def list_inbox(self, status: Optional[ContentStatus] = None) -> List[Content]: - """列出Inbox中的内容""" - contents = [] - if not os.path.exists(self.inbox_path): - return contents - - for filename in os.listdir(self.inbox_path): - if filename.endswith('.json'): - file_path = os.path.join(self.inbox_path, filename) - with open(file_path, 'r', encoding='utf-8') as f: - data = json.load(f) - content = Content.from_dict(data) - if status is None or content.status == status: - contents.append(content) - - # 按创建时间排序 - contents.sort(key=lambda x: x.created_at, reverse=True) - return contents - - def get_pending_count(self) -> int: - """获取待处理内容数量""" - return len(self.list_inbox(ContentStatus.CONFIRMED)) - - def get_stats(self) -> Dict[str, int]: - """获取统计信息""" - all_contents = self.list_inbox() - stats = {status.value: 0 for status in ContentStatus} - for content in all_contents: - stats[content.status.value] += 1 - return stats - - # ===== Topics 管理 ===== - - def add_to_topic(self, content: Content, topic: str) -> None: - """将内容添加到主题""" - index_path = self._topic_index_path(topic) - - # 读取现有索引 - entries = [] - if os.path.exists(index_path): - with open(index_path, 'r', encoding='utf-8') as f: - entries = json.load(f) - - # 添加新条目 - entry = { - 'id': content.id, - 'title': content.title, - 'url': content.url, - 'added_at': datetime.now().isoformat(), - 'output_path': content.output_path - } - - # 避免重复 - entries = [e for e in entries if e['id'] != content.id] - entries.append(entry) - - # 保存索引 - with open(index_path, 'w', encoding='utf-8') as f: - json.dump(entries, f, ensure_ascii=False, indent=2) - - def get_topic_contents(self, topic: str) -> List[Dict[str, Any]]: - """获取主题下的所有内容""" - index_path = self._topic_index_path(topic) - if not os.path.exists(index_path): - return [] - with open(index_path, 'r', encoding='utf-8') as f: - return json.load(f) - - def list_topics(self) -> List[str]: - """列出所有主题""" - if not os.path.exists(self.topics_path): - return [] - return [d for d in os.listdir(self.topics_path) - if os.path.isdir(os.path.join(self.topics_path, d))] - - def search(self, query: str) -> List[Content]: - """搜索内容""" - results = [] - all_contents = self.list_inbox() - query_lower = query.lower() - - for content in all_contents: - if (query_lower in content.title.lower() or - query_lower in content.summary.lower() or - query_lower in content.author.lower() or - any(query_lower in tag.lower() for tag in content.tags)): - results.append(content) - - return results - - def move_to_archive(self, content_id: str) -> bool: - """将内容移动到归档""" - content = self.load_content(content_id) - if not content: - return False - - archive_path = os.path.join(self.base_path, 'Archive') - os.makedirs(archive_path, exist_ok=True) - - source_file = self._content_file_path(content_id) - target_file = os.path.join(archive_path, f'{content_id}.json') - - if os.path.exists(source_file): - os.rename(source_file, target_file) - return True - return False diff --git a/skills/kb-workflow/src/template.py b/skills/kb-workflow/src/template.py deleted file mode 100644 index 2c71873..0000000 --- a/skills/kb-workflow/src/template.py +++ /dev/null @@ -1,71 +0,0 @@ -"""Template engine for note generation.""" - -from typing import Any, Dict -from pathlib import Path - - -class TemplateEngine: - """Manages templates for Obsidian note generation.""" - - def __init__(self, templates_dir: str = None): - """Initialize template engine. - - Args: - templates_dir: Directory containing template files - """ - if templates_dir is None: - base_dir = Path(__file__).parent.parent - templates_dir = base_dir / 'src' / 'templates' - - self.templates_dir = Path(templates_dir) - self.templates_dir.mkdir(parents=True, exist_ok=True) - - def render(self, template_name: str, context: Dict[str, Any]) -> str: - """Render a template with context. - - Args: - template_name: Name of template to use - context: Template context variables - - Returns: - Rendered template string - """ - template_path = self.templates_dir / f"{template_name}.md" - - if template_path.exists(): - template = template_path.read_text(encoding='utf-8') - else: - # Default template - template = self._default_template() - - # Simple variable substitution - result = template - for key, value in context.items(): - placeholder = f"{{{{{key}}}}}" - result = result.replace(placeholder, str(value)) - - return result - - def _default_template(self) -> str: - """Get default note template.""" - return """# {{{title}}} - -{{tags}} - -## Summary - -{{summary}} - -## Original Content - -**Source:** [{{platform}}]({{url}}) -**ID:** {{content_id}} - ---- - -{{content}} - -## Notes - - -""" diff --git a/skills/kb-workflow/src/template_renderer.py b/skills/kb-workflow/src/template_renderer.py deleted file mode 100644 index 5661b78..0000000 --- a/skills/kb-workflow/src/template_renderer.py +++ /dev/null @@ -1,307 +0,0 @@ -""" -Template Renderer Module -Handles rendering of content into Markdown templates using Jinja2. -""" -import os -from datetime import datetime -from typing import Dict, Any, Optional -from pathlib import Path - -# Try to import jinja2, fallback to simple string formatting -try: - from jinja2 import Template, Environment, FileSystemLoader - JINJA2_AVAILABLE = True -except ImportError: - JINJA2_AVAILABLE = False - Template = None - Environment = None - FileSystemLoader = None - - -class TemplateRenderer: - """ - Renders content objects into Markdown templates. - Supports both Jinja2 and simple string formatting. - """ - - def __init__(self, templates_dir: Optional[str] = None): - """ - Initialize the template renderer. - - Args: - templates_dir: Path to templates directory. If None, uses default. - """ - if templates_dir is None: - # Default to templates directory next to this file - self.templates_dir = Path(__file__).parent / "templates" - else: - self.templates_dir = Path(templates_dir) - - self.templates_dir.mkdir(parents=True, exist_ok=True) - - # Initialize Jinja2 environment if available - if JINJA2_AVAILABLE: - self.env = Environment( - loader=FileSystemLoader(str(self.templates_dir)), - trim_blocks=True, - lstrip_blocks=True - ) - else: - self.env = None - - def _get_template_name(self, content: Any) -> str: - """ - Determine which template to use based on content type and duration. - - Args: - content: Content object with content_type and duration attributes - - Returns: - Template filename - """ - content_type = getattr(content, 'content_type', None) - duration = getattr(content, 'duration', 0) - - # Handle enum types - if hasattr(content_type, 'value'): - content_type = content_type.value - - # Convert duration to seconds if it's a string - if isinstance(duration, str): - try: - parts = duration.split(':') - if len(parts) == 2: # MM:SS - duration = int(parts[0]) * 60 + int(parts[1]) - elif len(parts) == 3: # HH:MM:SS - duration = int(parts[0]) * 3600 + int(parts[1]) * 60 + int(parts[2]) - else: - duration = 0 - except (ValueError, IndexError): - duration = 0 - - # Select template based on type and duration - if content_type == 'article': - return 'article.md' - elif content_type == 'video': - # Long videos (>>30 min) use tutorial template - if duration and duration > 1800: # 30 minutes - return 'tutorial_video.md' - else: - return 'light_video.md' - elif duration and duration > 1800: - return 'tutorial_video.md' - else: - return 'light_video.md' - - def _prepare_context(self, content: Any, extra_context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: - """ - Prepare the context dictionary for template rendering. - - Args: - content: Content object - extra_context: Additional context to merge - - Returns: - Context dictionary - """ - # Get analysis data if available - analysis_data = {} - if hasattr(content, 'analysis') and content.analysis: - if isinstance(content.analysis, dict): - analysis_data = content.analysis - else: - # It's a ContentAnalysis object - analysis_data = { - 'one_liner': getattr(content.analysis, 'one_liner', ''), - 'summary': getattr(content.analysis, 'summary', ''), - 'key_points': getattr(content.analysis, 'key_points', []), - 'tags': getattr(content.analysis, 'tags', []), - 'quality_score': getattr(content.analysis, 'quality_score', 0), - 'prerequisites': getattr(content.analysis, 'prerequisites', []), - 'outline': getattr(content.analysis, 'outline', []), - 'pitfalls': getattr(content.analysis, 'pitfalls', []), - 'practice_advice': getattr(content.analysis, 'practice_advice', []), - } - - # Base context from content object - context = { - 'title': getattr(content, 'title', 'Untitled'), - 'source': getattr(content, 'source', 'unknown'), - 'url': getattr(content, 'url', ''), - 'author': getattr(content, 'author', 'Unknown'), - 'duration': getattr(content, 'duration', ''), - 'date': getattr(content, 'date', datetime.now().isoformat()), - 'tags': getattr(content, 'tags', ['untagged']), - # First check analysis_data, then fall back to content attributes - 'one_liner': analysis_data.get('one_liner', getattr(content, 'one_liner', '')), - 'summary': analysis_data.get('summary', getattr(content, 'summary', '')), - 'key_points': analysis_data.get('key_points', getattr(content, 'key_points', [])), - 'quality_score': analysis_data.get('quality_score', getattr(content, 'quality_score', 0)), - 'prerequisites': analysis_data.get('prerequisites', []), - 'outline': analysis_data.get('outline', []), - 'pitfalls': analysis_data.get('pitfalls', []), - 'practice_advice': analysis_data.get('practice_advice', []), - } - - # Add transcript-related data - if hasattr(content, 'transcript'): - context['transcript'] = content.transcript - - # Merge extra context - if extra_context: - context.update(extra_context) - - # Ensure tags is a list - if isinstance(context['tags'], str): - context['tags'] = [tag.strip() for tag in context['tags'].split(',')] - if not context['tags']: - context['tags'] = ['untagged'] - - return context - - def render(self, content: Any, extra_context: Optional[Dict[str, Any]] = None) -> str: - """ - Render content into Markdown using appropriate template. - - Args: - content: Content object to render - extra_context: Additional context for template - - Returns: - Rendered Markdown string - """ - template_name = self._get_template_name(content) - context = self._prepare_context(content, extra_context) - - template_path = self.templates_dir / template_name - - # Check if template exists - if not template_path.exists(): - raise FileNotFoundError(f"Template not found: {template_path}") - - # Read template content - template_content = template_path.read_text(encoding='utf-8') - - # Render using Jinja2 if available - if JINJA2_AVAILABLE and self.env: - template = self.env.from_string(template_content) - return template.render(**context) - else: - # Fallback to simple string formatting - return self._simple_render(template_content, context) - - def _simple_render(self, template_content: str, context: Dict[str, Any]) -> str: - """ - Simple string-based template rendering fallback. - Supports basic {{ variable }} and {% for %} loops. - - Args: - template_content: Template string - context: Context dictionary - - Returns: - Rendered string - """ - result = template_content - - # Handle simple for loops (very basic implementation) - import re - - # Pattern for {% for item in items %}...{% endfor %} - for_pattern = r'{%-?\s*for\s+(\w+)\s+in\s+(\w+)\s*-?%}(.*?){%-?\s*endfor\s*-?%}' - - def replace_for(match): - var_name = match.group(1) - list_name = match.group(2) - inner_template = match.group(3) - - items = context.get(list_name, []) - if not items: - return '' - - output = [] - for item in items: - item_str = inner_template - # Handle {{ var.attribute }} or {{ var }} - if isinstance(item, dict): - for key, value in item.items(): - placeholder1 = f'{{{{{var_name}.{key}}}}}' - placeholder2 = f'{{{{ {var_name}.{key} }}}}' - str_val = str(value) if value is not None else '' - item_str = item_str.replace(placeholder1, str_val) - item_str = item_str.replace(placeholder2, str_val) - else: - placeholder1 = f'{{{{{var_name}}}}}' - placeholder2 = f'{{{{ {var_name} }}}}' - str_val = str(item) if item is not None else '' - item_str = item_str.replace(placeholder1, str_val) - item_str = item_str.replace(placeholder2, str_val) - output.append(item_str) - - return ''.join(output) - - result = re.sub(for_pattern, replace_for, result, flags=re.DOTALL) - - # Handle simple if statements (basic) - if_pattern = r'{%-?\s*if\s+(\w+)\s*-?%}(.*?){%-?\s*endif\s*-?%}' - - def replace_if(match): - var_name = match.group(1) - inner_content = match.group(2) - if context.get(var_name): - return inner_content - return '' - - result = re.sub(if_pattern, replace_if, result, flags=re.DOTALL) - - # Handle variable substitution {{ variable }} or {{variable}} - # Sort keys by length (descending) to avoid partial replacements - for var_name in sorted(context.keys(), key=len, reverse=True): - value = context[var_name] - if isinstance(value, list): - str_val = ', '.join(str(v) for v in value) - else: - str_val = str(value) if value is not None else '' - - # Replace both {{ var }} and {{var}} - result = result.replace(f'{{{{ {var_name} }}}}', str_val) - result = result.replace(f'{{{{{var_name}}}}}', str_val) - - return result - - def render_to_file(self, content: Any, output_path: str, extra_context: Optional[Dict[str, Any]] = None) -> str: - """ - Render content and save to file. - - Args: - content: Content object to render - output_path: Path to output file - extra_context: Additional context for template - - Returns: - Path to rendered file - """ - rendered = self.render(content, extra_context) - - output_file = Path(output_path) - output_file.parent.mkdir(parents=True, exist_ok=True) - output_file.write_text(rendered, encoding='utf-8') - - return str(output_file) - - -# Convenience function -def render_content(content: Any, templates_dir: Optional[str] = None, extra_context: Optional[Dict[str, Any]] = None) -> str: - """ - Convenience function to render content without creating a renderer instance. - - Args: - content: Content object to render - templates_dir: Optional templates directory path - extra_context: Additional context for template - - Returns: - Rendered Markdown string - """ - renderer = TemplateRenderer(templates_dir) - return renderer.render(content, extra_context) diff --git a/skills/kb-workflow/src/templates/article.md b/skills/kb-workflow/src/templates/article.md deleted file mode 100644 index 157c274..0000000 --- a/skills/kb-workflow/src/templates/article.md +++ /dev/null @@ -1,33 +0,0 @@ ---- -title: "{{ title }}" -source: "{{ source }}" -url: "{{ url }}" -author: "{{ author }}" -date: "{{ date }}" -tags:{% for tag in tags %} - - {{ tag }}{% endfor %} -quality_score: {{ quality_score }} -reading_time: {{ reading_time }} ---- - -# {{ title }} - -**原文链接**: [{{ title }}]({{ url }}) - -## 🎯 一句话总结 -{{ one_liner }} - -## 📖 文章摘要 -{{ summary }} - -## 💡 核心观点{% for point in key_points %} -1. {{ point }}{% endfor %} - -## 📝 详细内容 -{{ content }} - -## 🔗 相关链接{% for link in related_links %} -- [{{ link.title }}]({{ link.url }}){% endfor %} - -## 🏷️ 标签 -{% for tag in tags %}#{{ tag }} {% endfor %} diff --git a/skills/kb-workflow/src/templates/light_video.md b/skills/kb-workflow/src/templates/light_video.md deleted file mode 100644 index 59b3148..0000000 --- a/skills/kb-workflow/src/templates/light_video.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: "{{ title }}" -source: "{{ source }}" -url: "{{ url }}" -author: "{{ author }}" -duration: "{{ duration }}" -date: "{{ date }}" -tags:{% for tag in tags %} - - {{ tag }}{% endfor %} ---- - -#{{ tags[0] }} #{{ tags[1] }} - -**一句话摘要**: {{ one_liner }} - -## 摘要 -{{ summary }} - -## 核心要点{% for point in key_points %} -1. {{ point }}{% endfor %} diff --git a/skills/kb-workflow/src/templates/topic_index.md b/skills/kb-workflow/src/templates/topic_index.md deleted file mode 100644 index 78c5303..0000000 --- a/skills/kb-workflow/src/templates/topic_index.md +++ /dev/null @@ -1,16 +0,0 @@ ---- -title: {{topic_name}} -date: {{date}} -tags: [topic-index] ---- - -# {{topic_name}} - -## 简介 -{{description}} - -## 相关内容 -{{content_list}} - -## 学习路径 -{{learning_path}} diff --git a/skills/kb-workflow/src/templates/tutorial_video.md b/skills/kb-workflow/src/templates/tutorial_video.md deleted file mode 100644 index 7f589df..0000000 --- a/skills/kb-workflow/src/templates/tutorial_video.md +++ /dev/null @@ -1,35 +0,0 @@ ---- -title: "{{ title }}" -source: "{{ source }}" -url: "{{ url }}" -up主: "{{ author }}" -duration: "{{ duration }}" -date: "{{ date }}" -tags:{% for tag in tags %} - - {{ tag }}{% endfor %} -quality_score: {{ quality_score }} ---- - -## 🎯 一句话总结 -{{ one_liner }} - -## 📚 前置知识{% for pre in prerequisites %} -- {{ pre }}{% endfor %} - -## 📝 步骤详解{% for section in outline %} -### {{ section.title }} [{{ section.time_range }}] -{{ section.summary }} -{% if section.code_snippet %} -```{{ section.code_language }} -{{ section.code_snippet }} -``` -{% endif %}{% endfor %} - -## 💡 关键要点{% for point in key_points %} -1. {{ point }}{% endfor %} - -## ⚠️ 常见坑点{% for pitfall in pitfalls %} -- {{ pitfall }}{% endfor %} - -## 🤔 实践建议{% for advice in practice_advice %} -- {{ advice }}{% endfor %}