starmen1265
87a1740cd3
- Add cache parameter to UnifiedSegmentationStrategy.__init__() - Add cache logic to segment() method (check before LLM, save after) - Pass cache parameter through SemanticSegmentationEngine.__init__() - Implement missing chunking methods (_segment_single_chunk, _segment_multiple_chunks, etc.) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
859 lines
28 KiB
Python
859 lines
28 KiB
Python
"""语义分段引擎
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使用 LLM 实现智能视频字幕语义分段。
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功能：
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- 视频类型识别（知识类/故事类/访谈类/新闻类/vlog 等）
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- 语义边界检测
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- 内容完整性保护
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- 主题标题生成
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- 可扩展的分段策略架构
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"""
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from __future__ import annotations
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import json
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import logging
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import re
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from typing import List, Optional, Protocol
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import requests.exceptions
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from pydantic import BaseModel, Field, field_validator, ValidationError
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logger = logging.getLogger(__name__)
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# ============================================================================
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# AI Client Protocol
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# ============================================================================
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class AIClientProtocol(Protocol):
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"""AI 客户端协议（与 ASRCorrector 保持一致）"""
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def generate(self, prompt: str, max_tokens: int = 16000,
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disable_reasoning: bool = False) -> str:
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"""生成文本"""
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...
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# ============================================================================
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# Segmentation Strategy Pattern (Plan 13-06)
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# ============================================================================
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class SegmentationStrategy(Protocol):
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"""分段策略接口
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定义了分段策略的通用接口，支持未来扩展不同的分段策略。
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Examples:
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>>> strategy = UnifiedSegmentationStrategy()
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>>> segments = strategy.segment(subtitle_segments, metadata)
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Note:
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遵守用户决策：Phase 13 实现统一策略，不区分视频类型。
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"""
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def segment(
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self,
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segments: List,
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metadata: dict
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) -> List[Union[str, 'VideoSegment']]:
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"""对字幕分段进行语义分段
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Args:
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segments: 字幕分段列表（来自 parser.py 的 Segment 对象）
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metadata: 视频元数据（title, duration 等）
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Returns:
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分段列表（可以是纯文本列表或 VideoSegment 对象列表）
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"""
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...
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class UnifiedSegmentationStrategy:
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"""统一分段策略
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适用于所有视频类型（知识类、故事类、访谈类等）。
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视频类型仅用于 Prompt 上下文，不改变分段策略。
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遵守用户决策：无类型区分，使用统一 Prompt。
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Attributes:
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ai_client: AI 客户端（实现 AIClientProtocol）
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fallback_segmenter: 规则分段器（用于降级）
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"""
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def __init__(
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self,
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ai_client: Optional[AIClientProtocol] = None,
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fallback_segmenter: Optional['SemanticSegmenter'] = None,
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cache: Optional['SemanticCache'] = None
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):
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"""初始化统一分段策略
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Args:
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ai_client: AI 客户端实例，如果为 None 则尝试创建 SimpleAIClient
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fallback_segmenter: 降级时使用的规则分段器
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cache: 缓存管理器，如果为 None 则不使用缓存
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"""
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self.ai_client = ai_client
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self.fallback_segmenter = fallback_segmenter
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self.cache = cache
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# Lazy AI Client 初始化（与 ASRCorrector 相同模式）
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if self.ai_client is None:
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try:
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from kb_workflow.deep_analyzer import SimpleAIClient
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self.ai_client = SimpleAIClient()
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if not self.ai_client.api_key:
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logger.info("AI API key not configured")
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self.ai_client = None
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except Exception as e:
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logger.warning(f"Failed to initialize AI client: {e}")
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self.ai_client = None
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# 初始化降级分段器
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if self.fallback_segmenter is None:
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from kb_workflow.srt.segmenter import SemanticSegmenter
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self.fallback_segmenter = SemanticSegmenter()
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@staticmethod
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def _is_recoverable_error(error: Exception) -> bool:
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"""判断错误是否可恢复
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可恢复错误：网络错误、超时、限流、服务器错误（500/502/503/504）、
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JSON 解析错误（JSONDecodeError, 非 ValidationError 的 ValueError）
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不可恢复错误：其他 HTTP 错误（4xx）、Pydantic 验证错误（ValidationError）
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Args:
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error: 异常对象
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Returns:
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True 如果错误可恢复，False 否则
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"""
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# Pydantic 验证错误（不可恢复）
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# 注意：ValidationError 继承自 ValueError，所以需要先检查
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if isinstance(error, ValidationError):
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return False
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# 网络层错误（可恢复）
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if isinstance(error, requests.exceptions.ConnectionError):
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return True
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if isinstance(error, requests.exceptions.Timeout):
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return True
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# JSON 解析错误（可恢复 - 可能是 LLM 输出格式问题）
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if isinstance(error, (json.JSONDecodeError, ValueError)):
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return True
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# HTTP 错误（根据状态码判断）
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if isinstance(error, requests.exceptions.HTTPError):
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status_code = error.response.status_code if hasattr(error, 'response') and error.response else None
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if status_code in (429, 500, 502, 503, 504):
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return True
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# 其他 4xx 错误不可恢复
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return False
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# 其他错误不可恢复
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return False
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def segment(
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self,
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segments: List,
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metadata: dict
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) -> List['VideoSegment']:
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"""对字幕分段进行语义分段
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使用统一的 LLM Prompt 进行语义分段，不区分视频类型。
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视频类型仅用于 Prompt 上下文。
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Args:
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segments: 字幕分段列表（来自 parser.py 的 Segment 对象）
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metadata: 视频元数据（title, duration 等）
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Returns:
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VideoSegment 对象列表（包含标题和内容）
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"""
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# AI 不可用，直接使用规则分段
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if self.ai_client is None:
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logger.info("AI client unavailable, using fallback segmenter")
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# 将规则分段结果转换为 VideoSegment 格式（无标题）
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paragraphs = self.fallback_segmenter.segment(segments)
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return [
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VideoSegment(title=None, content=para, start_index=0, end_index=0)
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for para in paragraphs
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]
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# 合并字幕文本
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content = " ".join(seg.text for seg in segments)
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# 检查缓存（Plan 14-05: 缓存集成）
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if self.cache is not None:
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cached_result = self.cache.get(content)
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if cached_result is not None:
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logger.info("Using cached segmentation result")
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return cached_result.segments
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# 检查是否需要分块（Plan 14-04: 分块阈值 5000 字符）
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if len(content) <= 5000:
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# 短文本直接处理
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video_segments = self._segment_single_chunk(content, metadata, segments)
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else:
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# 长文本分块处理
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video_segments = self._segment_multiple_chunks(content, metadata, segments)
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# 保存到缓存（Plan 14-05: 缓存结果）
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if self.cache is not None:
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# 构建完整的 SegmentationResult
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result = SegmentationResult(
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video_type=self._infer_video_type(metadata),
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segments=video_segments,
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actual_segment_count=len(video_segments)
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)
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self.cache.set(content, result)
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logger.debug("Saved segmentation result to cache")
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return video_segments
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def _build_prompt(self, content: str, metadata: dict) -> str:
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"""构建语义分段 Prompt
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Args:
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content: 字幕内容
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metadata: 视频元数据
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Returns:
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构建好的 Prompt 字符串
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"""
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# 推断视频类型
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video_type = self._infer_video_type(metadata)
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title = metadata.get("title", "") or ""
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# 构建 Prompt
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total_chars = len(content)
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prompt = f"""你是一个专业的视频内容分析师。请将以下字幕内容按语义分段。
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【视频信息】
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类型：{video_type}
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标题：{title}
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总字数：{total_chars}字
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【字幕内容】
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{content}
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【分段要求】
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1. 建议将内容分为 5-8 个段落（可根据语义完整性灵活调整）
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2. 每个段落约 1000-2000 字（为保持语义完整性可以突破此范围）
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3. 在话题转换处创建新分段，不要混入不同话题的内容
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4. **重要**：保持句子完整性，分段边界应放在自然停顿处（句号。、问号？、感叹号！、省略号……等标点符号后）
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- 基于语义停顿判断，不仅依赖标点符号
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- 保持内容的连贯性和可读性
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5. 保持论点完整性，不要在前提-结论结构中间截断
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6. 为主题/章节类型的分段生成简洁标题（5-15字），论点/观点类型分段不加标题
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【输出格式】
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必须返回以下 JSON 格式（不要添加任何额外文本）：
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```json
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{{
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"video_type": "{video_type}",
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"segments": [
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{{
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"title": "分段标题（如果此分段不需要标题则设为 null）",
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"content": "分段内容",
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"start_index": 0,
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"end_index": 123
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}}
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],
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"actual_segment_count": 6
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}}
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```
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【注意事项】
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- 优先保证语义完整性，不要为了凑数而强行截断
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- 标题应简洁准确，如「机器学习概述」「梯度下降原理」
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- 如果某个分段无法生成合适的标题（如过渡性内容），将 title 设为 null"""
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return prompt
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def _infer_video_type(self, metadata: dict) -> str:
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"""从元数据推断视频类型
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Args:
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metadata: 视频元数据，包含 title, category, tags 等字段
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Returns:
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推断出的视频类型，默认为"知识类"
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Examples:
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>>> strategy = UnifiedSegmentationStrategy()
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>>> strategy._infer_video_type({"title": "Python教程"})
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'知识类'
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>>> strategy._infer_video_type({"category": "影视"})
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'故事类'
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"""
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if not metadata:
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return DEFAULT_VIDEO_TYPE
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# 提取所有文本字段
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title = metadata.get("title", "") or ""
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category = metadata.get("category", "") or ""
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tags = metadata.get("tags", []) or []
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description = metadata.get("description", "") or ""
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# 合并所有文本用于匹配
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all_text = " ".join([title, category, description] + list(tags))
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# 如果没有有效文本，返回默认类型
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if not all_text or all_text.isspace():
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return DEFAULT_VIDEO_TYPE
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# 按优先级匹配类型关键词
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for video_type, keywords in VIDEO_TYPE_KEYWORDS.items():
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for keyword in keywords:
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if keyword in all_text:
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logger.debug(
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f"Detected video type '{video_type}' "
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f"from keyword '{keyword}'"
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)
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return video_type
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# 未匹配到任何类型，返回默认
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logger.debug("No specific video type detected, using default")
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return DEFAULT_VIDEO_TYPE
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def _parse_result(
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self,
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llm_output: str,
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fallback_segments: List
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) -> List[VideoSegment]:
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"""解析 LLM 输出并验证
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Args:
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llm_output: LLM 原始输出
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fallback_segments: 降级时使用的原始分段
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Returns:
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VideoSegment 对象列表
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Raises:
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json.JSONDecodeError: JSON 解析失败
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ValueError: 无法提取 JSON
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ValidationError: Pydantic 验证失败
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"""
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# 提取 JSON
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data = extract_json_from_llm_response(llm_output)
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# Pydantic 验证
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result = SegmentationResult(**data)
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logger.info(
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f"LLM 分段成功: 类型={result.video_type}, "
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f"分段数={result.actual_segment_count}"
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)
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return result.segments
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def _segment_single_chunk(
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self,
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content: str,
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metadata: dict,
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fallback_segments: List
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) -> List[VideoSegment]:
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"""处理单个文本块（不分块）
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Args:
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content: 字幕内容
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metadata: 视频元数据
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fallback_segments: 降级时使用的原始分段
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Returns:
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VideoSegment 对象列表
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"""
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# 构建 Prompt
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prompt = self._build_prompt(content=content, metadata=metadata)
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# 调用 LLM，带重试逻辑（最多 2 次尝试）
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max_attempts = 2
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last_error = None
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last_llm_output = None
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for attempt in range(max_attempts):
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try:
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llm_output = self.ai_client.generate(
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prompt,
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max_tokens=8000,
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disable_reasoning=True # 分段不需要思考模式
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)
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last_llm_output = llm_output
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# 解析和验证
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video_segments = self._parse_result(
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llm_output,
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fallback_segments=fallback_segments
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)
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logger.info(f"LLM semantic segmentation: {len(video_segments)} segments")
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return video_segments
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except Exception as e:
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last_error = e
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is_recoverable = self._is_recoverable_error(e)
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is_last_attempt = (attempt == max_attempts - 1)
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# 可恢复错误且不是最后一次尝试：重试
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if is_recoverable and not is_last_attempt:
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logger.info(
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f"LLM call failed (attempt {attempt + 1}/{max_attempts}): "
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f"{type(e).__name__}: {e}, retrying..."
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)
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continue
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# 不可恢复错误或最后一次尝试：降级
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error_type = type(e).__name__
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# 为 HTTPError 提取更详细的信息
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if isinstance(e, requests.exceptions.HTTPError) and hasattr(e, 'response') and e.response:
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status_code = e.response.status_code
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response_text = getattr(e.response, 'text', '') or ''
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error_msg = f"HTTP {status_code}"
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if response_text and isinstance(response_text, str):
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error_msg += f" ({response_text[:100]})"
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logger.warning(
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f"{error_type}: {error_msg}, 降级到规则分段"
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)
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# 对于 JSON 解析错误（排除 ValidationError），记录 LLM 输出片段（前 500 字符）
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elif isinstance(e, (json.JSONDecodeError, ValueError)) and not isinstance(e, ValidationError) and last_llm_output:
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error_msg = str(e) if str(e) else error_type
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logger.warning(
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f"{error_type}: {error_msg}\n"
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f"LLM 输出片段: {last_llm_output[:500]}...\n"
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f"降级到规则分段"
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)
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else:
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error_msg = str(e) if str(e) else error_type
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logger.warning(
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f"{error_type}: {error_msg}, 降级到规则分段"
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)
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break
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# 降级到规则分段
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logger.info("使用规则分段（降级）")
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paragraphs = self.fallback_segmenter.segment(fallback_segments)
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return [
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VideoSegment(title=None, content=para, start_index=0, end_index=0)
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for para in paragraphs
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]
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def _segment_multiple_chunks(
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self,
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content: str,
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metadata: dict,
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fallback_segments: List
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) -> List[VideoSegment]:
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"""处理多个文本块（分块）
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Args:
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content: 字幕内容
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metadata: 视频元数据
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fallback_segments: 降级时使用的原始分段
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Returns:
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VideoSegment 对象列表
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"""
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logger.info(f"Content too long ({len(content)} chars), splitting into chunks")
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# 分块
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chunks = self._split_into_chunks(content, max_chunk_size=5000)
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logger.info(f"Split into {len(chunks)} chunks")
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# 处理每个分块
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chunk_results = []
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for i, (chunk_text, offset) in enumerate(chunks, 1):
|
||
logger.info(f"Processing chunk {i}/{len(chunks)} (offset={offset}, length={len(chunk_text)})")
|
||
|
||
# 构建该分块的 prompt
|
||
chunk_metadata = metadata.copy()
|
||
chunk_metadata['chunk_info'] = f"第 {i}/{len(chunks)} 块"
|
||
prompt = self._build_prompt(content=chunk_text, metadata=chunk_metadata)
|
||
|
||
try:
|
||
# 调用 LLM
|
||
llm_output = self.ai_client.generate(prompt, max_tokens=8000, disable_reasoning=True)
|
||
segments = self._parse_result(llm_output, fallback_segments=fallback_segments)
|
||
chunk_results.append((segments, offset))
|
||
logger.info(f"Chunk {i} segmented successfully: {len(segments)} segments")
|
||
except Exception as e:
|
||
logger.warning(f"Chunk {i} failed: {e}, using fallback for this chunk")
|
||
# 该分块降级到规则分段
|
||
chunk_segments = self._fallback_chunk(chunk_text, offset)
|
||
chunk_results.append((chunk_segments, offset))
|
||
|
||
# 合并结果
|
||
return self._merge_segment_results(chunk_results)
|
||
|
||
def _split_into_chunks(self, text: str, max_chunk_size: int = 5000) -> List[tuple[str, int]]:
|
||
"""按句子边界分割文本为多个块
|
||
|
||
Args:
|
||
text: 待分割的文本
|
||
max_chunk_size: 每块的最大字符数
|
||
|
||
Returns:
|
||
List of (chunk_text, offset) 元组，offset 是该块在原文中的起始位置
|
||
"""
|
||
# 按句子分割
|
||
sentences = []
|
||
current = ""
|
||
for char in text:
|
||
current += char
|
||
if char in '。！？\n':
|
||
sentences.append(current)
|
||
current = ""
|
||
|
||
# 添加最后一个不完整的句子
|
||
if current:
|
||
sentences.append(current)
|
||
|
||
# 分块
|
||
chunks = []
|
||
current_chunk = []
|
||
current_length = 0
|
||
current_offset = 0
|
||
|
||
for sentence in sentences:
|
||
sentence_len = len(sentence)
|
||
if current_length + sentence_len > max_chunk_size:
|
||
if current_chunk:
|
||
chunk_text = ''.join(current_chunk)
|
||
chunks.append((chunk_text, current_offset))
|
||
current_offset += len(chunk_text)
|
||
current_chunk = [sentence]
|
||
current_length = sentence_len
|
||
else:
|
||
current_chunk.append(sentence)
|
||
current_length += sentence_len
|
||
|
||
# 添加最后一个块
|
||
if current_chunk:
|
||
chunk_text = ''.join(current_chunk)
|
||
chunks.append((chunk_text, current_offset))
|
||
|
||
return chunks
|
||
|
||
def _merge_segment_results(
|
||
self,
|
||
chunk_results: List[tuple[List[VideoSegment], int]]
|
||
) -> List[VideoSegment]:
|
||
"""合并分块的分段结果
|
||
|
||
Args:
|
||
chunk_results: List of (segments, offset) 元组
|
||
|
||
Returns:
|
||
合并后的分段列表，修正 start_index 和 end_index 为相对于原文的位置
|
||
"""
|
||
all_segments = []
|
||
|
||
for segments, offset in chunk_results:
|
||
for seg in segments:
|
||
# 修正索引为相对于原文的位置
|
||
corrected_seg = VideoSegment(
|
||
title=seg.title,
|
||
content=seg.content,
|
||
start_index=seg.start_index + offset,
|
||
end_index=seg.end_index + offset
|
||
)
|
||
all_segments.append(corrected_seg)
|
||
|
||
return all_segments
|
||
|
||
def _fallback_chunk(self, chunk_text: str, offset: int) -> List[VideoSegment]:
|
||
"""对单个分块使用规则分段
|
||
|
||
Args:
|
||
chunk_text: 分块文本
|
||
offset: 该块在原文中的起始位置
|
||
|
||
Returns:
|
||
VideoSegment 对象列表
|
||
"""
|
||
# 简单实现：将整个块作为一个分段
|
||
return [
|
||
VideoSegment(
|
||
title=None,
|
||
content=chunk_text,
|
||
start_index=offset,
|
||
end_index=offset + len(chunk_text)
|
||
)
|
||
]
|
||
|
||
|
||
# ============================================================================
|
||
# Pydantic Models
|
||
# ============================================================================
|
||
|
||
|
||
def extract_json_from_llm_response(response: str) -> dict:
|
||
"""从 LLM 响应中提取 JSON
|
||
|
||
处理以下情况：
|
||
- 纯 JSON
|
||
- 包裹在 ```json ``` 中的 JSON
|
||
- 前后包含额外说明文本的 JSON
|
||
|
||
Args:
|
||
response: LLM 响应文本
|
||
|
||
Returns:
|
||
解析后的 JSON 字典
|
||
|
||
Raises:
|
||
ValueError: 无法提取有效 JSON
|
||
"""
|
||
# 尝试直接解析
|
||
try:
|
||
return json.loads(response.strip())
|
||
except json.JSONDecodeError:
|
||
pass
|
||
|
||
# 尝试提取 ```json ``` 代码块
|
||
json_match = re.search(r'```json\s*(.*?)\s*```', response, re.DOTALL)
|
||
if json_match:
|
||
try:
|
||
return json.loads(json_match.group(1))
|
||
except json.JSONDecodeError:
|
||
pass
|
||
|
||
# 尝试提取任何 JSON 对象
|
||
json_match = re.search(r'\{.*\}', response, re.DOTALL)
|
||
if json_match:
|
||
try:
|
||
return json.loads(json_match.group())
|
||
except json.JSONDecodeError as e:
|
||
raise ValueError(f"无法从 LLM 响应中提取有效 JSON: {e}")
|
||
|
||
raise ValueError("LLM 响应中未找到 JSON 内容")
|
||
|
||
class VideoSegment(BaseModel):
|
||
"""视频分段
|
||
|
||
Attributes:
|
||
title: 分段标题（可选）
|
||
content: 分段内容（必填）
|
||
start_index: 起始字幕索引
|
||
end_index: 结束字幕索引
|
||
"""
|
||
title: Optional[str] = Field(
|
||
None,
|
||
description="分段标题，简洁描述型（5-15字）"
|
||
)
|
||
content: str = Field(
|
||
...,
|
||
description="分段内容，合并后的字幕文本"
|
||
)
|
||
start_index: int = Field(
|
||
...,
|
||
ge=0,
|
||
description="在原文中的起始字符位置"
|
||
)
|
||
end_index: int = Field(
|
||
...,
|
||
ge=0,
|
||
description="在原文中的结束字符位置"
|
||
)
|
||
|
||
@field_validator('title')
|
||
@classmethod
|
||
def validate_title_length(cls, v: Optional[str]) -> Optional[str]:
|
||
"""验证标题长度（5-15字）"""
|
||
if v is not None and len(v) > 15:
|
||
raise ValueError(f"标题长度不能超过15字，当前为{len(v)}字")
|
||
return v
|
||
|
||
model_config = {
|
||
"json_schema_extra": {
|
||
"examples": [
|
||
{
|
||
"title": "机器学习概述",
|
||
"content": "机器学习是人工智能的一个分支...",
|
||
"start_index": 1,
|
||
"end_index": 10
|
||
}
|
||
]
|
||
}
|
||
}
|
||
|
||
|
||
class SegmentationResult(BaseModel):
|
||
"""分段结果
|
||
|
||
Attributes:
|
||
video_type: 视频类型（知识类/故事类/访谈类/新闻类/vlog 等）
|
||
segments: 分段列表
|
||
actual_segment_count: 实际分段数量
|
||
"""
|
||
video_type: str = Field(
|
||
...,
|
||
description="识别出的视频类型"
|
||
)
|
||
segments: List[VideoSegment] = Field(
|
||
default_factory=list,
|
||
description="分段列表"
|
||
)
|
||
actual_segment_count: int = Field(
|
||
...,
|
||
ge=0,
|
||
description="实际分段数量"
|
||
)
|
||
|
||
model_config = {
|
||
"json_schema_extra": {
|
||
"examples": [
|
||
{
|
||
"video_type": "知识类",
|
||
"segments": [
|
||
{
|
||
"title": "机器学习概述",
|
||
"content": "...",
|
||
"start_index": 1,
|
||
"end_index": 10
|
||
}
|
||
],
|
||
"actual_segment_count": 5
|
||
}
|
||
]
|
||
}
|
||
}
|
||
|
||
|
||
# ============================================================================
|
||
# Video Type Detection
|
||
# ============================================================================
|
||
|
||
# 视频类型关键词映射
|
||
VIDEO_TYPE_KEYWORDS = {
|
||
"知识类": [
|
||
"教程", "课程", "教学", "入门", "基础", "进阶", "实战",
|
||
"编程", "开发", "技术", "学习", "教育", "培训", "讲座"
|
||
],
|
||
"故事类": [
|
||
"影视", "电影", "电视剧", "动漫", "动画", "小说", "故事",
|
||
"剧情", "短片", "纪录片"
|
||
],
|
||
"访谈类": [
|
||
"访谈", "对话", "采访", "专访", "聊天", "脱口秀", "播客"
|
||
],
|
||
"新闻类": [
|
||
"新闻", "报道", "资讯", "时事", "热点", "快讯"
|
||
],
|
||
"vlog": [
|
||
"vlog", "日常", "生活", "旅行", "美食", "探店",
|
||
"分享", "体验"
|
||
]
|
||
}
|
||
|
||
# 默认视频类型
|
||
DEFAULT_VIDEO_TYPE = "知识类"
|
||
|
||
|
||
# ============================================================================
|
||
# Semantic Segmentation Engine
|
||
# ============================================================================
|
||
|
||
class SemanticSegmentationEngine:
|
||
"""语义分段引擎（使用策略架构）
|
||
|
||
使用策略模式实现可扩展的分段架构。
|
||
|
||
Attributes:
|
||
strategy: 分段策略（实现 SegmentationStrategy 接口）
|
||
|
||
Examples:
|
||
>>> # 使用默认统一策略
|
||
>>> engine = SemanticSegmentationEngine()
|
||
>>> paragraphs = engine.segment(segments, metadata)
|
||
|
||
>>> # 使用自定义策略（未来扩展）
|
||
>>> custom_strategy = KnowledgeTypeStrategy()
|
||
>>> engine = SemanticSegmentationEngine(strategy=custom_strategy)
|
||
>>> paragraphs = engine.segment(segments, metadata)
|
||
|
||
Note:
|
||
Plan 13-06: 实现可扩展策略架构，Phase 13 使用统一策略。
|
||
"""
|
||
|
||
def __init__(
|
||
self,
|
||
strategy: Optional[SegmentationStrategy] = None,
|
||
fallback_segmenter: Optional['SemanticSegmenter'] = None,
|
||
cache: Optional['SemanticCache'] = None
|
||
):
|
||
"""初始化语义分段引擎
|
||
|
||
Args:
|
||
strategy: 分段策略实例，如果为 None 则使用统一策略
|
||
fallback_segmenter: 降级时使用的规则分段器（已弃用，保留兼容性）
|
||
cache: 缓存管理器
|
||
"""
|
||
# 默认使用统一策略
|
||
if strategy is None:
|
||
self.strategy = UnifiedSegmentationStrategy(
|
||
fallback_segmenter=fallback_segmenter,
|
||
cache=cache
|
||
)
|
||
else:
|
||
self.strategy = strategy
|
||
|
||
logger.debug(
|
||
f"SemanticSegmentationEngine initialized with strategy: "
|
||
f"{self.strategy.__class__.__name__}"
|
||
)
|
||
|
||
def _infer_video_type(self, metadata: dict) -> str:
|
||
"""从元数据推断视频类型（代理到策略）
|
||
|
||
Args:
|
||
metadata: 视频元数据
|
||
|
||
Returns:
|
||
推断出的视频类型
|
||
"""
|
||
# 如果策略有 _infer_video_type 方法，使用它
|
||
if hasattr(self.strategy, '_infer_video_type'):
|
||
return self.strategy._infer_video_type(metadata)
|
||
# 否则返回默认类型
|
||
return DEFAULT_VIDEO_TYPE
|
||
|
||
def _build_prompt(self, content: str, metadata: dict) -> str:
|
||
"""构建 Prompt（代理到策略）
|
||
|
||
Args:
|
||
content: 字幕内容
|
||
metadata: 视频元数据
|
||
|
||
Returns:
|
||
构建好的 Prompt
|
||
"""
|
||
# 如果策略有 _build_prompt 方法，使用它
|
||
if hasattr(self.strategy, '_build_prompt'):
|
||
return self.strategy._build_prompt(content, metadata)
|
||
# 否则返回基本 prompt
|
||
return f"Content: {content}\nMetadata: {metadata}"
|
||
|
||
def segment(
|
||
self,
|
||
segments: List,
|
||
metadata: dict
|
||
) -> List['VideoSegment']:
|
||
"""对字幕分段进行语义分段
|
||
|
||
使用当前配置的策略进行分段。
|
||
|
||
Args:
|
||
segments: 字幕分段列表（来自 parser.py 的 Segment 对象）
|
||
metadata: 视频元数据（title, duration 等）
|
||
|
||
Returns:
|
||
VideoSegment 对象列表（包含标题和内容）
|
||
"""
|
||
return self.strategy.segment(segments, metadata)
|