feat(12-01): implement SemanticSegmenter for subtitle segmentation

- Merge short segments into paragraphs
- Split on time gaps (>3 seconds by default)
- Enforce max paragraph length (500 chars by default)
- Simple rule-based approach (no LLM needed)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

kb_workflow/srt/segmenter.py

@@ -0,0 +1,97 @@
+"""Semantic Segmenter Module
+
+将字幕分段按语义合并为段落。基于简单启发式规则：
+1. 时间间隔超过阈值时分段
+2. 段落长度限制
+
+不使用 LLM 进行分段判断，保持简单和快速。
+"""
+
+from __future__ import annotations
+
+from typing import List
+
+from kb_workflow.srt.parser import Segment
+
+
+class SemanticSegmenter:
+    """语义分段器
+
+    将字幕分段转换为语义段落。基于时间和长度规则：
+    - 长时间停顿（>gap_threshold 秒）作为分段点
+    - 段落长度限制（max_paragraph_chars 字符）
+
+    Attributes:
+        gap_threshold: 时间间隔阈值（秒），默认 3.0
+        max_paragraph_chars: 单段落最大字符数，默认 500
+    """
+
+    def __init__(
+        self,
+        gap_threshold: float = 3.0,
+        max_paragraph_chars: int = 500
+    ):
+        """初始化分段器
+
+        Args:
+            gap_threshold: 时间间隔阈值（秒），超过此值时分段
+            max_paragraph_chars: 单段落最大字符数
+        """
+        self.gap_threshold = gap_threshold
+        self.max_paragraph_chars = max_paragraph_chars
+
+    def segment(self, segments: List[Segment]) -> List[str]:
+        """将字幕分段转换为语义段落
+
+        Args:
+            segments: 字幕分段列表
+
+        Returns:
+            段落文本列表（已去除时间戳）
+
+        Example:
+            >>> segmenter = SemanticSegmenter()
+            >>> segments = [Segment(1, 0, 5, "第一句"), Segment(2, 5, 10, "第二句")]
+            >>> paragraphs = segmenter.segment(segments)
+            >>> len(paragraphs)
+            1
+        """
+        if not segments:
+            return []
+
+        paragraphs = []
+        current_paragraph = []
+        current_length = 0
+        last_end_time = None
+
+        for seg in segments:
+            # 检查是否需要分段
+            should_start_new = False
+
+            # 规则1: 时间间隔超过阈值
+            if last_end_time is not None:
+                gap = seg.start_time - last_end_time
+                if gap > self.gap_threshold:
+                    should_start_new = True
+
+            # 规则2: 段落长度超过限制
+            if current_length + len(seg.text) > self.max_paragraph_chars:
+                if current_paragraph:  # 只有当前段落有内容时才分段
+                    should_start_new = True
+
+            # 执行分段
+            if should_start_new and current_paragraph:
+                paragraphs.append(' '.join(current_paragraph))
+                current_paragraph = []
+                current_length = 0
+
+            # 添加当前分段
+            current_paragraph.append(seg.text)
+            current_length += len(seg.text)
+            last_end_time = seg.end_time
+
+        # 添加最后一个段落
+        if current_paragraph:
+            paragraphs.append(' '.join(current_paragraph))
+
+        return paragraphs