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@@ -35,6 +35,7 @@ def get_input_path() -> Path:
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return Path(sys.argv[1])
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# 默认测试路径（可按需修改）
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return Path("data/corpus/三国演义.txt")
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#return Path("data/user_docs/doublestory.txt")
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async def main():
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@@ -1,34 +0,0 @@
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#!/usr/bin/env python3
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"""
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删除 Qdrant 集合并重新索引
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"""
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import asyncio
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import os
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import sys
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from backend.rag_core import QdrantHybridStore
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async def delete_and_recreate():
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"""删除并重新创建集合"""
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print("="*70)
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print("删除旧集合并重新创建...")
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print("="*70)
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vs = QdrantHybridStore(collection_name="rag_documents")
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# 删除旧集合
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try:
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vs.delete_collection()
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print("✅ 旧集合已删除")
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except Exception as e:
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print(f"⚠️ 删除集合时出错（可能不存在）: {e}")
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# 重新创建
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vs.create_collection()
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print("✅ 新集合已创建")
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if __name__ == "__main__":
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asyncio.run(delete_and_recreate())
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@@ -17,10 +17,8 @@ class SplitterType(str, Enum):
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PARENT_CHILD = "parent_child"
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# ---------- 配置数据类，统一参数 ----------
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@dataclass
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class RecursiveSplitterConfig:
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"""递归字符切分器配置"""
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chunk_size: int = 500
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chunk_overlap: int = 50
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separators: List[str] = field(default_factory=lambda: ["\n\n", "\n", "。", "！", "？", " ", ""])
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@@ -30,33 +28,31 @@ class RecursiveSplitterConfig:
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@dataclass
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class SemanticSplitterConfig:
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"""语义切分器配置，仅包含 SemanticChunker 支持的参数。"""
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embeddings: Any
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buffer_size: int = 1
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add_start_index: bool = False
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breakpoint_threshold_type: str = "percentile"
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breakpoint_threshold_amount: Optional[float] = None
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breakpoint_threshold_amount: float = 0.6 # 非 None，切分更积极
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number_of_chunks: Optional[int] = None
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sentence_split_regex: str = r"(?<=[.?!。？！])\s+"
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sentence_split_regex: str = r"(?<=[。！？；.!?;])" # 中文友好
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min_chunk_size: int = 100
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@dataclass
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class ParentChildSplitterConfig:
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"""父子切分器配置"""
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embeddings: Any # 子块语义切分所需
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parent_chunk_size: int = 1000
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parent_chunk_overlap: int = 100
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child_buffer_size: int = 1
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child_breakpoint_threshold_type: str = "percentile"
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child_breakpoint_threshold_amount: Optional[float] = None
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child_min_chunk_size: int = 100
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child_max_chunk_size: Optional[int] = 200
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embeddings: Any
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# 语义切分（用于父块）
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semantic_threshold_type: str = "percentile"
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semantic_threshold_amount: float = 0.6
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semantic_buffer_size: int = 1
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semantic_min_chunk_size: int = 100
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# 子块（递归字符切分）
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child_chunk_size: int = 400
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child_chunk_overlap: int = 50
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# ---------- 适配器：让 SemanticChunker 实现 TextSplitter 接口 ----------
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# ---------- 适配器 ----------
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class SemanticChunkerAdapter(TextSplitter):
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"""将 SemanticChunker 适配为 LangChain TextSplitter 接口。"""
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def __init__(self, config: SemanticSplitterConfig, **kwargs):
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super().__init__(**kwargs)
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self._config = config
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@@ -86,12 +82,8 @@ class SemanticChunkerAdapter(TextSplitter):
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return result
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# ---------- 工厂函数，统一创建切分器 ----------
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# ---------- 工厂函数 ----------
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def get_splitter(splitter_type: SplitterType, **kwargs) -> TextSplitter:
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"""
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根据类型创建切分器。
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支持传入配置对象或直接参数。
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"""
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if splitter_type == SplitterType.RECURSIVE:
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config = RecursiveSplitterConfig(
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chunk_size=kwargs.get("chunk_size", 500),
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@@ -114,98 +106,90 @@ def get_splitter(splitter_type: SplitterType, **kwargs) -> TextSplitter:
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if "config" in kwargs and isinstance(kwargs["config"], SemanticSplitterConfig):
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config = kwargs["config"]
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else:
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# 过滤出 SemanticSplitterConfig 支持的字段
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config_kwargs = {
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"embeddings": embeddings,
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"buffer_size": kwargs.get("buffer_size", 1),
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"breakpoint_threshold_type": kwargs.get("breakpoint_threshold_type", "percentile"),
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"breakpoint_threshold_amount": kwargs.get("breakpoint_threshold_amount"),
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"number_of_chunks": kwargs.get("number_of_chunks"),
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"min_chunk_size": kwargs.get("min_chunk_size", 100),
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}
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config = SemanticSplitterConfig(**config_kwargs)
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config = SemanticSplitterConfig(
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embeddings=embeddings,
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buffer_size=kwargs.get("buffer_size", 1),
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breakpoint_threshold_type=kwargs.get("breakpoint_threshold_type", "percentile"),
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breakpoint_threshold_amount=kwargs.get("breakpoint_threshold_amount", 0.6),
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number_of_chunks=kwargs.get("number_of_chunks"),
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min_chunk_size=kwargs.get("min_chunk_size", 100),
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)
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return SemanticChunkerAdapter(config)
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elif splitter_type == SplitterType.PARENT_CHILD:
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# 父子切分器在 builder 中单独处理，不通过本函数创建
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raise ValueError("父子切分器应通过 IndexBuilder 创建，不支持 get_splitter 直接构建")
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raise ValueError("父子切分器应通过 ParentChildSplitter 直接创建")
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else:
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raise ValueError(f"不支持的切分器类型: {splitter_type}")
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# ---------- 父子切分器实现 ----------
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# ---------- 父子切分器 ----------
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class ParentChildSplitter:
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"""
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将文档切分为父块（大块，用于上下文）和子块（小块，用于索引检索）。
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内部维护父子块之间的映射关系。
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切分流程：
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1. 语义切分 → 父块
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2. 递归字符切分 → 子块
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"""
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def __init__(self, config: ParentChildSplitterConfig):
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self.config = config
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# 父块使用递归字符切分
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self.parent_splitter = RecursiveCharacterTextSplitter(
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chunk_size=config.parent_chunk_size,
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chunk_overlap=config.parent_chunk_overlap,
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)
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# 子块使用语义切分
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# 语义切分（父块）
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semantic_config = SemanticSplitterConfig(
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embeddings=config.embeddings,
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buffer_size=config.child_buffer_size,
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breakpoint_threshold_type=config.child_breakpoint_threshold_type,
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breakpoint_threshold_amount=config.child_breakpoint_threshold_amount,
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min_chunk_size=config.child_min_chunk_size,
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buffer_size=config.semantic_buffer_size,
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breakpoint_threshold_type=config.semantic_threshold_type,
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breakpoint_threshold_amount=config.semantic_threshold_amount,
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min_chunk_size=config.semantic_min_chunk_size,
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)
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self.semantic_splitter = SemanticChunkerAdapter(semantic_config)
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# 递归字符切分（子块，大小由 child_chunk_size 控制）
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self.recursive_splitter = RecursiveCharacterTextSplitter(
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chunk_size=config.child_chunk_size,
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chunk_overlap=config.child_chunk_overlap,
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separators=["\n\n", "\n", "。", "！", "？", "；", "，", " ", ""]
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)
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self.child_splitter = SemanticChunkerAdapter(semantic_config)
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# 存储父子块映射关系（可选）
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self.parent_to_children: Dict[str, List[str]] = {}
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self.child_to_parent: Dict[str, str] = {}
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def split_documents(self, documents: List[Document]) -> Tuple[List[Document], List[Document]]:
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"""
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返回:
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(父块列表, 子块列表)
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同时填充内部映射字典。
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"""
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parent_chunks = self.parent_splitter.split_documents(documents)
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child_chunks = self.child_splitter.split_documents(documents)
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parent_chunks = []
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child_chunks = []
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# 建立映射关系（简化示例：根据文本包含关系粗略匹配，实际需更精确的算法）
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# 这里仅作示意，生产环境建议使用 embedding 相似度或精确子串定位
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self._build_mappings(parent_chunks, child_chunks)
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for doc in documents:
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# Step 1: 语义切分（父块）
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semantic_blocks = self.semantic_splitter.split_text(doc.page_content)
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for p_idx, semantic_block in enumerate(semantic_blocks):
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parent_id = f"parent_{len(parent_chunks)}"
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parent_doc = Document(
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page_content=semantic_block,
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metadata={**doc.metadata, "id": parent_id, "chunk_index": p_idx}
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)
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parent_chunks.append(parent_doc)
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# Step 2: 递归字符切分（子块）
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sub_chunks = self.recursive_splitter.split_text(semantic_block)
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for c_idx, sub_chunk in enumerate(sub_chunks):
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child_id = f"child_{len(child_chunks)}"
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child_doc = Document(
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page_content=sub_chunk,
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metadata={**doc.metadata, "id": child_id, "parent_id": parent_id, "child_index": c_idx}
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)
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child_chunks.append(child_doc)
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self.child_to_parent[child_id] = parent_id
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if parent_id not in self.parent_to_children:
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self.parent_to_children[parent_id] = []
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self.parent_to_children[parent_id].append(child_id)
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return parent_chunks, child_chunks
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def _build_mappings(self, parents: List[Document], children: List[Document]) -> None:
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"""
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根据文本内容建立父子映射。
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本方法为简化实现，实际使用时请替换为更可靠的匹配逻辑。
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"""
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self.parent_to_children.clear()
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self.child_to_parent.clear()
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# 为每个父块生成唯一 ID（若无则使用索引）
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for p_idx, parent in enumerate(parents):
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parent_id = parent.metadata.get("id", f"parent_{p_idx}")
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parent.metadata["id"] = parent_id
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self.parent_to_children[parent_id] = []
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# 将每个子块分配给包含其文本的第一个父块
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for c_idx, child in enumerate(children):
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child_id = child.metadata.get("id", f"child_{c_idx}")
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child.metadata["id"] = child_id
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for parent in parents:
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if child.page_content in parent.page_content:
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parent_id = parent.metadata["id"]
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self.parent_to_children[parent_id].append(child_id)
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self.child_to_parent[child_id] = parent_id
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child.metadata["parent_id"] = parent_id
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break
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def get_parent_for_child(self, child_id: str) -> Optional[str]:
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"""根据子块 ID 获取父块 ID"""
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return self.child_to_parent.get(child_id)
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def get_children_for_parent(self, parent_id: str) -> List[str]:
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"""根据父块 ID 获取所有子块 ID"""
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return self.parent_to_children.get(parent_id, [])
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return self.parent_to_children.get(parent_id, [])
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