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构建并部署 AI Agent 服务 / deploy (push) Failing after 6m34s
- 重写 rag_core/vector_store.py：完全异步实现 aadd_documents、asimilarity_search - 重写 app/rag/retriever.py：异步混合检索，移除同步兼容代码 - 修改 rag_indexer/index_builder.py：全链路异步调用 - 删除 rag_core/retriever_factory.py：不再使用 LangChain ParentDocumentRetriever - 清理冗余导入和代码：移除 model_services 兼容、不需要的异常导入 - 更新 rag_indexer/README.md：反映新架构 核心改进： - 完全异步化：索引构建和检索全链路 async/await - 自定义实现：不再依赖 LangChain 的 ParentDocumentRetriever - 双向量支持：子文档同时存储 dense + sparse 向量到 Qdrant - 架构清晰：rag_core 公共组件、rag_indexer 索引、app/rag 检索
312 lines
12 KiB
Python
312 lines
12 KiB
Python
"""
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离线 RAG 索引构建核心流水线。
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自定义实现父子块策略，支持 Qdrant 混合检索（Dense + Sparse）。
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"""
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import asyncio
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import logging
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import sys
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from pathlib import Path
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from dataclasses import dataclass, field
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from typing import List, Union, Optional, Any, Dict
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.stores import BaseStore
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from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
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from .loaders import DocumentLoader
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from .splitters import SplitterType, get_splitter
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from backend.rag_core import get_embeddings, QdrantHybridStore, create_docstore
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logger = logging.getLogger(__name__)
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# ---------- 配置数据类 ----------
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@dataclass
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class DocstoreConfig:
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"""文档存储配置（用于父文档存储）。"""
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pool_config: Dict[str, Any] | None = None
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max_concurrency: int | None = None
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# 若要从外部注入已创建好的 docstore，可直接设置此字段
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instance: BaseStore | None = None
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@dataclass
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class IndexBuilderConfig:
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"""索引构建器配置。"""
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collection_name: str = "rag_documents"
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splitter_type: SplitterType = SplitterType.PARENT_CHILD
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# 父块切分参数（仅当 splitter_type 为 PARENT_CHILD 时生效）
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parent_chunk_size: int = 1000
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parent_chunk_overlap: int = 100
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# 子块切分参数
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child_chunk_size: int = 200
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child_chunk_overlap: int = 20
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child_splitter_type: SplitterType = SplitterType.SEMANTIC # 子块默认语义切分
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# 检索参数
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search_k: int = 5
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# 文档存储配置（仅父子块模式需要）
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docstore: DocstoreConfig = field(default_factory=DocstoreConfig)
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# 其他切分器参数（当 splitter_type 非父子块时使用）
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extra_splitter_kwargs: Dict[str, Any] = field(default_factory=dict)
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# ---------- 索引构建器 ----------
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class IndexBuilder:
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"""RAG 索引构建主流水线，支持单块切分与父子块切分，支持混合检索。"""
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def __init__(self, config: Optional[IndexBuilderConfig] = None, **kwargs):
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"""
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Args:
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config: 索引构建器配置对象，优先级高于 kwargs
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**kwargs: 可直接传入配置参数，会合并到 config 中（为方便使用保留）
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"""
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if config is None:
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config = IndexBuilderConfig(**kwargs)
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elif kwargs:
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# 合并 kwargs 到 config 的字段（仅更新已有字段）
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for key, value in kwargs.items():
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if hasattr(config, key):
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setattr(config, key, value)
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self.config = config
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self._docstore_conn: Optional[str] = None # 用于记录由 create_docstore 创建的连接信息
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# 初始化基础组件
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self.loader = DocumentLoader()
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# 设置嵌入模型 - 完全使用服务内部提供
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self.embeddings = get_embeddings()
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logger.info("使用统一嵌入服务")
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# 初始化向量存储（自动支持稠密+稀疏混合检索）
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self.vector_store = QdrantHybridStore(
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collection_name=config.collection_name,
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embeddings=self.embeddings,
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)
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logger.info("✅ 混合检索向量存储初始化成功（稠密+BM25稀疏）")
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# 根据切分类型初始化相关组件
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self._init_splitters_and_retriever()
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# ---------- 私有初始化方法 ----------
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def _init_splitters_and_retriever(self) -> None:
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"""根据配置初始化切分器和检索器。"""
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if self.config.splitter_type == SplitterType.PARENT_CHILD:
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self._init_parent_child_mode()
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else:
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self._init_single_splitter_mode()
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def _init_single_splitter_mode(self) -> None:
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"""单一切分模式（递归或语义）。"""
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splitter_kwargs = self.config.extra_splitter_kwargs.copy()
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if self.config.splitter_type == SplitterType.SEMANTIC:
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splitter_kwargs["embeddings"] = self.embeddings
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self.splitter = get_splitter(self.config.splitter_type, **splitter_kwargs)
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self.retriever = None
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self.docstore = None
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logger.info("使用单一 %s 切分器", self.config.splitter_type.value)
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def _init_parent_child_mode(self) -> None:
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cfg = self.config
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# 父块切分器
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self.parent_splitter = RecursiveCharacterTextSplitter(
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chunk_size=cfg.parent_chunk_size,
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chunk_overlap=cfg.parent_chunk_overlap,
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)
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# 子块切分器
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if cfg.child_splitter_type == SplitterType.SEMANTIC:
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self.child_splitter = get_splitter(
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SplitterType.SEMANTIC,
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embeddings=self.embeddings,
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**cfg.extra_splitter_kwargs
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)
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else:
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self.child_splitter = RecursiveCharacterTextSplitter(
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chunk_size=cfg.child_chunk_size,
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chunk_overlap=cfg.child_chunk_overlap,
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)
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# 文档存储
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self.docstore = self._create_or_use_docstore()
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# 注意：不再使用 LangChain 的 ParentDocumentRetriever
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# 改为自定义实现，以支持稀疏向量
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self.retriever = None
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logger.info("父子文档模式初始化完成（使用自定义索引逻辑）")
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def _create_or_use_docstore(self) -> BaseStore:
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"""创建或获取文档存储实例。"""
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cfg = self.config.docstore
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if cfg.instance is not None:
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logger.debug("使用外部注入的文档存储")
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return cfg.instance
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# 使用 create_docstore 创建 PostgreSQL 存储
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docstore, conn_info = create_docstore(
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pool_config=cfg.pool_config,
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max_concurrency=cfg.max_concurrency,
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)
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self._docstore_conn = conn_info
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logger.info("文档存储已创建（PostgreSQL）")
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return docstore
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# ---------- 公共构建方法 ----------
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async def build_from_file(self, file_path: Union[str, Path]) -> int:
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"""从单个文件构建索引。"""
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logger.info("加载文件: %s", file_path)
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documents = self.loader.load_file(file_path)
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logger.info("已加载 %d 个文档", len(documents))
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return await self._process_documents(documents)
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async def build_from_directory(self, directory_path: Union[str, Path], recursive: bool = True) -> int:
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"""从目录递归构建索引。"""
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logger.info("加载目录: %s (递归=%s)", directory_path, recursive)
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documents = self.loader.load_directory(directory_path, recursive=recursive)
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logger.info("已从目录加载 %d 个文档", len(documents))
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return await self._process_documents(documents)
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async def _process_documents(self, documents: List[Document]) -> int:
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"""处理文档列表，分发给相应的索引逻辑。"""
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if not documents:
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logger.warning("没有文档需要处理")
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return 0
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if self.config.splitter_type == SplitterType.PARENT_CHILD:
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return await self._index_with_parent_child(documents)
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else:
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return await self._index_with_single_splitter(documents)
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async def _index_with_single_splitter(self, documents: List[Document]) -> int:
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"""单一切分模式：切分后直接写入向量库（异步）。"""
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chunks = self.splitter.split_documents(documents)
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logger.info("已切分为 %d 个块", len(chunks))
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self.vector_store.create_collection()
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await self.vector_store.aadd_documents(chunks)
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return len(chunks)
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async def _index_with_parent_child(self, documents: List[Document]) -> int:
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"""父子块模式：自定义实现，支持稠密+稀疏双向量。"""
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self.vector_store.create_collection()
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assert self.docstore is not None
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import uuid
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total_chunks = 0
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# 1. 切分父块
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parent_chunks = self.parent_splitter.split_documents(documents)
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logger.info("切分出 %d 个父块", len(parent_chunks))
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# 2. 为每个父块生成 UUID 并存储
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parent_docs_with_ids = []
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for parent_chunk in parent_chunks:
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parent_id = str(uuid.uuid4())
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parent_chunk.metadata["id"] = parent_id
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parent_chunk.metadata["is_parent"] = True
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parent_docs_with_ids.append((parent_id, parent_chunk))
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# 3. 父文档批量存入 PostgreSQL
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await self.docstore.amset(parent_docs_with_ids)
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logger.info("已存入 %d 个父文档到 PostgreSQL", len(parent_docs_with_ids))
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# 4. 切分子块并添加 parent_id
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all_child_chunks = []
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for parent_id, parent_chunk in parent_docs_with_ids:
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child_chunks = self.child_splitter.split_documents([parent_chunk])
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for child_chunk in child_chunks:
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child_chunk.metadata["parent_id"] = parent_id
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child_chunk.metadata["is_parent"] = False
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# 继承父文档的重要元数据
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child_chunk.metadata["source"] = parent_chunk.metadata.get("source")
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child_chunk.metadata["page"] = parent_chunk.metadata.get("page")
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child_chunk.metadata["file_path"] = parent_chunk.metadata.get("file_path")
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all_child_chunks.append(child_chunk)
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total_chunks = len(all_child_chunks)
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logger.info("切分出 %d 个子块", total_chunks)
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# 5. 子文档分批存入 Qdrant（双向量，异步）
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batch_size = 100
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for i in range(0, total_chunks, batch_size):
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batch = all_child_chunks[i:i+batch_size]
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await self.vector_store.aadd_documents(batch)
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logger.info("已向 Qdrant 存入子文档批次 %d/%d",
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i // batch_size + 1,
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(total_chunks + batch_size - 1) // batch_size)
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logger.info("父子文档索引完成：%d 父文档，%d 子文档",
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len(parent_docs_with_ids), total_chunks)
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return total_chunks
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async def _add_batch_with_retry(self, batch: List[Document], batch_no: int) -> None:
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"""这个方法不再使用，保留只是为了兼容（不再被调用）"""
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# 这个方法现在不需要了，因为我们重写了 _index_with_parent_child
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pass
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# ---------- 信息获取方法 ----------
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def get_collection_info(self) -> Any:
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"""获取向量库集合信息。"""
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return self.vector_store.get_collection_info()
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def get_child_splitter(self) -> TextSplitter:
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"""获取当前使用的子块切分器。"""
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if self.config.splitter_type == SplitterType.PARENT_CHILD:
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return self.child_splitter
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return self.splitter
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def get_parent_splitter(self) -> RecursiveCharacterTextSplitter:
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"""获取父块切分器（仅父子块模式可用）。"""
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if self.config.splitter_type != SplitterType.PARENT_CHILD:
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raise RuntimeError("父块切分器仅在父子块模式下可用")
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return self.parent_splitter
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def get_docstore(self) -> BaseStore:
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"""获取文档存储实例（仅父子块模式可用）。"""
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if self.config.splitter_type != SplitterType.PARENT_CHILD:
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raise RuntimeError("文档存储仅在父子块模式下可用")
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assert self.docstore is not None
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return self.docstore
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# ---------- 资源管理 ----------
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def close(self) -> None:
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"""关闭资源（同步版本，供上下文管理器使用）。"""
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if self.docstore is not None and hasattr(self.docstore, "aclose"):
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try:
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loop = asyncio.get_running_loop()
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except RuntimeError:
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# 无运行中的事件循环，创建临时循环
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loop = asyncio.new_event_loop()
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loop.run_until_complete(self.docstore.aclose())
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loop.close()
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else:
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# 已有运行中的循环，创建任务（用户自行等待）
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loop.create_task(self.docstore.aclose())
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logger.info("IndexBuilder 资源已关闭")
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async def aclose(self) -> None:
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"""异步关闭资源。"""
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if self.docstore is not None and hasattr(self.docstore, "aclose"):
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await self.docstore.aclose()
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logger.info("IndexBuilder 资源已异步关闭")
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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self.close()
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return False
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async def __aenter__(self):
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return self
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async def __aexit__(self, exc_type, exc_val, exc_tb):
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await self.aclose()
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return False
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