🚀 完全实现 Qdrant 混合检索功能
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构建并部署 AI Agent 服务 / deploy (push) Successful in 11m7s
- 不需要兼容,完全重写为混合检索 - 检索器:强制使用 FastEmbedSparse + RetrievalMode.HYBRID - 索引器:强制启用稀疏向量,混合检索模式 - 添加 fastembed 依赖到 requirements.txt - 语法检查通过
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@@ -2,6 +2,7 @@
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离线 RAG 索引构建核心流水线。
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使用 LangChain 的 ParentDocumentRetriever 实现父子块策略。
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支持 Qdrant 混合检索(Dense + Sparse)。
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"""
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import asyncio
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@@ -20,6 +21,8 @@ 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 qdrant_client.http.exceptions import ResponseHandlingException
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from qdrant_client import QdrantClient
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from qdrant_client.http.models import SparseVectorParams
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from .loaders import DocumentLoader
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from .splitters import SplitterType, get_splitter
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@@ -71,13 +74,10 @@ class IndexBuilderConfig:
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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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# 混合检索支持(默认 False,完全兼容)
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enable_sparse: bool = False
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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。"""
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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, embeddings: Optional[Embeddings] = None, **kwargs):
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"""
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@@ -118,28 +118,19 @@ class IndexBuilder:
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self.embedder = LlamaCppEmbedder()
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self.embeddings = self.embedder.as_langchain_embeddings()
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# 初始化向量存储
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# 默认 enable_sparse=False,完全兼容现有代码
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# 若需要启用混合检索,请先安装 fastembed,然后设置 enable_sparse=True
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qdrant_kwargs = {
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"collection_name": config.collection_name,
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}
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if self.config.enable_sparse:
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try:
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from langchain_qdrant import FastEmbedSparse, RetrievalMode
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qdrant_kwargs["sparse_embedding"] = FastEmbedSparse(model_name="Qdrant/bm25")
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qdrant_kwargs["retrieval_mode"] = RetrievalMode.HYBRID
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logger.info("✅ 稀疏向量支持已启用")
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except ImportError:
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logger.warning("⚠️ fastembed 未安装,无法启用稀疏向量,继续使用纯稠密")
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except Exception as e:
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logger.warning(f"⚠️ 稀疏向量初始化失败: {e},继续使用纯稠密")
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if self.embedder is None:
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qdrant_kwargs["embedding"] = self.embeddings
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self.vector_store = QdrantVectorStore(**qdrant_kwargs)
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# 初始化稀疏嵌入
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from langchain_qdrant import FastEmbedSparse, RetrievalMode
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self.sparse_embeddings = FastEmbedSparse(model_name="Qdrant/bm25")
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logger.info("✅ FastEmbedSparse 初始化成功")
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# 初始化向量存储(混合检索模式)
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self.vector_store = QdrantVectorStore(
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collection_name=config.collection_name,
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embedding=self.embeddings if self.embedder is None else None,
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sparse_embedding=self.sparse_embeddings,
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retrieval_mode=RetrievalMode.HYBRID,
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)
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logger.info("✅ 混合检索向量存储初始化成功")
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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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@@ -222,9 +213,7 @@ class IndexBuilder:
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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(
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self, directory_path: Union[str, Path], recursive: bool = True
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) -> int:
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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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@@ -243,8 +232,8 @@ class IndexBuilder:
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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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"""单一模式:切分后直接写入向量库。"""
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chunks = self.splitter.split_documents(documents) # type: ignore[union-attr]
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"""单一切分模式:切分后直接写入向量库。"""
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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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@@ -252,7 +241,7 @@ class IndexBuilder:
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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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"""父子模式:使用 ParentDocumentRetriever 批量添加。"""
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"""父子块模式:使用 ParentDocumentRetriever 批量添加。"""
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self.vector_store.create_collection()
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assert self.retriever is not None
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@@ -261,7 +250,7 @@ class IndexBuilder:
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processed = 0
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for i in range(0, total, batch_size):
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batch = documents[i:i + batch_size]
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batch = documents[i:i+batch_size]
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await self._add_batch_with_retry(batch, i // batch_size + 1)
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processed += len(batch)
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logger.info("批次 %d: 已处理 %d/%d", i // batch_size + 1, processed, total)
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@@ -275,7 +264,7 @@ class IndexBuilder:
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base_delay = 2
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for attempt in range(max_retries):
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try:
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await self.retriever.aadd_documents(batch) # type: ignore[union-attr]
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await self.retriever.aadd_documents(batch)
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logger.info("批次 %d 成功添加 %d 个文档", batch_no, len(batch))
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return
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except (RemoteProtocolError, ConnectionError, OSError, ResponseHandlingException) as e:
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@@ -300,17 +289,17 @@ class IndexBuilder:
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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 # type: ignore[return-value]
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return self.splitter # type: ignore[return-value]
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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(仅父子模式可用)。"""
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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 # type: ignore[return-value]
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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(仅父子模式可用)。"""
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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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@@ -325,17 +314,17 @@ class IndexBuilder:
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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()) # type: ignore[attr-defined]
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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()) # type: ignore[attr-defined]
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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() # type: ignore[attr-defined]
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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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@@ -350,4 +339,4 @@ class IndexBuilder:
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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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return False
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