重排，多路查询

rag_core/__init__.py

@@ -7,6 +7,8 @@ RAG Core - 公共 RAG 组件包
 from .embedders import LlamaCppEmbedder
 from .vector_store import QdrantVectorStore, QDRANT_URL, QDRANT_API_KEY
 from .store import PostgresDocStore, create_docstore
+from .retriever_factory import create_parent_retriever
+
 
 __all__ = [
     "LlamaCppEmbedder",
@@ -15,4 +17,5 @@ __all__ = [
     "QDRANT_API_KEY",
     "PostgresDocStore",
     "create_docstore",
+    "create_parent_retriever",
 ]

rag_core/client.py

@@ -0,0 +1,24 @@
+# rag_core/client.py
+import os
+from typing import Optional
+from qdrant_client import QdrantClient
+
+QDRANT_URL = os.getenv("QDRANT_URL", "http://127.0.0.1:6333")
+QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
+
+def create_qdrant_client(
+    url: Optional[str] = None,
+    api_key: Optional[str] = None,
+    timeout: int = 120,  # 索引构建需要较长超时
+) -> QdrantClient:
+    effective_url = url or QDRANT_URL
+    effective_api_key = api_key or QDRANT_API_KEY
+    
+    if not effective_url:
+        raise ValueError("Qdrant URL 未配置")
+    
+    client_kwargs = {"url": effective_url, "timeout": timeout}
+    if effective_api_key:
+        client_kwargs["api_key"] = effective_api_key
+    
+    return QdrantClient(**client_kwargs)

rag_core/retriever_factory.py

@@ -0,0 +1,67 @@
+# rag_core/retriever_factory.py 
+from langchain_core.embeddings import Embeddings
+from langchain_classic.retrievers import ParentDocumentRetriever
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from rag_indexer.splitters import SplitterType, get_splitter
+import asyncio
+import logging
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import List, Union, Optional, Any, Dict, Tuple
+from httpx import RemoteProtocolError
+from langchain_core.documents import Document
+from langchain_core.embeddings import Embeddings
+from langchain_core.stores import BaseStore
+from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
+from langchain_classic.retrievers import ParentDocumentRetriever
+
+from rag_core import LlamaCppEmbedder, QdrantVectorStore, create_docstore
+
+
+def create_parent_retriever(
+    collection_name: str = "rag_documents",
+    embeddings: Optional[Embeddings] = None,
+    parent_splitter: Optional[TextSplitter] = None,
+    child_splitter: Optional[TextSplitter] = None,
+    docstore: Optional[BaseStore] = None,
+    search_k: int = 5,
+    # 若未传入切分器，则用以下参数创建默认切分器
+    parent_chunk_size: int = 1000,
+    parent_chunk_overlap: int = 100,
+    child_chunk_size: int = 200,
+    child_chunk_overlap: int = 20,
+) -> ParentDocumentRetriever:
+    # 嵌入模型
+    if embeddings is None:
+        embedder = LlamaCppEmbedder()
+        embeddings = embedder.as_langchain_embeddings()
+
+    # 向量存储（只读）
+    vector_store = QdrantVectorStore(
+        collection_name=collection_name,
+        embeddings=embeddings,
+    )
+
+    # 切分器（若未提供则创建默认）
+    if parent_splitter is None:
+        parent_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=parent_chunk_size,
+            chunk_overlap=parent_chunk_overlap,
+        )
+    if child_splitter is None:
+        child_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=child_chunk_size,
+            chunk_overlap=child_chunk_overlap,
+        )
+
+    # 文档存储
+    if docstore is None:
+        docstore, _ = create_docstore()  # 从环境变量读取连接
+
+    return ParentDocumentRetriever(
+        vectorstore=vector_store.get_langchain_vectorstore(),
+        docstore=docstore,
+        child_splitter=child_splitter,
+        parent_splitter=parent_splitter,
+        search_kwargs={"k": search_k},
+    )

rag_core/vector_store.py

@@ -10,6 +10,7 @@ from langchain_core.documents import Document
 from langchain_qdrant import QdrantVectorStore as LangchainQdrantVS
 from qdrant_client import QdrantClient
 from qdrant_client.http.models import Distance, VectorParams
+from .client import create_qdrant_client
 
 logger = logging.getLogger(__name__)
 
@@ -44,14 +45,8 @@ class QdrantVectorStore:
         )
 
     def get_client(self) -> QdrantClient:
-        """懒加载客户端，每次获取时确保连接可用。"""
         if self._client is None:
-            self._client = QdrantClient(
-                url=QDRANT_URL,
-                api_key=QDRANT_API_KEY,
-                timeout=120,
-                http2=False,
-            )
+            self._client = create_qdrant_client(timeout=120)
         return self._client
 
     def refresh_client(self):