重构代码，统一config配置

backend/rag_core/retriever_factory.py

@@ -0,0 +1,59 @@
+# rag_core/retriever_factory.py 
+from langchain_core.embeddings import Embeddings
+from langchain_classic.retrievers import ParentDocumentRetriever
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from typing import Optional
+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},
+    )