参数配置统一

backend/rag_core/retriever_factory.py

@@ -1,38 +1,46 @@
-# rag_core/retriever_factory.py 
+# 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 langchain_core.stores import BaseStore
 
 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,
+    parent_splitter: TextSplitter | None = None,
+    child_splitter: TextSplitter | None = None,
+    docstore: BaseStore | None = 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:
+    """
+    创建 ParentDocumentRetriever 实例。
+
+    Args:
+        collection_name: Qdrant 集合名称，默认 "rag_documents"
+        parent_splitter: 父文档切分器，默认 None（使用默认参数创建）
+        child_splitter: 子文档切分器，默认 None（使用默认参数创建）
+        docstore: 文档存储实例，默认 None（使用默认参数创建）
+        search_k: 检索时返回的结果数，默认 5
+        parent_chunk_size: 父文档块大小，默认 1000
+        parent_chunk_overlap: 父文档块重叠大小，默认 100
+        child_chunk_size: 子文档块大小，默认 200
+        child_chunk_overlap: 子文档块重叠大小，默认 20
+
+    Returns:
+        ParentDocumentRetriever 实例
+    """
     # 嵌入模型
-    if embeddings is None:
-        embedder = LlamaCppEmbedder()
-        embeddings = embedder.as_langchain_embeddings()
+    embedder = LlamaCppEmbedder()
+    embeddings = embedder.as_langchain_embeddings()
 
     # 向量存储（只读）
-    vector_store = QdrantVectorStore(
-        collection_name=collection_name,
-        embeddings=embeddings,
-    )
+    vector_store = QdrantVectorStore(collection_name=collection_name)
 
     # 切分器（若未提供则创建默认）
     if parent_splitter is None:
@@ -48,7 +56,7 @@ def create_parent_retriever(
 
     # 文档存储
     if docstore is None:
-        docstore, _ = create_docstore()  # 从环境变量读取连接
+        docstore, _ = create_docstore()
 
     return ParentDocumentRetriever(
         vectorstore=vector_store.get_langchain_vectorstore(),
@@ -56,4 +64,4 @@ def create_parent_retriever(
         child_splitter=child_splitter,
         parent_splitter=parent_splitter,
         search_kwargs={"k": search_k},
-    )
+    )