langchain[patch], docs[patch]: use byte store in multivectorretriever (#14474)

libs/langchain/langchain/retrievers/multi_vector.py

@@ -1,8 +1,8 @@
 from enum import Enum
-from typing import Any, List, Optional
+from typing import Dict, List, Optional
 
 from langchain_core.documents import Document
-from langchain_core.pydantic_v1 import Field, validator
+from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.retrievers import BaseRetriever
 from langchain_core.stores import BaseStore, ByteStore
 from langchain_core.vectorstores import VectorStore
@@ -26,7 +26,7 @@ class MultiVectorRetriever(BaseRetriever):
     vectorstore: VectorStore
     """The underlying vectorstore to use to store small chunks
     and their embedding vectors"""
-    byte_store: Optional[ByteStore]
+    byte_store: Optional[ByteStore] = None
     """The lower-level backing storage layer for the parent documents"""
     docstore: BaseStore[str, Document]
     """The storage interface for the parent documents"""
@@ -36,16 +36,16 @@ class MultiVectorRetriever(BaseRetriever):
     search_type: SearchType = SearchType.similarity
     """Type of search to perform (similarity / mmr)"""
 
-    @validator("docstore", pre=True, always=True)
-    def shim_docstore(
-        cls, docstore: Optional[BaseStore[str, Document]], values: Any
-    ) -> BaseStore[str, Document]:
+    @root_validator(pre=True)
+    def shim_docstore(cls, values: Dict) -> Dict:
         byte_store = values.get("byte_store")
+        docstore = values.get("docstore")
         if byte_store is not None:
             docstore = create_kv_docstore(byte_store)
         elif docstore is None:
             raise Exception("You must pass a `byte_store` parameter.")
-        return docstore
+        values["docstore"] = docstore
+        return values
 
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun