Add unit tests

libs/langchain/langchain/retrievers/multi_vector.py

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import List, Optional, Any
+from typing import Any, List, Optional
 
 from langchain_core.documents import Document
 from langchain_core.pydantic_v1 import Field, validator
@@ -38,7 +38,7 @@ class MultiVectorRetriever(BaseRetriever):
 
     @validator("docstore", pre=True, always=True)
     def shim_docstore(
-        cls, docstore: Optional[BaseStore[str, Document]], values: any
+        cls, docstore: Optional[BaseStore[str, Document]], values: Any
     ) -> BaseStore[str, Document]:
         base_store = values.get("base_store")
         if base_store is not None:

libs/langchain/tests/unit_tests/retrievers/test_multi_vector.py

@@ -0,0 +1,30 @@
+from typing import Any, List
+
+from langchain_core.documents import Document
+
+from langchain.retrievers.multi_vector import MultiVectorRetriever
+from langchain.storage import InMemoryStore
+from tests.unit_tests.indexes.test_indexing import InMemoryVectorStore
+
+
+class InMemoryVectorstoreWithSearch(InMemoryVectorStore):
+    def similarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Document]:
+        res = self.store.get(query)
+        if res is None:
+            return []
+        return [res]
+
+
+def test_multi_vector_retriever() -> None:
+    vectorstore = InMemoryVectorstoreWithSearch()
+    retriever = MultiVectorRetriever(
+        vectorstore=vectorstore, docstore=InMemoryStore(), doc_id="doc_id"
+    )
+    documents = [Document(page_content="test document", metadata={"doc_id": "1"})]
+    retriever.vectorstore.add_documents(documents, ids=["1"])
+    retriever.docstore.mset(list(zip(["1"], documents)))
+    results = retriever.invoke("1")
+    assert len(results) > 0
+    assert results[0].page_content == "test document"

libs/langchain/tests/unit_tests/retrievers/test_parent_document.py

@@ -0,0 +1,37 @@
+from typing import Any, List, Sequence
+
+from langchain_core.documents import Document
+
+from langchain.retrievers import ParentDocumentRetriever
+from langchain.storage import InMemoryStore
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from tests.unit_tests.indexes.test_indexing import InMemoryVectorStore
+
+
+class InMemoryVectorstoreWithSearch(InMemoryVectorStore):
+    def similarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Document]:
+        res = self.store.get(query)
+        if res is None:
+            return []
+        return [res]
+
+    def add_documents(self, documents: Sequence) -> None:
+        return super().add_documents(documents, ids=["1"])
+
+
+def test_parent_document_retriever() -> None:
+    vectorstore = InMemoryVectorstoreWithSearch()
+    store = InMemoryStore()
+    child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
+    documents = [Document(page_content="test document", metadata={"doc_id": "1"})]
+    retriever = ParentDocumentRetriever(
+        vectorstore=vectorstore,
+        docstore=store,
+        child_splitter=child_splitter,
+    )
+    retriever.add_documents(documents, ids=["1"])
+    results = retriever.invoke("1")
+    assert len(results) > 0
+    assert results[0].page_content == "test document"