langchain[patch]: Add async methods to MultiVectorRetriever (#16878)

Adds async support to multi vector retriever

libs/langchain/langchain/retrievers/multi_vector.py

@@ -1,7 +1,10 @@
 from enum import Enum
 from typing import Dict, List, Optional
 
-from langchain_core.callbacks import CallbackManagerForRetrieverRun
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForRetrieverRun,
+    CallbackManagerForRetrieverRun,
+)
 from langchain_core.documents import Document
 from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.retrievers import BaseRetriever
@@ -71,3 +74,30 @@ class MultiVectorRetriever(BaseRetriever):
                 ids.append(d.metadata[self.id_key])
         docs = self.docstore.mget(ids)
         return [d for d in docs if d is not None]
+
+    async def _aget_relevant_documents(
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        """Asynchronously get documents relevant to a query.
+        Args:
+            query: String to find relevant documents for
+            run_manager: The callbacks handler to use
+        Returns:
+            List of relevant documents
+        """
+        if self.search_type == SearchType.mmr:
+            sub_docs = await self.vectorstore.amax_marginal_relevance_search(
+                query, **self.search_kwargs
+            )
+        else:
+            sub_docs = await self.vectorstore.asimilarity_search(
+                query, **self.search_kwargs
+            )
+
+        # We do this to maintain the order of the ids that are returned
+        ids = []
+        for d in sub_docs:
+            if self.id_key in d.metadata and d.metadata[self.id_key] not in ids:
+                ids.append(d.metadata[self.id_key])
+        docs = await self.docstore.amget(ids)
+        return [d for d in docs if d is not None]

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

@@ -28,3 +28,16 @@ def test_multi_vector_retriever_initialization() -> None:
     results = retriever.invoke("1")
     assert len(results) > 0
     assert results[0].page_content == "test document"
+
+
+async def test_multi_vector_retriever_initialization_async() -> None:
+    vectorstore = InMemoryVectorstoreWithSearch()
+    retriever = MultiVectorRetriever(
+        vectorstore=vectorstore, docstore=InMemoryStore(), doc_id="doc_id"
+    )
+    documents = [Document(page_content="test document", metadata={"doc_id": "1"})]
+    await retriever.vectorstore.aadd_documents(documents, ids=["1"])
+    await retriever.docstore.amset(list(zip(["1"], documents)))
+    results = await retriever.ainvoke("1")
+    assert len(results) > 0
+    assert results[0].page_content == "test document"