langchain[patch]: Add async methods to EmbeddingRouterChain (#19603)

libs/langchain/langchain/chains/router/embedding_router.py

@@ -2,7 +2,10 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional, Sequence, Tuple, Type
 
-from langchain_core.callbacks import CallbackManagerForChainRun
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForChainRun,
+    CallbackManagerForChainRun,
+)
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import Extra
@@ -40,6 +43,15 @@ class EmbeddingRouterChain(RouterChain):
         results = self.vectorstore.similarity_search(_input, k=1)
         return {"next_inputs": inputs, "destination": results[0].metadata["name"]}
 
+    async def _acall(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        _input = ", ".join([inputs[k] for k in self.routing_keys])
+        results = await self.vectorstore.asimilarity_search(_input, k=1)
+        return {"next_inputs": inputs, "destination": results[0].metadata["name"]}
+
     @classmethod
     def from_names_and_descriptions(
         cls,
@@ -57,3 +69,21 @@ class EmbeddingRouterChain(RouterChain):
                 )
         vectorstore = vectorstore_cls.from_documents(documents, embeddings)
         return cls(vectorstore=vectorstore, **kwargs)
+
+    @classmethod
+    async def afrom_names_and_descriptions(
+        cls,
+        names_and_descriptions: Sequence[Tuple[str, Sequence[str]]],
+        vectorstore_cls: Type[VectorStore],
+        embeddings: Embeddings,
+        **kwargs: Any,
+    ) -> EmbeddingRouterChain:
+        """Convenience constructor."""
+        documents = []
+        for name, descriptions in names_and_descriptions:
+            for description in descriptions:
+                documents.append(
+                    Document(page_content=description, metadata={"name": name})
+                )
+        vectorstore = await vectorstore_cls.afrom_documents(documents, embeddings)
+        return cls(vectorstore=vectorstore, **kwargs)