Bedrock embeddings async methods (#9024)

## Description
This PR adds the `aembed_query` and `aembed_documents` async methods for
improving the embeddings generation for large documents. The
implementation uses asyncio tasks and gather to achieve concurrency as
there is no bedrock async API in boto3.

### Maintainers
@agola11 
@aarora79  

### Open questions
To avoid throttling from the Bedrock API, should there be an option to
limit the concurrency of the calls?

libs/langchain/langchain/embeddings/bedrock.py

@@ -1,5 +1,7 @@
+import asyncio
 import json
 import os
+from functools import partial
 from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Extra, root_validator
@@ -128,17 +130,11 @@ class BedrockEmbeddings(BaseModel, Embeddings):
         except Exception as e:
             raise ValueError(f"Error raised by inference endpoint: {e}")
 
-    def embed_documents(
-        self, texts: List[str], chunk_size: int = 1
-    ) -> List[List[float]]:
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Compute doc embeddings using a Bedrock model.
 
         Args:
-            texts: The list of texts to embed.
-            chunk_size: Bedrock currently only allows single string
-                inputs, so chunk size is always 1. This input is here
-                only for compatibility with the embeddings interface.
-
+            texts: The list of texts to embed
 
         Returns:
             List of embeddings, one for each text.
@@ -159,3 +155,31 @@ class BedrockEmbeddings(BaseModel, Embeddings):
             Embeddings for the text.
         """
         return self._embedding_func(text)
+
+    async def aembed_query(self, text: str) -> List[float]:
+        """Asynchronous compute query embeddings using a Bedrock model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+
+        return await asyncio.get_running_loop().run_in_executor(
+            None, partial(self.embed_query, text)
+        )
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Asynchronous compute doc embeddings using a Bedrock model.
+
+        Args:
+            texts: The list of texts to embed
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+
+        result = await asyncio.gather(*[self.aembed_query(text) for text in texts])
+
+        return list(result)