diff --git a/langchain/vectorstores/elastic_vector_search.py b/langchain/vectorstores/elastic_vector_search.py
index 57a73dcdd6..537dfb0970 100644
--- a/langchain/vectorstores/elastic_vector_search.py
+++ b/langchain/vectorstores/elastic_vector_search.py
@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 import uuid
+from abc import ABC
 from typing import Any, Dict, Iterable, List, Optional
 
 from langchain.docstore.document import Document
@@ -31,19 +32,81 @@ def _default_script_query(query_vector: List[float]) -> Dict:
     }
 
 
-class ElasticVectorSearch(VectorStore):
+# ElasticVectorSearch is a concrete implementation of the abstract base class
+# VectorStore, which defines a common interface for all vector database
+# implementations. By inheriting from the ABC class, ElasticVectorSearch can be
+# defined as an abstract base class itself, allowing the creation of subclasses with
+# their own specific implementations. If you plan to subclass ElasticVectorSearch,
+# you can inherit from it and define your own implementation of the necessary methods
+# and attributes.
+class ElasticVectorSearch(VectorStore, ABC):
     """Wrapper around Elasticsearch as a vector database.
 
+    To connect to an Elasticsearch instance that does not require
+    login credentials, pass the Elasticsearch URL and index name along with the
+    embedding object to the constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain import ElasticVectorSearch
+            from langchain.embeddings import OpenAIEmbeddings
+
+            embedding = OpenAIEmbeddings()
+            elastic_vector_search = ElasticVectorSearch(
+                elasticsearch_url="http://localhost:9200",
+                index_name="test_index",
+                embedding=embedding
+            )
+
+
+    To connect to an Elasticsearch instance that requires login credentials,
+    including Elastic Cloud, use the Elasticsearch URL format
+    https://username:password@es_host:9243. For example, to connect to Elastic
+    Cloud, create the Elasticsearch URL with the required authentication details and
+    pass it to the ElasticVectorSearch constructor as the named parameter
+    elasticsearch_url.
+
+    You can obtain your Elastic Cloud URL and login credentials by logging in to the
+    Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and
+    navigating to the "Deployments" page.
+
+    To obtain your Elastic Cloud password for the default "elastic" user:
+
+    1. Log in to the Elastic Cloud console at https://cloud.elastic.co
+    2. Go to "Security" > "Users"
+    3. Locate the "elastic" user and click "Edit"
+    4. Click "Reset password"
+    5. Follow the prompts to reset the password
+
+    The format for Elastic Cloud URLs is
+    https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
+
     Example:
         .. code-block:: python
 
             from langchain import ElasticVectorSearch
+            from langchain.embeddings import OpenAIEmbeddings
+
+            embedding = OpenAIEmbeddings()
+
+            elastic_host = "cluster_id.region_id.gcp.cloud.es.io"
+            elasticsearch_url = f"https://username:password@{elastic_host}:9243"
             elastic_vector_search = ElasticVectorSearch(
-                "http://localhost:9200",
-                "embeddings",
-                embedding
+                elasticsearch_url=elasticsearch_url,
+                index_name="test_index",
+                embedding=embedding
             )
 
+    Args:
+        elasticsearch_url (str): The URL for the Elasticsearch instance.
+        index_name (str): The name of the Elasticsearch index for the embeddings.
+        embedding (Embeddings): An object that provides the ability to embed text.
+                It should be an instance of a class that subclasses the Embeddings
+                abstract base class, such as OpenAIEmbeddings()
+
+    Raises:
+        ValueError: If the elasticsearch python package is not installed.
     """
 
     def __init__(self, elasticsearch_url: str, index_name: str, embedding: Embeddings):