diff --git a/docs/modules/models/text_embedding/examples/elasticsearch.ipynb b/docs/modules/models/text_embedding/examples/elasticsearch.ipynb
index a9aa79884da..3a9b6b7d0ab 100644
--- a/docs/modules/models/text_embedding/examples/elasticsearch.ipynb
+++ b/docs/modules/models/text_embedding/examples/elasticsearch.ipynb
@@ -1,124 +1,252 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1eZl1oaVUNeC"
+   },
+   "source": [
+    "# Elasticsearch\n",
+    "Walkthrough of how to generate embeddings using a hosted embedding model in Elasticsearch\n",
+    "\n",
+    "The easiest way to instantiate the `ElasticsearchEmebddings` class it either\n",
+    "- using the `from_credentials` constructor if you are using Elastic Cloud\n",
+    "- or using the `from_es_connection` constructor with any Elasticsearch cluster"
+   ]
   },
-  "cells": [
-    {
-      "cell_type": "code",
-      "source": [
-        "!pip -q install elasticsearch langchain"
-      ],
-      "metadata": {
-        "id": "6dJxqebov4eU"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import elasticsearch\n",
-        "from langchain.embeddings.elasticsearch import ElasticsearchEmbeddings"
-      ],
-      "metadata": {
-        "id": "RV7C3DUmv4aq"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Define the model ID\n",
-        "model_id = 'your_model_id'"
-      ],
-      "metadata": {
-        "id": "MrT3jplJvp09"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Instantiate ElasticsearchEmbeddings using credentials\n",
-        "embeddings = ElasticsearchEmbeddings.from_credentials(\n",
-        "    model_id,\n",
-        "    es_cloud_id='your_cloud_id', \n",
-        "    es_user='your_user', \n",
-        "    es_password='your_password'\n",
-        ")\n"
-      ],
-      "metadata": {
-        "id": "svtdnC-dvpxR"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Create embeddings for multiple documents\n",
-        "documents = [\n",
-        "    'This is an example document.', \n",
-        "    'Another example document to generate embeddings for.'\n",
-        "]\n",
-        "document_embeddings = embeddings.embed_documents(documents)\n"
-      ],
-      "metadata": {
-        "id": "7DXZAK7Kvpth"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Print document embeddings\n",
-        "for i, embedding in enumerate(document_embeddings):\n",
-        "    print(f\"Embedding for document {i+1}: {embedding}\")\n"
-      ],
-      "metadata": {
-        "id": "K8ra75W_vpqy"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Create an embedding for a single query\n",
-        "query = 'This is a single query.'\n",
-        "query_embedding = embeddings.embed_query(query)\n"
-      ],
-      "metadata": {
-        "id": "V4Q5kQo9vpna"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Print query embedding\n",
-        "print(f\"Embedding for query: {query_embedding}\")\n"
-      ],
-      "metadata": {
-        "id": "O0oQDzGKvpkz"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "6dJxqebov4eU"
+   },
+   "outputs": [],
+   "source": [
+    "!pip -q install elasticsearch langchain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RV7C3DUmv4aq"
+   },
+   "outputs": [],
+   "source": [
+    "import elasticsearch\n",
+    "from langchain.embeddings.elasticsearch import ElasticsearchEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "MrT3jplJvp09"
+   },
+   "outputs": [],
+   "source": [
+    "# Define the model ID\n",
+    "model_id = 'your_model_id'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "j5F-nwLVS_Zu"
+   },
+   "source": [
+    "## Testing with `from_credentials`\n",
+    "This required an Elastic Cloud `cloud_id`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "svtdnC-dvpxR"
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate ElasticsearchEmbeddings using credentials\n",
+    "embeddings = ElasticsearchEmbeddings.from_credentials(\n",
+    "    model_id,\n",
+    "    es_cloud_id='your_cloud_id', \n",
+    "    es_user='your_user', \n",
+    "    es_password='your_password'\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7DXZAK7Kvpth"
+   },
+   "outputs": [],
+   "source": [
+    "# Create embeddings for multiple documents\n",
+    "documents = [\n",
+    "    'This is an example document.', \n",
+    "    'Another example document to generate embeddings for.'\n",
+    "]\n",
+    "document_embeddings = embeddings.embed_documents(documents)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "K8ra75W_vpqy"
+   },
+   "outputs": [],
+   "source": [
+    "# Print document embeddings\n",
+    "for i, embedding in enumerate(document_embeddings):\n",
+    "    print(f\"Embedding for document {i+1}: {embedding}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "V4Q5kQo9vpna"
+   },
+   "outputs": [],
+   "source": [
+    "# Create an embedding for a single query\n",
+    "query = 'This is a single query.'\n",
+    "query_embedding = embeddings.embed_query(query)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "O0oQDzGKvpkz"
+   },
+   "outputs": [],
+   "source": [
+    "# Print query embedding\n",
+    "print(f\"Embedding for query: {query_embedding}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rHN03yV6TJ5q"
+   },
+   "source": [
+    "## Testing with Existing Elasticsearch client connection\n",
+    "This can be used with any Elasticsearch deployment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "GMQcJDwBTJFm"
+   },
+   "outputs": [],
+   "source": [
+    "# Create Elasticsearch connection\n",
+    "es_connection = Elasticsearch(\n",
+    "    hosts=['https://es_cluster_url:port'], \n",
+    "    basic_auth=('user', 'password')\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "WTYIU4u3TJO1"
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate ElasticsearchEmbeddings using es_connection\n",
+    "embeddings = ElasticsearchEmbeddings.from_es_connection(\n",
+    "  model_id,\n",
+    "  es_connection,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "4gdAUHwoTJO3"
+   },
+   "outputs": [],
+   "source": [
+    "# Create embeddings for multiple documents\n",
+    "documents = [\n",
+    "    'This is an example document.', \n",
+    "    'Another example document to generate embeddings for.'\n",
+    "]\n",
+    "document_embeddings = embeddings.embed_documents(documents)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RC_-tov6TJO3"
+   },
+   "outputs": [],
+   "source": [
+    "# Print document embeddings\n",
+    "for i, embedding in enumerate(document_embeddings):\n",
+    "    print(f\"Embedding for document {i+1}: {embedding}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "6GEnHBqETJO3"
+   },
+   "outputs": [],
+   "source": [
+    "# Create an embedding for a single query\n",
+    "query = 'This is a single query.'\n",
+    "query_embedding = embeddings.embed_query(query)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-kyUQAXDTJO4"
+   },
+   "outputs": [],
+   "source": [
+    "# Print query embedding\n",
+    "print(f\"Embedding for query: {query_embedding}\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/langchain/embeddings/elasticsearch.py b/langchain/embeddings/elasticsearch.py
index 78d7dec04cf..9d3b1192310 100644
--- a/langchain/embeddings/elasticsearch.py
+++ b/langchain/embeddings/elasticsearch.py
@@ -5,6 +5,7 @@ from typing import TYPE_CHECKING, List, Optional
 from langchain.utils import get_from_env
 
 if TYPE_CHECKING:
+    from elasticsearch import Elasticsearch
     from elasticsearch.client import MlClient
 
 from langchain.embeddings.base import Embeddings
@@ -110,6 +111,68 @@ class ElasticsearchEmbeddings(Embeddings):
         client = MlClient(es_connection)
         return cls(client, model_id, input_field=input_field)
 
+    @classmethod
+    def from_es_connection(
+        cls,
+        model_id: str,
+        es_connection: Elasticsearch,
+        input_field: str = "text_field",
+    ) -> ElasticsearchEmbeddings:
+        """
+        Instantiate embeddings from an existing Elasticsearch connection.
+
+        This method provides a way to create an instance of the ElasticsearchEmbeddings
+        class using an existing Elasticsearch connection. The connection object is used
+        to create an MlClient, which is then used to initialize the
+        ElasticsearchEmbeddings instance.
+
+        Args:
+        model_id (str): The model_id of the model deployed in the Elasticsearch cluster.
+        es_connection (elasticsearch.Elasticsearch): An existing Elasticsearch
+        connection object. input_field (str, optional): The name of the key for the
+        input text field in the document. Defaults to 'text_field'.
+
+        Returns:
+        ElasticsearchEmbeddings: An instance of the ElasticsearchEmbeddings class.
+
+        Example Usage:
+        from elasticsearch import Elasticsearch
+        from langchain.embeddings import ElasticsearchEmbeddings
+
+        # Define the model ID and input field name (if different from default)
+        model_id = "your_model_id"
+        # Optional, only if different from 'text_field'
+        input_field = "your_input_field"
+
+        # Create Elasticsearch connection
+        es_connection = Elasticsearch(
+            hosts=["localhost:9200"], http_auth=("user", "password")
+        )
+
+        # Instantiate ElasticsearchEmbeddings using the existing connection
+        embeddings = ElasticsearchEmbeddings.from_es_connection(
+            model_id,
+            es_connection,
+            input_field=input_field,
+        )
+
+        documents = [
+            "This is an example document.",
+            "Another example document to generate embeddings for.",
+        ]
+        embeddings_generator.embed_documents(documents)
+        """
+        # Importing MlClient from elasticsearch.client within the method to
+        # avoid unnecessary import if the method is not used
+        from elasticsearch.client import MlClient
+
+        # Create an MlClient from the given Elasticsearch connection
+        client = MlClient(es_connection)
+
+        # Return a new instance of the ElasticsearchEmbeddings class with
+        # the MlClient, model_id, and input_field
+        return cls(client, model_id, input_field=input_field)
+
     def _embedding_func(self, texts: List[str]) -> List[List[float]]:
         """
         Generate embeddings for the given texts using the Elasticsearch model.