diff --git a/examples/embeddings.ipynb b/examples/embeddings.ipynb
index fe90ee6178c..fb714b4dbae 100644
--- a/examples/embeddings.ipynb
+++ b/examples/embeddings.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "965eecee",
    "metadata": {},
    "outputs": [],
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "68481687",
    "metadata": {},
    "outputs": [],
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "015f4ff5",
    "metadata": {},
    "outputs": [],
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "67baf32e",
    "metadata": {},
    "outputs": [
@@ -69,12 +69,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "4906b8a3",
    "metadata": {},
    "outputs": [],
    "source": [
-    "docsearch = ElasticVectorSearch.from_texts(\"http://localhost:9200\", texts, embeddings)\n",
+    "docsearch = ElasticVectorSearch.from_texts(texts, embeddings, elasticsearch_url=\"http://localhost:9200\")\n",
     "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "docs = docsearch.similarity_search(query)"
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "95f9eee9",
    "metadata": {},
    "outputs": [
diff --git a/langchain/vectorstores/base.py b/langchain/vectorstores/base.py
index d6da56f7e40..db4f7196e44 100644
--- a/langchain/vectorstores/base.py
+++ b/langchain/vectorstores/base.py
@@ -1,8 +1,9 @@
 """Interface for vector stores."""
 from abc import ABC, abstractmethod
-from typing import List
+from typing import Any, List
 
 from langchain.docstore.document import Document
+from langchain.embeddings.base import Embeddings
 
 
 class VectorStore(ABC):
@@ -11,3 +12,10 @@ class VectorStore(ABC):
     @abstractmethod
     def similarity_search(self, query: str, k: int = 4) -> List[Document]:
         """Return docs most similar to query."""
+
+    @classmethod
+    @abstractmethod
+    def from_texts(
+        cls, texts: List[str], embedding: Embeddings, **kwargs: Any
+    ) -> "VectorStore":
+        """Return VectorStore initialized from texts and embeddings."""
diff --git a/langchain/vectorstores/elastic_vector_search.py b/langchain/vectorstores/elastic_vector_search.py
index deeee972784..13114cce8af 100644
--- a/langchain/vectorstores/elastic_vector_search.py
+++ b/langchain/vectorstores/elastic_vector_search.py
@@ -1,6 +1,7 @@
 """Wrapper around Elasticsearch vector database."""
+import os
 import uuid
-from typing import Callable, Dict, List
+from typing import Any, Callable, Dict, List
 
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
@@ -46,7 +47,7 @@ class ElasticVectorSearch(VectorStore):
 
     def __init__(
         self,
-        elastic_url: str,
+        elasticsearch_url: str,
         index_name: str,
         mapping: Dict,
         embedding_function: Callable,
@@ -62,7 +63,7 @@ class ElasticVectorSearch(VectorStore):
         self.embedding_function = embedding_function
         self.index_name = index_name
         try:
-            es_client = elasticsearch.Elasticsearch(elastic_url)  # noqa
+            es_client = elasticsearch.Elasticsearch(elasticsearch_url)  # noqa
         except ValueError as e:
             raise ValueError(
                 "Your elasticsearch client string is misformatted. " f"Got error: {e} "
@@ -89,7 +90,7 @@ class ElasticVectorSearch(VectorStore):
 
     @classmethod
     def from_texts(
-        cls, elastic_url: str, texts: List[str], embedding: Embeddings
+        cls, texts: List[str], embedding: Embeddings, **kwargs: Any
     ) -> "ElasticVectorSearch":
         """Construct ElasticVectorSearch wrapper from raw documents.
 
@@ -107,11 +108,21 @@ class ElasticVectorSearch(VectorStore):
                 from langchain.embeddings import OpenAIEmbeddings
                 embeddings = OpenAIEmbeddings()
                 elastic_vector_search = ElasticVectorSearch.from_texts(
-                    "http://localhost:9200",
                     texts,
-                    embeddings
+                    embeddings,
+                    elasticsearch_url="http://localhost:9200"
                 )
         """
+        elasticsearch_url = kwargs.get("elasticsearch_url")
+        if not elasticsearch_url:
+            elasticsearch_url = os.environ.get("ELASTICSEARCH_URL")
+
+            if elasticsearch_url is None or elasticsearch_url == "":
+                raise ValueError(
+                    "Did not find Elasticsearch URL, please add an environment variable"
+                    " `ELASTICSEARCH_URL` which contains it, or pass"
+                    "  `elasticsearch_url` as a named parameter."
+                )
         try:
             import elasticsearch
             from elasticsearch.helpers import bulk
@@ -121,7 +132,7 @@ class ElasticVectorSearch(VectorStore):
                 "Please install it with `pip install elasticearch`."
             )
         try:
-            client = elasticsearch.Elasticsearch(elastic_url)
+            client = elasticsearch.Elasticsearch(elasticsearch_url)
         except ValueError as e:
             raise ValueError(
                 "Your elasticsearch client string is misformatted. " f"Got error: {e} "
@@ -144,4 +155,4 @@ class ElasticVectorSearch(VectorStore):
             requests.append(request)
         bulk(client, requests)
         client.indices.refresh(index=index_name)
-        return cls(elastic_url, index_name, mapping, embedding.embed_query)
+        return cls(elasticsearch_url, index_name, mapping, embedding.embed_query)
diff --git a/langchain/vectorstores/faiss.py b/langchain/vectorstores/faiss.py
index 81446ad200e..937ad80e731 100644
--- a/langchain/vectorstores/faiss.py
+++ b/langchain/vectorstores/faiss.py
@@ -53,7 +53,9 @@ class FAISS(VectorStore):
         return docs
 
     @classmethod
-    def from_texts(cls, texts: List[str], embedding: Embeddings) -> "FAISS":
+    def from_texts(
+        cls, texts: List[str], embedding: Embeddings, **kwargs: Any
+    ) -> "FAISS":
         """Construct FAISS wrapper from raw documents.
 
         This is a user friendly interface that: