Update MongoDB Atlas support docs (#6022)

Updating MongoDB Atlas support docs @hwchase17 let me know if you have
any questions

docs/modules/indexes/vectorstores/examples/mongodb_atlas_vector_search.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "683953b3",
    "metadata": {},
@@ -8,14 +9,14 @@
     "#### Commented out until further notice\n",
     "MongoDB Atlas Vector Search\n",
     "\n",
-    ">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a document database managed in the cloud. It also enables Lucene and its vector search feature.\n",
+    ">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database offered in the cloud service provider of your choice (AWS , Azure, and GCP).  It now has support for native Vector Search ontop of your MongoDB document data.\n",
     "\n",
-    "This notebook shows how to use the functionality related to the `MongoDB Atlas Vector Search` feature where you can store your embeddings in MongoDB documents and create a Lucene vector index to perform a KNN search.\n",
+    "This notebook shows how to use `MongoDB Atlas Vector Search` to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with and approximate nearest neighbor algorithm.\n",
     "\n",
-    "It uses the [knnBeta Operator](https://www.mongodb.com/docs/atlas/atlas-search/knn-beta) available in MongoDB Atlas Search. This feature is in early access and available only for evaluation purposes, to validate functionality, and to gather feedback from a small closed group of early access users. It is not recommended for production deployments as we may introduce breaking changes.\n",
+    "It uses the [knnBeta Operator](https://www.mongodb.com/docs/atlas/atlas-search/knn-beta) available in MongoDB Atlas Search. This feature is in Public Preview and available only for evaluation purposes, to validate functionality, and to gather feedback from public preview users. It is not recommended for production deployments as we may introduce breaking changes.\n",
     "\n",
-    "To use MongoDB Atlas, you must have first deployed a cluster. Free clusters are available. \n",
-    "Here is the MongoDB Atlas [quick start](https://www.mongodb.com/docs/atlas/getting-started/)."
+    "To use MongoDB Atlas, you must have first deployed a cluster. We have a Forever Free tier of clusters available. \n",
+    "To get started head over to Atlas here: [quick start](https://www.mongodb.com/docs/atlas/getting-started/)."
    ]
   },
   {
@@ -38,24 +39,39 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "import getpass\n",
     "\n",
-    "MONGODB_ATLAS_URI = os.environ['MONGODB_ATLAS_URI']"
+    "MONGODB_ATLAS_CLUSTER_URI = getpass.getpass('MongoDB Atlas Cluster URI:')\n",
+    "MONGODB_ATLAS_CLUSTER_URI = os.environ['MONGODB_ATLAS_CLUSTER_URI']"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "457ace44-1d95-4001-9dd5-78811ab208ad",
    "metadata": {},
    "source": [
-    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key. Make sure the environment variable `OPENAI_API_KEY` is set up before proceeding."
+    "We want to use `OpenAIEmbeddings` so we have setup the OpenAI API Key. "
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d8f240d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')\n",
+    "OPENAI_API_KEY = os.environ['OPENAI_API_KEY']"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "1f3ecc42",
    "metadata": {},
    "source": [
-    "Now, let's create a Lucene vector index on your cluster. In the below example, `embedding` is the name of the field that contains the embedding vector. Please refer to the [documentation](https://www.mongodb.com/docs/atlas/atlas-search/define-field-mappings-for-vector-search) to get more details on how to define an Atlas Search index.\n",
+    "Now, let's create a vector index on your cluster. In the below example, `embedding` is the name of the field that contains the embedding vector. Please refer to the [documentation](https://www.mongodb.com/docs/atlas/atlas-search/define-field-mappings-for-vector-search) to get more details on how to define an Atlas Vector Search index.\n",
     "You can name the index `langchain_demo` and create the index on the namespace `lanchain_db.langchain_col`. Finally, write the following definition in the JSON editor:\n",
     "\n",
     "```json\n",
@@ -115,7 +131,7 @@
     "from pymongo import MongoClient\n",
     "\n",
     "# initialize MongoDB python client\n",
-    "client = MongoClient(MONGODB_ATLAS_CONNECTION_STRING)\n",
+    "client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)\n",
     "\n",
     "db_name = \"lanchain_db\"\n",
     "collection_name = \"langchain_col\"\n",
@@ -146,11 +162,12 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "851a2ec9-9390-49a4-8412-3e132c9f789d",
    "metadata": {},
    "source": [
-    "You can reuse vector index you created before, make sure environment variable `OPENAI_API_KEY` is set up, then create another file."
+    "You can reuse the vector index you created before, make sure environment variable `OPENAI_API_KEY` is set up, then create another file."
    ]
   },
   {