docs:Update reference to langchain-mongodb (#22705)

**Description**: Update reference to langchain-mongodb

docs/docs/integrations/vectorstores/mongodb_atlas.ipynb

@@ -7,29 +7,33 @@
    "source": [
     "# MongoDB Atlas\n",
     "\n",
-    ">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS, Azure, and GCP.  It now has support for native Vector Search on your MongoDB document data.\n",
+    "This notebook covers how to MongoDB Atlas vector search in LangChain, using the `langchain-mongodb` package.\n",
     "\n",
-    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
-    "\n",
-    "This notebook shows how to use [MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with an approximate nearest neighbor algorithm (`Hierarchical Navigable Small Worlds`). It uses the  [$vectorSearch MQL Stage](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/). \n",
+    ">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS, Azure, and GCP.  It supports native Vector Search and full text search (BM25) on your MongoDB document data.\n",
     "\n",
+    ">[MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) allows to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with an approximate nearest neighbor algorithm (`Hierarchical Navigable Small Worlds`). It uses the [$vectorSearch MQL Stage](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "359b8e9b",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    ">*An Atlas cluster running MongoDB version 6.0.11, 7.0.2, or later (including RCs).\n",
     "\n",
-    "To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of clusters available. To get started head over to Atlas here: [quick start](https://www.mongodb.com/docs/atlas/getting-started/).\n",
+    ">*An OpenAI API Key. You must have a paid OpenAI account with credits available for API requests.\n",
     "\n",
-    " "
+    "You'll need to install `langchain-mongodb` to use this integration"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5abfec15",
+   "id": "d899e588",
    "metadata": {},
    "source": [
-    "> Note: \n",
-    "> \n",
-    ">* More documentation can be found at [LangChain-MongoDB site](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/)\n",
-    ">* This feature is Generally Available and ready for production deployments.\n",
-    ">* The langchain version 0.0.305 ([release notes](https://github.com/langchain-ai/langchain/releases/tag/v0.0.305)) introduces the support for $vectorSearch MQL stage, which is available with MongoDB Atlas 6.0.11 and 7.0.2. Users utilizing earlier versions of MongoDB Atlas need to pin their LangChain version to <=0.0.304\n",
-    "> "
+    "## Setting up MongoDB Atlas Cluster\n",
+    "To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of clusters available. To get started head over to Atlas here: [quick start](https://www.mongodb.com/docs/atlas/getting-started/)."
    ]
   },
   {
@@ -37,6 +41,7 @@
    "id": "1b5ce18d",
    "metadata": {},
    "source": [
+    "## Usage\n",
     "In the notebook we will demonstrate how to perform `Retrieval Augmented Generation` (RAG) using MongoDB Atlas, OpenAI and Langchain. We will be performing Similarity Search, Similarity Search with Metadata Pre-Filtering, and Question Answering over the PDF document for [GPT 4 technical report](https://arxiv.org/pdf/2303.08774.pdf) that came out in March 2023 and hence is not part of the OpenAI's Large Language Model(LLM)'s parametric memory, which had a knowledge cutoff of September 2021."
    ]
   },
@@ -76,7 +81,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain pypdf pymongo langchain-openai tiktoken"
+    "%pip install --upgrade --quiet langchain langchain-mongodb pypdf pymongo langchain-openai tiktoken"
    ]
   },
   {
@@ -411,6 +416,18 @@
    "source": [
     "GPT-4 requires significantly more compute than earlier GPT models. On a dataset derived from OpenAI's internal codebase, GPT-4 requires 100p (petaflops) of compute to reach the lowest loss, while the smaller models require 1-10n (nanoflops)."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ac44802",
+   "metadata": {},
+   "source": [
+    "# Other Notes\n",
+    ">* More documentation can be found at [LangChain-MongoDB](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/) site\n",
+    ">* This feature is Generally Available and ready for production deployments.\n",
+    ">* The langchain version 0.0.305 ([release notes](https://github.com/langchain-ai/langchain/releases/tag/v0.0.305)) introduces the support for $vectorSearch MQL stage, which is available with MongoDB Atlas 6.0.11 and 7.0.2. Users utilizing earlier versions of MongoDB Atlas need to pin their LangChain version to <=0.0.304\n",
+    "> "
+   ]
   }
  ],
  "metadata": {