diff --git a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
index 004da71dac2..dd29cf56dc1 100644
--- a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
+++ b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "This notebook covers how to MongoDB Atlas vector search in LangChain, using the `langchain-mongodb` package.\n",
     "\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",
+    ">[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, full text search (BM25), and hybrid search 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/). "
    ]
@@ -23,7 +23,7 @@
     "\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",
+    "To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of cluster on a cloud of your choice available. To get started head over to Atlas here: [quick start](https://www.mongodb.com/docs/atlas/getting-started/).\n",
     "\n",
     "You'll need to install `langchain-mongodb` and `pymongo` to use this integration."
    ]
@@ -104,7 +104,7 @@
     "# | echo: false\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
-    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")"
+    "embeddings = OpenAIEmbeddings()"
    ]
   },
   {
@@ -114,7 +114,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch\n",
+    "from langchain_mongodb import MongoDBAtlasVectorSearch\n",
     "from pymongo import MongoClient\n",
     "\n",
     "# initialize MongoDB python client\n",
@@ -131,7 +131,31 @@
     "    embedding=embeddings,\n",
     "    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,\n",
     "    relevance_score_fn=\"cosine\",\n",
-    ")"
+    ")\n",
+    "\n",
+    "# Create vector search index on the collection\n",
+    "# Since we are using the default OpenAI embedding model (ada-v2) we need to specify the dimensions as 1536\n",
+    "vector_store.create_vector_search_index(dimensions=1536)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aec0bb58",
+   "metadata": {},
+   "source": [
+    "[OPTIONAL] Alternative to the `vector_store.create_vector_search_index` command above, you can also create the vector search index using the Atlas UI with the following index definition:\n",
+    "```json\n",
+    "{\n",
+    "  \"fields\":[\n",
+    "    {\n",
+    "      \"type\": \"vector\",\n",
+    "      \"path\": \"embedding\",\n",
+    "      \"numDimensions\": 1536,\n",
+    "      \"similarity\": \"cosine\"\n",
+    "    }\n",
+    "  ]\n",
+    "}\n",
+    "```"
    ]
   },
   {
@@ -367,6 +391,21 @@
    "id": "dacac7b8",
    "metadata": {},
    "source": [
+    "\n",
+    "\n",
+    "To enable pre-filtering you need to update the index definition to include a filter field. In this example, we will use the `source` field as the filter field.\n",
+    "\n",
+    "This can be done programmatically using the `MongoDBAtlasVectorSearch.create_vector_search_index` method.\n",
+    "\n",
+    "```python\n",
+    "vectorstore.create_vector_search_index(\n",
+    "  dimensions=1536,\n",
+    "  filters=[{\"type\":\"filter\", \"path\":\"source\"}],\n",
+    "  update=True\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "Alternatively, you can also update the index using the Atlas UI with the following index definition:\n",
     "```json\n",
     "{\n",
     "  \"fields\":[\n",
@@ -384,20 +423,10 @@
     "}\n",
     "```\n",
     "\n",
-    "You can also update the index programmatically using the `MongoDBAtlasVectorSearch.create_index` method.\n",
-    "\n",
-    "```python\n",
-    "vectorstore.create_index(\n",
-    "  dimensions=1536,\n",
-    "  filters=[{\"type\":\"filter\", \"path\":\"source\"}],\n",
-    "  update=True\n",
-    ")\n",
-    "```\n",
-    "\n",
     "And then you can run a query with filter as follows:\n",
     "\n",
     "```python\n",
-    "results = vector_store.similarity_search(query=\"foo\",k=1,pre_filter={\"source\": {\"$eq\": \"https://example.com\"}})\n",
+    "results = vector_store.similarity_search(query=\"foo\", k=1, pre_filter={\"source\": {\"$eq\": \"https://example.com\"}})\n",
     "for doc in results:\n",
     "    print(f\"* {doc.page_content} [{doc.metadata}]\")\n",
     "```"
@@ -410,7 +439,7 @@
    "source": [
     "#### Other search methods\n",
     "\n",
-    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `AstraDBVectorStore` check out the [API reference](https://python.langchain.com/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html)."
+    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `MongoDBAtlasVectorStore` check out the [API reference](https://api.python.langchain.com/en/latest/vectorstores/langchain_mongodb.vectorstores.MongoDBAtlasVectorSearch.html)."
    ]
   },
   {
@@ -470,7 +499,7 @@
    "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",
+    ">* More documentation can be found at [MongoDB's LangChain Docs](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",
     "> "
@@ -509,3 +538,4 @@
  "nbformat": 4,
  "nbformat_minor": 5
 }
+