diff --git a/docs/docs/integrations/llm_caching.ipynb b/docs/docs/integrations/llm_caching.ipynb
index 42e2036eef..f13e5b4bf9 100644
--- a/docs/docs/integrations/llm_caching.ipynb
+++ b/docs/docs/integrations/llm_caching.ipynb
@@ -724,6 +724,83 @@
     "llm(\"Tell me joke\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9b2b2777",
+   "metadata": {},
+   "source": [
+    "## `MongoDB Atlas` Cache\n",
+    "\n",
+    "[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS, Azure, and GCP. It has native support for \n",
+    "Vector Search on the MongoDB document data.\n",
+    "Use [MongoDB Atlas Vector Search](/docs/integrations/providers/mongodb_atlas) to semantically cache prompts and responses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecdc2a0a",
+   "metadata": {},
+   "source": [
+    "### `MongoDBCache`\n",
+    "An abstraction to store a simple cache in MongoDB. This does not use Semantic Caching, nor does it require an index to be made on the collection before generation.\n",
+    "\n",
+    "To import this cache:\n",
+    "\n",
+    "```python\n",
+    "from langchain_mongodb.cache import MongoDBCache\n",
+    "```\n",
+    "\n",
+    "\n",
+    "To use this cache with your LLMs:\n",
+    "```python\n",
+    "from langchain_core.globals import set_llm_cache\n",
+    "\n",
+    "# use any embedding provider...\n",
+    "from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n",
+    "\n",
+    "mongodb_atlas_uri = \"<YOUR_CONNECTION_STRING>\"\n",
+    "COLLECTION_NAME=\"<YOUR_CACHE_COLLECTION_NAME>\"\n",
+    "DATABASE_NAME=\"<YOUR_DATABASE_NAME>\"\n",
+    "\n",
+    "set_llm_cache(MongoDBCache(\n",
+    "    connection_string=mongodb_atlas_uri,\n",
+    "    collection_name=COLLECTION_NAME,\n",
+    "    database_name=DATABASE_NAME,\n",
+    "))\n",
+    "```\n",
+    "\n",
+    "\n",
+    "### `MongoDBAtlasSemanticCache`\n",
+    "Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached results. Under the hood it blends MongoDBAtlas as both a cache and a vectorstore.\n",
+    "The MongoDBAtlasSemanticCache inherits from `MongoDBAtlasVectorSearch` and needs an Atlas Vector Search Index defined to work. Please look at the [usage example](/docs/integrations/vectorstores/mongodb_atlas) on how to set up the index.\n",
+    "\n",
+    "To import this cache:\n",
+    "```python\n",
+    "from langchain_mongodb.cache import MongoDBAtlasSemanticCache\n",
+    "```\n",
+    "\n",
+    "To use this cache with your LLMs:\n",
+    "```python\n",
+    "from langchain_core.globals import set_llm_cache\n",
+    "\n",
+    "# use any embedding provider...\n",
+    "from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n",
+    "\n",
+    "mongodb_atlas_uri = \"<YOUR_CONNECTION_STRING>\"\n",
+    "COLLECTION_NAME=\"<YOUR_CACHE_COLLECTION_NAME>\"\n",
+    "DATABASE_NAME=\"<YOUR_DATABASE_NAME>\"\n",
+    "\n",
+    "set_llm_cache(MongoDBAtlasSemanticCache(\n",
+    "    embedding=FakeEmbeddings(),\n",
+    "    connection_string=mongodb_atlas_uri,\n",
+    "    collection_name=COLLECTION_NAME,\n",
+    "    database_name=DATABASE_NAME,\n",
+    "))\n",
+    "```\n",
+    "\n",
+    "To find more resources about using MongoDBSemanticCache visit [here](https://www.mongodb.com/blog/post/introducing-semantic-caching-dedicated-mongodb-lang-chain-package-gen-ai-apps)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "726fe754",
@@ -993,7 +1070,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "CASSANDRA_KEYSPACE =  demo_keyspace\n"
@@ -1029,7 +1106,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "ASTRA_DB_ID =  01234567-89ab-cdef-0123-456789abcdef\n",