add doc about reusing MongoDBAtlasVectorSearch (#5805)

DOC: add doc about reusing MongoDBAtlasVectorSearch

#### Who can review?

Anyone authorized.

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

@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "320af802-9271-46ee-948f-d2453933d44b",
+   "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."
@@ -143,6 +143,47 @@
    "source": [
     "print(docs[0].page_content)"
    ]
+  },
+  {
+   "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6336fe79-3e73-48be-b20a-0ff1bb6a4399",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pymongo import MongoClient\n",
+    "from langchain.vectorstores import MongoDBAtlasVectorSearch\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "import os\n",
+    "\n",
+    "MONGODB_ATLAS_URI = os.environ['MONGODB_ATLAS_URI']\n",
+    "\n",
+    "# initialize MongoDB python client\n",
+    "client = MongoClient(MONGODB_ATLAS_URI)\n",
+    "\n",
+    "db_name = \"langchain_db\"\n",
+    "collection_name = \"langchain_col\"\n",
+    "collection = client[db_name][collection_name]\n",
+    "index_name = \"langchain_index\"\n",
+    "\n",
+    "# initialize vector store\n",
+    "vectorStore = MongoDBAtlasVectorSearch(\n",
+    "    collection, OpenAIEmbeddings(), index_name=index_name)\n",
+    "\n",
+    "# perform a similarity search between the embedding of the query and the embeddings of the documents\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = vectorStore.similarity_search(query)\n",
+    "\n",
+    "print(docs[0].page_content)"
+   ]
   }
  ],
  "metadata": {