LangChain-MongoDB: [Experimental] Driver-side index creation helper (#19359)

## Description
Created a helper method to make vector search indexes via client-side
pymongo.

**Recent Update** -- Removed error suppressing/overwriting layer in
favor of letting the original exception provide information.

## ToDo's
- [x] Make _wait_untils for integration test delete index
functionalities.
- [x] Add documentation for its use. Highlight it's experimental
- [x] Post Integration Test Results in a screenshot
- [x] Get review from MongoDB internal team (@shaneharvey, @blink1073 ,
@NoahStapp , @caseyclements)



- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. Added new integration tests. Not eligible for unit testing since the
operation is Atlas Cloud specific.
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.

![image](https://github.com/langchain-ai/langchain/assets/2887713/a3fc8ee1-e04c-4976-accc-fea0eeae028a)


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

docs/docs/integrations/vectorstores/mongodb_atlas.ipynb

@@ -143,6 +143,28 @@
     "    }\n",
     "  ]\n",
     "}\n",
+    "```\n",
+    "\n",
+    "Additionally, if you are running a MongoDB M10 cluster with server version 6.0+, you can leverage the `MongoDBAtlasVectorSearch.create_index`. To add the above index its usage would look like this.\n",
+    "\n",
+    "```python\n",
+    "from langchain_community.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch\n",
+    "from pymongo import MongoClient\n",
+    "\n",
+    "mongo_client = MongoClient(\"<YOUR-CONNECTION-STRING>\")\n",
+    "collection = mongo_client[\"<db_name>\"][\"<collection_name>\"]\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "\n",
+    "vectorstore = MongoDBAtlasVectorSearch(\n",
+    "  collection=collection,\n",
+    "  embedding=embeddings,\n",
+    "  index_name=\"<ATLAS_VECTOR_SEARCH_INDEX_NAME>\",\n",
+    "  relevance_score_fn=\"cosine\",\n",
+    ")\n",
+    "\n",
+    "# Creates an index using the index_name provided and relevance_score_fn type\n",
+    "vectorstore.create_index(dimensions=1536)\n",
     "```"
    ]
   },
@@ -296,6 +318,16 @@
     "    }\n",
     "  ]\n",
     "}\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\":\"page\"}],\n",
+    "  update=True\n",
+    ")\n",
     "```"
    ]
   },