Added deeplake use case examples of the new features (#6528)

<!--
Thank you for contributing to LangChain! Your PR will appear in our
release under the title you set. Please make sure it highlights your
valuable contribution.

Replace this with a description of the change, the issue it fixes (if
applicable), and relevant context. List any dependencies required for
this change.

After you're done, someone will review your PR. They may suggest
improvements. If no one reviews your PR within a few days, feel free to
@-mention the same people again, as notifications can get lost.

Finally, we'd love to show appreciation for your contribution - if you'd
like us to shout you out on Twitter, please also include your handle!
-->

<!-- Remove if not applicable -->

Fixes # (issue)

#### Before submitting

<!-- If you're adding a new integration, please include:

1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use


See contribution guidelines for more information on how to write tests,
lint
etc:


https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->

#### Who can review?

Tag maintainers/contributors who might be interested:

<!-- For a quicker response, figure out the right person to tag with @

  @hwchase17 - project lead

  Tracing / Callbacks
  - @agola11

  Async
  - @agola11

  DataLoaders
  - @eyurtsev

  Models
  - @hwchase17
  - @agola11

  Agents / Tools / Toolkits
  - @hwchase17

  VectorStores / Retrievers / Memory
  - @dev2049

 -->
 
 1. Added use cases of the new features
 2. Done some code refactoring

---------

Co-authored-by: Ivo Stranic <istranic@gmail.com>

docs/extras/use_cases/code/twitter-the-algorithm-analysis-deeplake.ipynb

@@ -5,8 +5,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Deep Lake\n",
-    "In this tutorial, we are going to use Langchain + Deep Lake with GPT4 to analyze the code base of the twitter algorithm. "
+    "# Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep Lake\n",
+    "In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT4 to analyze the code base of the twitter algorithm. "
    ]
   },
   {
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!python3 -m pip install --upgrade langchain deeplake openai tiktoken"
+    "!python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken"
    ]
   },
   {
@@ -41,7 +41,8 @@
     "from langchain.vectorstores import DeepLake\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
-    "os.environ[\"ACTIVELOOP_TOKEN\"] = getpass.getpass(\"Activeloop Token:\")"
+    "activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",
+    "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
    ]
   },
   {
@@ -149,6 +150,29 @@
     "db.add_documents(texts)"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# username = \"davitbun\"  # replace with your username from app.activeloop.ai\n",
+    "# db = DeepLake(\n",
+    "#     dataset_path=f\"hub://{username}/twitter-algorithm\",\n",
+    "#     embedding_function=embeddings,\n",
+    "#     runtime={\"tensor_db\": True}\n",
+    "# )\n",
+    "# db.add_documents(texts)"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -176,6 +200,7 @@
     "    dataset_path=\"hub://davitbun/twitter-algorithm\",\n",
     "    read_only=True,\n",
     "    embedding_function=embeddings,\n",
+    "    \n",
     ")"
    ]
   },