Add JS SDK how-to page. (#787)

examples/sdk/python_sdk.ipynb

@@ -4,6 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "\n",
+    "This how-to takes you through the steps of using the Python SDK for interacting with deployed Langgraph Cloud APIs.\n",
+    "\n",
     "## Initialization\n",
     "\n",
     "### Initializing client\n",
@@ -38,7 +41,7 @@
    "source": [
     "### Selecting an Assistant\n",
     "\n",
-    "To select an assistant we can search the assistants that are hosted on our client, and then select the one we want,"
+    "To select an assistant we can search the assistants that are hosted on our client, and then select the one we want:"
    ]
   },
   {
@@ -84,6 +87,79 @@
     "assistant"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configuring an assistant\n",
+    "\n",
+    "One important thing to know is that graph can be defined to be configurable, meaning that not every instance of the graph needs to be the same (read up on [this guide](https://langchain-ai.github.io/langgraph/how-tos/configuration/) to learn more about how to create your own configurable graphs). Let's briefly show how we can configure an assistant. The first step to do is find the assistant we want to configure. In our simple example we are only hosting a single graph, so we must choose it as the graph to configure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_assistant = assistants[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This assistant has one configurable argument called `model`, which can take on two values: `openai` or `anthropic`. In this case, I want to create a graph that originates from this assistant that uses the `openai` option, which we can do as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config_graph = await client.assistants.create(base_assistant['graph_id'],config = {'configurable':{'model':'openai'}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now if we inspect our new graph, we can see that it has been configured and whenever we run it, it will select the `openai` model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'assistant_id': '4d91047c-585c-4d1b-9ade-5b0f4d292528',\n",
+       " 'graph_id': 'agent',\n",
+       " 'created_at': '2024-06-21T23:06:08.203776+00:00',\n",
+       " 'updated_at': '2024-06-21T23:06:08.203776+00:00',\n",
+       " 'config': {'configurable': {'model': 'openai'}},\n",
+       " 'metadata': {}}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "config_graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Being able to create configurable assistants allows you to create different graphs all based on the same underlying structure. This can be very powerful for testing different configurations of a graph or allowing users to customize their graph."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -369,6 +445,41 @@
    "source": [
     "Perfect! The state persisted across separate runs, and the LLM remembers the name of our user. In a future we will explore non-sequential runs, i.e. not having each run just follow the last one but choosing which checkpoint we start our run from.\n",
     "\n",
+    "### Invoking on a schedule\n",
+    "\n",
+    "Running your graph on a schedule can be valuable in situations where you want to run tasks periodically. The Langgraph Cloud API allows you to do this through the `CronAssistant` class. To schedule a graph job, you need to pass a [cron expression](https://crontab.cronhub.io/) to inform the client when you want to run the graph. `Cron` jobs are run in the background and do not interfere with normal invocations of the graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This schedules a job to run at 15:27 (3:27PM) every day\n",
+    "cron_1 = await client.crons.create(assistant['assistant_id'],schedule=\"27 15 * * *\",input={'messages':[{\"role\":\"user\",\"content\":\"What time is it?\"}]})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that it is **very** important to delete `Cron` jobs that are no longer useful. Otherwise you could rack up unwanted API charges to the LLM! You can delete a `Cron` job using the following code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "await client.crons.delete(cron_1['cron_id'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Querying and Updating the thread\n",
     "\n",
     "### Getting checkpoints by metadata\n",