Update embeddings notebook

* Update api_version to 2022-12-01
* Fix some texts

examples/azure/embeddings.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Azure embeddings example\n",
     "In this example we'll try to go over all operations for embeddings that can be done using the Azure endpoints. \\\n",
-    "This example focuses on finetuning but touches on the majority of operations that are also available using the API. This example is meant to be a quick way of showing simple operations and is not meant as a tutorial."
+    "This example focuses on embeddings but also touches on the some other operations that are also available using the API. This example is meant to be a quick way of showing simple operations and is not meant as a tutorial."
    ]
   },
   {
@@ -38,7 +38,7 @@
     "openai.api_base = '' # Please add your endpoint here\n",
     "\n",
     "openai.api_type = 'azure'\n",
-    "openai.api_version = '2022-03-01-preview' # this may change in the future"
+    "openai.api_version = '2022-12-01' # this may change in the future"
    ]
   },
   {
@@ -46,7 +46,7 @@
    "metadata": {},
    "source": [
     "## Deployments\n",
-    "In this section we are going to create a deployment using the finetune model that we just adapted and then used the deployment to create a simple completion operation."
+    "In this section we are going to create a deployment that we can use to create embeddings."
    ]
   },
   {
@@ -54,7 +54,7 @@
    "metadata": {},
    "source": [
     "### Deployments: Create Manually\n",
-    "Let's create a deployment using the text-similarity-curie-001 engine. You can create a new deployment by going to your Resource in your portal under \"Resource Management\" -> \"Deployments\"."
+    "Let's create a deployment using the `text-similarity-curie-001` engine. Create a new deployment by going to your Resource in your portal under \"Resource Management\" -> \"Deployments\"."
    ]
   },
   {
@@ -113,18 +113,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print('While deployment running, selecting a completed one.')\n",
+    "print('While deployment running, selecting a completed one that supports embeddings.')\n",
     "deployment_id = None\n",
     "result = openai.Deployment.list()\n",
     "for deployment in result.data:\n",
-    "    if deployment[\"status\"] == \"succeeded\":\n",
-    "        deployment_id = deployment[\"id\"]\n",
-    "        break\n",
+    "    if deployment[\"status\"] != \"succeeded\":\n",
+    "        continue\n",
+    "    \n",
+    "    model = openai.Model.retrieve(deployment[\"model\"])\n",
+    "    if model[\"capabilities\"][\"embeddings\"] != True:\n",
+    "        continue\n",
+    "    \n",
+    "    deployment_id = deployment[\"id\"]\n",
+    "    break\n",
     "\n",
     "if not deployment_id:\n",
     "    print('No deployment with status: succeeded found.')\n",
     "else:\n",
-    "    print(f'Found a successful deployment with id: {deployment_id}.')"
+    "    print(f'Found a succeeded deployment that supports embeddings with id: {deployment_id}.')"
    ]
   },
   {