Updating the "Using embeddings" cookbook to reflect the latest SDK (#1014)

examples/Using_embeddings.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -27,11 +27,12 @@
     }
    ],
    "source": [
-    "import openai\n",
+    "from openai import OpenAI\n",
+    "client = OpenAI()\n",
     "\n",
-    "embedding = openai.Embedding.create(\n",
-    "    input=\"Your text goes here\", model=\"text-embedding-3-small\"\n",
-    ")[\"data\"][0][\"embedding\"]\n",
+    "embedding = client.embeddings.create(\n",
+    "    input=\"Your text goes here\", model=\"text-embedding-ada-002\"\n",
+    ").data[0].embedding\n",
     "len(embedding)\n"
    ]
   },
@@ -50,13 +51,14 @@
    "outputs": [],
    "source": [
     "# Negative example (slow and rate-limited)\n",
-    "import openai\n",
+    "from openai import OpenAI\n",
+    "client = OpenAI()\n",
     "\n",
     "num_embeddings = 10000 # Some large number\n",
     "for i in range(num_embeddings):\n",
-    "    embedding = openai.Embedding.create(\n",
-    "        input=\"Your text goes here\", model=\"text-embedding-3-small\"\n",
-    "    )[\"data\"][0][\"embedding\"]\n",
+    "    embedding = client.embeddings.create(\n",
+    "        input=\"Your text goes here\", model=\"text-embedding-ada-002\"\n",
+    "    ).data[0].embedding\n",
     "    print(len(embedding))"
    ]
   },
@@ -75,13 +77,14 @@
    ],
    "source": [
     "# Best practice\n",
-    "import openai\n",
     "from tenacity import retry, wait_random_exponential, stop_after_attempt\n",
+    "from openai import OpenAI\n",
+    "client = OpenAI()\n",
     "\n",
     "# Retry up to 6 times with exponential backoff, starting at 1 second and maxing out at 20 seconds delay\n",
     "@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))\n",
     "def get_embedding(text: str, model=\"text-embedding-3-small\") -> list[float]:\n",
-    "    return openai.Embedding.create(input=[text], model=model)[\"data\"][0][\"embedding\"]\n",
+    "    return client.embeddings.create(input=[text], model=model).data[0].embedding\n",
     "\n",
     "embedding = get_embedding(\"Your text goes here\", model=\"text-embedding-3-small\")\n",
     "print(len(embedding))"