Merge branch 'main' into colin

examples/Code_search.ipynb

@@ -84,13 +84,6 @@
     "print(\"Total number of functions extracted:\", len(all_funcs))"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For code search models we use code-search-{model}-code to obtain embeddings for code snippets, and code-search-{model}-text to embed natural language queries."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,

examples/How_to_handle_rate_limits.ipynb

@@ -95,11 +95,9 @@
     "\n",
     "### Requesting a rate limit increase\n",
     "\n",
-    "If you'd like your organization's rate limit increased, please feel free to reach out to <support@openai.com> with the following information:\n",
+    "If you'd like your organization's rate limit increased, please fill out the following form:\n",
     "\n",
-    "- The model(s) you need increased limits on\n",
-    "- The estimated rate of requests\n",
-    "- The reason for the increase"
+        "- [OpenAI Rate Limit Increase Request form](https://forms.gle/56ZrwXXoxAN1yt6i9)\n"
    ]
   },
   {

examples/Obtain_dataset.ipynb

@@ -162,7 +162,7 @@
     "\n",
     "# This will take just between 5 and 10 minutes\n",
     "df['ada_similarity'] = df.combined.apply(lambda x: get_embedding(x, engine='text-embedding-ada-002'))\n",
-    "df['ada_search'] = df.combined.apply(lambda x: get_embedding(x, engine='text-embedding-ada-002'))\n",
+    "df['ada_search'] = df['ada_similarity']\n",
     "df.to_csv('data/fine_food_reviews_with_embeddings_1k.csv')"
    ]
   }