added colab

examples/evaluation/ragas/openai-ragas-synthetic-test.ipynb

@@ -33,7 +33,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install ragas"
+    "! pip install -q ragas"
    ]
   },
   {
@@ -204,7 +204,8 @@
    "id": "cb3721b0-1e04-4b25-9348-71c251c0eff9",
    "metadata": {},
    "source": [
-    "### Saving results"
+    "### Saving results\n",
+    "- filter some samples that have no (nan) answers before saving"
    ]
   },
   {
@@ -503,7 +504,7 @@
    ],
    "source": [
     "df = df[df['ground_truth']!=\"nan\"].reset_index(drop=True)\n",
-    "df"
+    "df.sample(5)"
    ]
   },
   {
@@ -516,10 +517,20 @@
     "df.to_csv(\"synthetic_test_dataset.csv\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "840a2213-7e50-4774-b702-1b0c82c54d4f",
+   "metadata": {},
+   "source": [
+    "Upnext we are going into dive into how to use this to evaluate your RAG.\n",
+    "\n",
+    "**If you liked this tutorial, checkout [ragas](https://github.com/explodinggradients/ragas) and consider leaving a star**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "58e5c4c8-47dc-4195-8332-453f96e1a6d2",
+   "id": "234a94b3-6527-47ee-af0c-cb1160da2c9b",
    "metadata": {},
    "outputs": [],
    "source": []