fix: repair notebook CI (dead model, missing API key, pyarrow type bug) (#348)

* fix: repair notebook CI by replacing dead vision model and adding missing API key

- Replace `meta/llama-4-scout-17b-16e-instruct` (no longer serving on
  build.nvidia.com) with `nvidia/nemotron-nano-12b-v2-vl` (project default)
  in tutorial notebook 4
- Add `OPENROUTER_API_KEY` to the `build-notebooks` workflow so notebooks
  5 and 6 (which use OpenRouter for image generation) can authenticate
- Regenerate colab notebooks to reflect the model change

* fix: handle pyarrow list types in notebook 6 display_image

When image columns are loaded from parquet with pyarrow backend,
list values are pyarrow ListScalars, not Python lists. The
isinstance(x, list) check fails, causing the whole ListScalar to be
treated as a single path string (producing filenames ending in
`png')]`). Use isinstance(x, str) instead to correctly handle any
iterable type.

Author: andreatgretel

.github/workflows/build-notebooks.yml

@@ -4,14 +4,15 @@ on:
   workflow_dispatch:
   schedule:
     - cron: "0 12 * * MON"
-  
+
 jobs:
   build:
     runs-on: ubuntu-latest
     permissions:
       contents: write
     env:
       NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
+      OPENROUTER_API_KEY: ${{ secrets.TEST_OPENROUTER_API_KEY }}
     steps:
       - name: Checkout repository
         uses: actions/checkout@v2

docs/colab_notebooks/1-the-basics.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f80a5317",
+   "id": "538dab5d",
    "metadata": {},
    "source": [
     "# 🎨 Data Designer Tutorial: The Basics\n",
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f94e1097",
+   "id": "8f148ac1",
    "metadata": {},
    "source": [
     "### 📦 Import Data Designer\n",
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "847f7d3b",
+   "id": "85a4f14c",
    "metadata": {},
    "source": [
     "### ⚡ Colab Setup\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ef72fc77",
+   "id": "8cb4fa30",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +48,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cfa5d568",
+   "id": "8c97cb88",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dc6a83bf",
+   "id": "192de987",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d2f7999",
+   "id": "09cc055e",
    "metadata": {},
    "source": [
     "### ⚙️ Initialize the Data Designer interface\n",
@@ -89,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a073af81",
+   "id": "64cd40d2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92c42c7d",
+   "id": "25827639",
    "metadata": {},
    "source": [
     "### 🎛️ Define model configurations\n",
@@ -115,7 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "45ae1dbf",
+   "id": "6ccc7929",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35d0b3af",
+   "id": "ffeb8a6e",
    "metadata": {},
    "source": [
     "### 🏗️ Initialize the Data Designer Config Builder\n",
@@ -160,7 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0c957c0b",
+   "id": "0f13f0dd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "132e10d6",
+   "id": "ba888091",
    "metadata": {},
    "source": [
     "## 🎲 Getting started with sampler columns\n",
@@ -186,7 +186,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b62b30fd",
+   "id": "9d831f16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04df17aa",
+   "id": "6bf9f07e",
    "metadata": {},
    "source": [
     "Let's start designing our product review dataset by adding product category and subcategory columns.\n"
@@ -204,7 +204,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cb62478e",
+   "id": "47236c2a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -285,7 +285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "889beb4b",
+   "id": "5fef86e3",
    "metadata": {},
    "source": [
     "Next, let's add samplers to generate data related to the customer and their review.\n"
@@ -294,7 +294,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e2746105",
+   "id": "6f4282c6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9fcf1a92",
+   "id": "09c6d0bd",
    "metadata": {},
    "source": [
     "## 🦜 LLM-generated columns\n",
@@ -346,7 +346,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6e6ac591",
+   "id": "b136c9c9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35332948",
+   "id": "77b21c85",
    "metadata": {},
    "source": [
     "### 🔁 Iteration is key – preview the dataset!\n",
@@ -399,7 +399,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e2830ad2",
+   "id": "9e7d8e57",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -409,7 +409,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "911fecd7",
+   "id": "35a3e198",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -420,7 +420,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "46faf8e9",
+   "id": "ac646977",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -430,7 +430,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3565f974",
+   "id": "135c82ff",
    "metadata": {},
    "source": [
     "### 📊 Analyze the generated data\n",
@@ -443,7 +443,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6effb2c0",
+   "id": "8b0290e0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,7 +453,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b63d3ec",
+   "id": "f780d07f",
    "metadata": {},
    "source": [
     "### 🆙 Scale up!\n",
@@ -466,7 +466,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0214e011",
+   "id": "e040f619",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -476,7 +476,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "11560d0f",
+   "id": "2beb335c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -489,7 +489,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "246f210c",
+   "id": "c72948ca",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -501,7 +501,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9f91c1d",
+   "id": "59725e56",
    "metadata": {},
    "source": [
     "## ⏭️ Next Steps\n",