Fix Colab button

Author: vishal-bala

python-recipes/vector-search/06_hnsw_to_svs_vamana_migration.ipynb

@@ -8,7 +8,7 @@
     "# Migrating from HNSW to SVS-VAMANA\n",
     "\n",
     "## Let's Begin!\n",
-    "<a href=\"https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/06_hnsw_to_svs_migration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/06_hnsw_to_svs_vamana_migration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "This notebook demonstrates how to migrate existing HNSW vector indices to SVS-VAMANA for improved memory efficiency while maintaining search quality.\n",
     "\n",
@@ -121,6 +121,43 @@
    ],
    "source": "%pip install \"redisvl>=0.11.0\" \"redis>=6.4.0\" \"numpy>=1.21.0\" \"sentence-transformers>=2.2.0\""
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "### Download Sample Data"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-12-09T15:18:18.087794Z",
+     "start_time": "2025-12-09T15:18:14.222219Z"
+    }
+   },
+   "cell_type": "code",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cloning into 'temp_repo'...\r\n",
+      "remote: Enumerating objects: 2879, done.\u001B[K\r\n",
+      "remote: Counting objects: 100% (1068/1068), done.\u001B[K\r\n",
+      "remote: Compressing objects: 100% (492/492), done.\u001B[K\r\n",
+      "remote: Total 2879 (delta 784), reused 733 (delta 574), pack-reused 1811 (from 2)\u001B[K\r\n",
+      "Receiving objects: 100% (2879/2879), 68.57 MiB | 34.97 MiB/s, done.\r\n",
+      "Resolving deltas: 100% (1649/1649), done.\r\n",
+      "mv: rename temp_repo/python-recipes/vector-search/resources to ./resources: Directory not empty\r\n"
+     ]
+    }
+   ],
+   "execution_count": 3,
+   "source": [
+    "# NBVAL_SKIP\n",
+    "!git clone https://github.com/redis-developer/redis-ai-resources.git temp_repo\n",
+    "!mv temp_repo/python-recipes/vector-search/resources .\n",
+    "!rm -rf temp_repo"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -283,38 +320,6 @@
     "We'll use the movie dataset to demonstrate the migration process."
    ]
   },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-12-09T15:18:18.087794Z",
-     "start_time": "2025-12-09T15:18:14.222219Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "# NBVAL_SKIP\n",
-    "!git clone https://github.com/redis-developer/redis-ai-resources.git temp_repo\n",
-    "!mv temp_repo/python-recipes/vector-search/resources .\n",
-    "!rm -rf temp_repo"
-   ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cloning into 'temp_repo'...\r\n",
-      "remote: Enumerating objects: 2879, done.\u001B[K\r\n",
-      "remote: Counting objects: 100% (1068/1068), done.\u001B[K\r\n",
-      "remote: Compressing objects: 100% (492/492), done.\u001B[K\r\n",
-      "remote: Total 2879 (delta 784), reused 733 (delta 574), pack-reused 1811 (from 2)\u001B[K\r\n",
-      "Receiving objects: 100% (2879/2879), 68.57 MiB | 34.97 MiB/s, done.\r\n",
-      "Resolving deltas: 100% (1649/1649), done.\r\n",
-      "mv: rename temp_repo/python-recipes/vector-search/resources to ./resources: Directory not empty\r\n"
-     ]
-    }
-   ],
-   "execution_count": 3
-  },
   {
    "cell_type": "code",
    "metadata": {

python-recipes/vector-search/07_flat_to_svs_vamana_migration.ipynb

@@ -8,7 +8,7 @@
     "# Migrating from FLAT to SVS-VAMANA\n",
     "\n",
     "## Let's Begin!\n",
-    "<a href=\"https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/06_svs_vamana_migration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/07_flat_to_svs_vamana_migration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "This notebook demonstrates how to migrate existing FLAT vector indices to SVS-VAMANA for improved memory efficiency and cost savings.\n",
     "\n",
@@ -98,6 +98,23 @@
    "outputs": [],
    "source": "%pip install \"redisvl>=0.11.0\" \"redis>=6.4.0\" \"numpy>=1.21.0\" \"sentence-transformers>=2.2.0\""
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "### Download Sample Data"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# NBVAL_SKIP\n",
+    "!git clone https://github.com/redis-developer/redis-ai-resources.git temp_repo\n",
+    "!mv temp_repo/python-recipes/vector-search/resources .\n",
+    "!rm -rf temp_repo"
+   ]
+  },
   {
    "metadata": {},
    "cell_type": "markdown",
@@ -247,18 +264,6 @@
     "For this demonstration, we'll create a sample FLAT index. In practice, you would analyze your existing index."
    ]
   },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": [
-    "# NBVAL_SKIP\n",
-    "!git clone https://github.com/redis-developer/redis-ai-resources.git temp_repo\n",
-    "!mv temp_repo/python-recipes/vector-search/resources .\n",
-    "!rm -rf temp_repo"
-   ]
-  },
   {
    "cell_type": "code",
    "metadata": {