diff --git a/notebooks/en/code_search.ipynb b/notebooks/en/code_search.ipynb
index 07b7e9a..c85d1d4 100644
--- a/notebooks/en/code_search.ipynb
+++ b/notebooks/en/code_search.ipynb
@@ -172,7 +172,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -180,33 +180,31 @@
         "id": "nZEBXstzQQCk",
         "outputId": "81d723e6-08c2-4a25-8b8e-508c4a7e86b1"
       },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "{'name': 'InvertedIndexRam',\n",
-              " 'signature': '# [doc = \" Inverted flatten index from dimension id to posting list\"] # [derive (Debug , Clone , PartialEq)] pub struct InvertedIndexRam { # [doc = \" Posting lists for each dimension flattened (dimension id -> posting list)\"] # [doc = \" Gaps are filled with empty posting lists\"] pub postings : Vec < PostingList > , # [doc = \" Number of unique indexed vectors\"] # [doc = \" pre-computed on build and upsert to avoid having to traverse the posting lists.\"] pub vector_count : usize , }',\n",
-              " 'code_type': 'Struct',\n",
-              " 'docstring': '= \" Inverted flatten index from dimension id to posting list\"',\n",
-              " 'line': 15,\n",
-              " 'line_from': 13,\n",
-              " 'line_to': 22,\n",
-              " 'context': {'module': 'inverted_index',\n",
-              "  'file_path': 'lib/sparse/src/index/inverted_index/inverted_index_ram.rs',\n",
-              "  'file_name': 'inverted_index_ram.rs',\n",
-              "  'struct_name': None,\n",
-              "  'snippet': '/// Inverted flatten index from dimension id to posting list\\n#[derive(Debug, Clone, PartialEq)]\\npub struct InvertedIndexRam {\\n    /// Posting lists for each dimension flattened (dimension id -> posting list)\\n    /// Gaps are filled with empty posting lists\\n    pub postings: Vec<PostingList>,\\n    /// Number of unique indexed vectors\\n    /// pre-computed on build and upsert to avoid having to traverse the posting lists.\\n    pub vector_count: usize,\\n}\\n'}}"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "structures[0]"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "```python\n",
+        "{'name': 'InvertedIndexRam',\n",
+        " 'signature': '# [doc = \" Inverted flatten index from dimension id to posting list\"] # [derive (Debug , Clone , PartialEq)] pub struct InvertedIndexRam { # [doc = \" Posting lists for each dimension flattened (dimension id -> posting list)\"] # [doc = \" Gaps are filled with empty posting lists\"] pub postings : Vec < PostingList > , # [doc = \" Number of unique indexed vectors\"] # [doc = \" pre-computed on build and upsert to avoid having to traverse the posting lists.\"] pub vector_count : usize , }',\n",
+        " 'code_type': 'Struct',\n",
+        " 'docstring': '= \" Inverted flatten index from dimension id to posting list\"',\n",
+        " 'line': 15,\n",
+        " 'line_from': 13,\n",
+        " 'line_to': 22,\n",
+        " 'context': {'module': 'inverted_index',\n",
+        "  'file_path': 'lib/sparse/src/index/inverted_index/inverted_index_ram.rs',\n",
+        "  'file_name': 'inverted_index_ram.rs',\n",
+        "  'struct_name': None,\n",
+        "  'snippet': '/// Inverted flatten index from dimension id to posting list\\n#[derive(Debug, Clone, PartialEq)]\\npub struct InvertedIndexRam {\\n    /// Posting lists for each dimension flattened (dimension id -> posting list)\\n    /// Gaps are filled with empty posting lists\\n    pub postings: Vec<PostingList>,\\n    /// Number of unique indexed vectors\\n    /// pre-computed on build and upsert to avoid having to traverse the posting lists.\\n    pub vector_count: usize,\\n}\\n'}}\n",
+        "  ```"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -314,7 +312,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -323,25 +321,20 @@
         "id": "zosN7TC9QQCl",
         "outputId": "38e0d938-3fb1-4426-f00c-74a42267bf7d"
       },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            },
-            "text/plain": [
-              "'Function Hnsw discover precision that does Checks discovery search precision when using hnsw index this is different from the tests in defined as Fn hnsw discover precision module integration file hnsw_discover_test rs'"
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "text_representations[1000]"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "```python\n",
+        "'Function Hnsw discover precision that does Checks discovery search precision when using hnsw index this is different from the tests in defined as Fn hnsw discover precision module integration file hnsw_discover_test rs'\n",
+        "```"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {