diff --git a/EverNote_Documents_To_FAISS_Colab_GPU.ipynb b/EverNote_Documents_To_FAISS_Colab_GPU.ipynb
index a66fcbf..2565fd1 100644
--- a/EverNote_Documents_To_FAISS_Colab_GPU.ipynb
+++ b/EverNote_Documents_To_FAISS_Colab_GPU.ipynb
@@ -1264,7 +1264,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 22,
       "id": "e6ffc345c26298ad",
       "metadata": {
         "ExecuteTime": {
@@ -1273,7 +1273,7 @@
         },
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 49,
+          "height": 67,
           "referenced_widgets": [
             "592f37baf1c74e149577e80678db668f",
             "2cdb27f1d7b14b558cf6f19fc0ab4fd9",
@@ -1305,6 +1305,13 @@
           },
           "metadata": {},
           "output_type": "display_data"
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "All texts have been added to the database.\n"
+          ]
         }
       ],
       "source": [
@@ -1372,6 +1379,78 @@
         "vectorize_data_in_batches(chunks=text_chunks, embeddings=embeddings)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Similarity search"
+      ],
+      "metadata": {
+        "id": "WfjpAoJqE_L4"
+      },
+      "id": "WfjpAoJqE_L4"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "loaded_db = FAISS.load_local(\"faiss_index_cosine\", embeddings, distance_strategy=\"COSINE\", allow_dangerous_deserialization=True)"
+      ],
+      "metadata": {
+        "id": "nJG7eD8eFBnV"
+      },
+      "id": "nJG7eD8eFBnV",
+      "execution_count": 27,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results_with_scores = loaded_db.similarity_search_with_score(\"What is an exploit?\")"
+      ],
+      "metadata": {
+        "id": "Wwbebp6wF9fG"
+      },
+      "id": "Wwbebp6wF9fG",
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for doc, score in results_with_scores:\n",
+        "    print(f\"Content: {doc.page_content}, Score: {score}\")"
+      ],
+      "metadata": {
+        "id": "iWfiO0KFGZXN",
+        "outputId": "0dc2aed8-9689-4efa-e8e8-3d73bb81361b",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "iWfiO0KFGZXN",
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Content: `exploit', Score: 0.24956563115119934\n",
+            "Content: `exploit', Score: 0.24956563115119934\n",
+            "Content: `exploit', Score: 0.24956563115119934\n",
+            "Content: `exploit', Score: 0.24956563115119934\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Sandbox code - test area"
+      ],
+      "metadata": {
+        "id": "82gFVyrNCYOF"
+      },
+      "id": "82gFVyrNCYOF"
+    },
     {
       "cell_type": "code",
       "source": [
@@ -1381,17 +1460,19 @@
         "faiss = FAISS.from_texts(texts, embeddings, distance_strategy=\"COSINE\")\n",
         "print(type(faiss))\n",
         "\n",
-        "faiss.save_local(\"test\")"
+        "faiss.save_local(\"test\")\n",
+        "\n",
+        "new_db = FAISS.load_local(\"test\", embeddings, allow_dangerous_deserialization=True)\n"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "v6bhYHU5_9oo",
-        "outputId": "0fec4528-d10b-4a83-f5b5-c75476d2b4d1"
+        "outputId": "a88691e1-3ee4-4a34-edbf-4fac688dd78d"
       },
       "id": "v6bhYHU5_9oo",
-      "execution_count": 21,
+      "execution_count": 26,
       "outputs": [
         {
           "output_type": "stream",
@@ -1422,7 +1503,6 @@
     },
     "colab": {
       "provenance": [],
-      "toc_visible": true,
       "machine_shape": "hm",
       "gpuType": "V100"
     },
@@ -5231,7 +5311,7 @@
             "layout": "IPY_MODEL_0fbb5a8dd8c64e6c862779496a0c1867",
             "placeholder": "​",
             "style": "IPY_MODEL_8c77c7def1804fd6884a601c76618fa7",
-            "value": "Processing batches:  49%"
+            "value": "Processing batches: 100%"
           }
         },
         "ce8eed52d57c47479ab9a45b85296c04": {
@@ -5247,7 +5327,7 @@
             "_view_module": "@jupyter-widgets/controls",
             "_view_module_version": "1.5.0",
             "_view_name": "ProgressView",
-            "bar_style": "",
+            "bar_style": "success",
             "description": "",
             "description_tooltip": null,
             "layout": "IPY_MODEL_4f06cc3b83e641cd81deba9aaea93fbb",
@@ -5255,7 +5335,7 @@
             "min": 0,
             "orientation": "horizontal",
             "style": "IPY_MODEL_b0e999f6c752439a8f4ba962815160ae",
-            "value": 703
+            "value": 1448
           }
         },
         "a18c165ea7fc485c91e64df34974d685": {
@@ -5276,7 +5356,7 @@
             "layout": "IPY_MODEL_a18f27c970524c048b424be9672e106f",
             "placeholder": "​",
             "style": "IPY_MODEL_f6b2c8e5621143729c8d6e3129251f29",
-            "value": " 703/1448 [14:11<15:18,  1.23s/it]"
+            "value": " 1448/1448 [30:16<00:00,  1.23it/s]"
           }
         },
         "090543e0523a4d0e8dbd89e0152a3a15": {