diff --git a/examples/notebooks/OpenAIChatExperiment.ipynb b/examples/notebooks/OpenAIChatExperiment.ipynb
index cf6ab6b..a0be254 100644
--- a/examples/notebooks/OpenAIChatExperiment.ipynb
+++ b/examples/notebooks/OpenAIChatExperiment.ipynb
@@ -169,7 +169,7 @@
        "      <td>0.0</td>\n",
        "      <td>[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]</td>\n",
        "      <td>George Washington</td>\n",
-       "      <td>2.917135e-06</td>\n",
+       "      <td>2.625049e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -177,7 +177,7 @@
        "      <td>1.0</td>\n",
        "      <td>[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]</td>\n",
        "      <td>George Washington</td>\n",
-       "      <td>1.291977e-06</td>\n",
+       "      <td>1.000008e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -185,7 +185,7 @@
        "      <td>0.0</td>\n",
        "      <td>[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]</td>\n",
        "      <td>George Washington</td>\n",
-       "      <td>8.328352e-07</td>\n",
+       "      <td>7.500057e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -193,7 +193,7 @@
        "      <td>1.0</td>\n",
        "      <td>[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]</td>\n",
        "      <td>George Washington</td>\n",
-       "      <td>7.499475e-07</td>\n",
+       "      <td>6.670016e-07</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -213,10 +213,10 @@
        "3  [{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]   \n",
        "\n",
        "            response       latency  \n",
-       "0  George Washington  2.917135e-06  \n",
-       "1  George Washington  1.291977e-06  \n",
-       "2  George Washington  8.328352e-07  \n",
-       "3  George Washington  7.499475e-07  "
+       "0  George Washington  2.625049e-06  \n",
+       "1  George Washington  1.000008e-06  \n",
+       "2  George Washington  7.500057e-07  \n",
+       "3  George Washington  6.670016e-07  "
       ]
      },
      "metadata": {},
@@ -224,204 +224,40 @@
     }
    ],
    "source": [
-    "experiment.visualize_table()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "4bb6b6d4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/kevin/miniconda3/envs/prompttools/lib/python3.11/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
-      "  if not hasattr(tensorboard, \"__version__\") or LooseVersion(\n",
-      "/Users/kevin/miniconda3/envs/prompttools/lib/python3.11/site-packages/torch/utils/tensorboard/__init__.py:6: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
-      "  ) < LooseVersion(\"1.15\"):\n",
-      "/Users/kevin/miniconda3/envs/prompttools/lib/python3.11/site-packages/tensorflow/python/debug/cli/debugger_cli_common.py:19: DeprecationWarning: module 'sre_constants' is deprecated\n",
-      "  import sre_constants\n"
-     ]
-    }
-   ],
-   "source": [
-    "from prompttools.utils import similarity\n",
-    "\n",
-    "experiment.evaluate_by_row(\"similar_to_expected\", similarity.semantic_similarity_row, expected=[\"George Washington\"] * 4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "07d4bd2a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>temperature</th>\n",
-       "      <th>messages</th>\n",
-       "      <th>response</th>\n",
-       "      <th>latency</th>\n",
-       "      <th>similar_to_expected</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>gpt-3.5-turbo</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]</td>\n",
-       "      <td>George Washington</td>\n",
-       "      <td>2.917135e-06</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>gpt-3.5-turbo</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]</td>\n",
-       "      <td>George Washington</td>\n",
-       "      <td>1.291977e-06</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>gpt-3.5-turbo-0613</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]</td>\n",
-       "      <td>George Washington</td>\n",
-       "      <td>8.328352e-07</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>gpt-3.5-turbo-0613</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]</td>\n",
-       "      <td>George Washington</td>\n",
-       "      <td>7.499475e-07</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                model  temperature  \\\n",
-       "0  gpt-3.5-turbo       0.0           \n",
-       "1  gpt-3.5-turbo       1.0           \n",
-       "2  gpt-3.5-turbo-0613  0.0           \n",
-       "3  gpt-3.5-turbo-0613  1.0           \n",
-       "\n",
-       "                                                                                                                       messages  \\\n",
-       "0  [{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]   \n",
-       "1  [{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]   \n",
-       "2  [{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]   \n",
-       "3  [{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]   \n",
-       "\n",
-       "            response       latency  similar_to_expected  \n",
-       "0  George Washington  2.917135e-06  1.0                  \n",
-       "1  George Washington  1.291977e-06  1.0                  \n",
-       "2  George Washington  8.328352e-07  1.0                  \n",
-       "3  George Washington  7.499475e-07  1.0                  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "experiment.visualize_table()"
+    "experiment.visualize()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "a508b623",
+   "cell_type": "markdown",
+   "id": "266c13eb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "experiment.aggregate_by_row(metric_name=\"latency\", column_name=\"model\")"
+    "## Evaluate the model response"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bde10f4b",
+   "cell_type": "markdown",
+   "id": "bebb8023",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "experiment.aggregate(metric_name=\"latency\", column_name=\"model\")"
+    "To evaluate the results, we'll define an eval function. We can use semantic distance to check if the model's response is similar to our expected output.\n",
+    "\n",
+    "Since we are using semantic similarity, you need to have the library `sentence_transformers` installed."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ae154478",
+   "id": "d861ab10",
    "metadata": {},
    "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "40f611d4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "266c13eb",
-   "metadata": {},
-   "source": [
-    "## Evaluate the model response"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bebb8023",
-   "metadata": {},
    "source": [
-    "To evaluate the results, we'll define an eval function. We can use semantic distance to check if the model's response is similar to our expected output."
+    "# !pip install --quiet sentence_transformers"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "id": "8ddbb951",
    "metadata": {},
    "outputs": [],
@@ -431,14 +267,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "id": "e80dfeec",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/kevin/miniconda3/envs/prompttools/lib/python3.11/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  if not hasattr(tensorboard, \"__version__\") or LooseVersion(\n",
+      "/Users/kevin/miniconda3/envs/prompttools/lib/python3.11/site-packages/torch/utils/tensorboard/__init__.py:6: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  ) < LooseVersion(\"1.15\"):\n"
+     ]
+    }
+   ],
    "source": [
-    "experiment.evaluate(\"similar_to_expected\", similarity.evaluate, expected=[\"George Washington\"] * 4)"
+    "experiment.evaluate(\"similar_to_expected\", similarity.semantic_similarity, expected=[\"George Washington\"] * 4)"
    ]
   },
   {
@@ -451,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "id": "4d09c18e",
    "metadata": {
     "scrolled": true
@@ -478,49 +325,49 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>temperature</th>\n",
        "      <th>messages</th>\n",
        "      <th>response</th>\n",
        "      <th>latency</th>\n",
        "      <th>similar_to_expected</th>\n",
-       "      <th>model</th>\n",
-       "      <th>temperature</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>gpt-3.5-turbo</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]</td>\n",
        "      <td>George Washington</td>\n",
-       "      <td>0.000007</td>\n",
+       "      <td>2.625049e-06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>gpt-3.5-turbo</td>\n",
-       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>gpt-3.5-turbo</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]</td>\n",
        "      <td>George Washington</td>\n",
-       "      <td>0.000004</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>gpt-3.5-turbo</td>\n",
+       "      <td>1.000008e-06</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
+       "      <td>gpt-3.5-turbo-0613</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]</td>\n",
        "      <td>George Washington</td>\n",
-       "      <td>0.000003</td>\n",
+       "      <td>7.500057e-07</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>gpt-3.5-turbo-0613</td>\n",
-       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
+       "      <td>gpt-3.5-turbo-0613</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]</td>\n",
        "      <td>George Washington</td>\n",
-       "      <td>0.000002</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>gpt-3.5-turbo-0613</td>\n",
+       "      <td>6.670016e-07</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -528,23 +375,23 @@
        "</div>"
       ],
       "text/plain": [
+       "                model  temperature  \\\n",
+       "0  gpt-3.5-turbo       0.0           \n",
+       "1  gpt-3.5-turbo       1.0           \n",
+       "2  gpt-3.5-turbo-0613  0.0           \n",
+       "3  gpt-3.5-turbo-0613  1.0           \n",
+       "\n",
        "                                                                                                                       messages  \\\n",
        "0  [{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]   \n",
        "1  [{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]   \n",
        "2  [{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]   \n",
        "3  [{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who was the first president?'}]   \n",
        "\n",
-       "            response   latency  similar_to_expected               model  \\\n",
-       "0  George Washington  0.000007  1.0                  gpt-3.5-turbo        \n",
-       "1  George Washington  0.000004  1.0                  gpt-3.5-turbo        \n",
-       "2  George Washington  0.000003  1.0                  gpt-3.5-turbo-0613   \n",
-       "3  George Washington  0.000002  1.0                  gpt-3.5-turbo-0613   \n",
-       "\n",
-       "   temperature  \n",
-       "0  0.0          \n",
-       "1  1.0          \n",
-       "2  0.0          \n",
-       "3  1.0          "
+       "            response       latency  similar_to_expected  \n",
+       "0  George Washington  2.625049e-06  1.0                  \n",
+       "1  George Washington  1.000008e-06  1.0                  \n",
+       "2  George Washington  7.500057e-07  1.0                  \n",
+       "3  George Washington  6.670016e-07  1.0                  "
       ]
      },
      "metadata": {},
@@ -557,13 +404,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "d0007a1f",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]