diff --git a/notebooks/benchmarks/vector_database_comparison_mongodb_postgreSQL.ipynb b/notebooks/benchmarks/vector_database_comparison_mongodb_postgreSQL.ipynb
index 9336008..db3680d 100644
--- a/notebooks/benchmarks/vector_database_comparison_mongodb_postgreSQL.ipynb
+++ b/notebooks/benchmarks/vector_database_comparison_mongodb_postgreSQL.ipynb
@@ -9,6 +9,8 @@
         "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mongodb-developer/GenAI-Showcase/blob/main/notebooks/benchmarks/vector_database_comparison_mongodb_postgreSQL.ipynb)\n",
         "-----\n",
         "\n",
+        "[![YouTube](https://img.shields.io/badge/YouTube-Watch%20Video-red)](https://www.youtube.com/live/6NGqgRzOT8E?si=ujPa31IDwlsGQCYU)\n",
+        "\n",
         "This notebook implements and benchmarks a standard AI workload that involves vector embeddings and the retreival of semantically similar documents from a database. The system uses two different vector databases:\n",
         "- PostgreSQL with pgvector: A vector database extension for PostgreSQL that enables vector search on the database.\n",
         "- MongoDB Atlas Vector Search: A vector search feature for MongoDB Database that enables vector search on the database.\n"
@@ -487,455 +489,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "data": {
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-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>When was YouTube officially launched, and by whom?</th>\n",
-              "      <th>What is YouTube's slogan introduced after Google's acquisition?</th>\n",
-              "      <th>How many hours of videos are collectively watched on YouTube daily?</th>\n",
-              "      <th>Which was the first video uploaded to YouTube, and when was it uploaded?</th>\n",
-              "      <th>What was the acquisition cost of YouTube by Google, and when was the deal finalized?</th>\n",
-              "      <th>What was the first YouTube video to reach one million views, and when did it happen?</th>\n",
-              "      <th>What are the three separate branches of the United States government?</th>\n",
-              "      <th>Which country has the highest documented incarceration rate and prison population?</th>\n",
-              "      <th>How many executions have occurred in the United States since 1977, and which countries have more?</th>\n",
-              "      <th>What percentage of the global military spending did the United States account for in 2019?</th>\n",
-              "      <th>How is the U.S. president elected?</th>\n",
-              "      <th>What cooling system innovation was included in the proposed venues for the World Cup in Qatar?</th>\n",
-              "      <th>What lawsuit was filed against Google in June 2020, and what was it about?</th>\n",
-              "      <th>How much was Google fined by CNIL in January 2022, and for what reason?</th>\n",
-              "      <th>When did YouTube join the NSA's PRISM program, according to reports?</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>0.184692</td>\n",
-              "      <td>0.181519</td>\n",
-              "      <td>0.124939</td>\n",
-              "      <td>0.226074</td>\n",
-              "      <td>0.415771</td>\n",
-              "      <td>0.127075</td>\n",
-              "      <td>0.237915</td>\n",
-              "      <td>0.524902</td>\n",
-              "      <td>0.396484</td>\n",
-              "      <td>0.342529</td>\n",
-              "      <td>0.375488</td>\n",
-              "      <td>0.490234</td>\n",
-              "      <td>0.118774</td>\n",
-              "      <td>0.138428</td>\n",
-              "      <td>0.477783</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>0.262451</td>\n",
-              "      <td>0.019638</td>\n",
-              "      <td>0.022446</td>\n",
-              "      <td>0.130493</td>\n",
-              "      <td>0.049530</td>\n",
-              "      <td>0.195923</td>\n",
-              "      <td>0.418945</td>\n",
-              "      <td>-0.256348</td>\n",
-              "      <td>-0.112061</td>\n",
-              "      <td>-0.033020</td>\n",
-              "      <td>0.158081</td>\n",
-              "      <td>0.331055</td>\n",
-              "      <td>0.199219</td>\n",
-              "      <td>-0.065125</td>\n",
-              "      <td>0.128540</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>-0.314697</td>\n",
-              "      <td>0.134399</td>\n",
-              "      <td>0.049500</td>\n",
-              "      <td>-0.228027</td>\n",
-              "      <td>0.077454</td>\n",
-              "      <td>-0.062683</td>\n",
-              "      <td>0.057587</td>\n",
-              "      <td>-0.576660</td>\n",
-              "      <td>0.051880</td>\n",
-              "      <td>-0.189575</td>\n",
-              "      <td>-0.151855</td>\n",
-              "      <td>-0.245117</td>\n",
-              "      <td>-0.053436</td>\n",
-              "      <td>-0.451172</td>\n",
-              "      <td>-0.274658</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>0.098145</td>\n",
-              "      <td>0.179077</td>\n",
-              "      <td>0.499756</td>\n",
-              "      <td>0.089600</td>\n",
-              "      <td>0.000246</td>\n",
-              "      <td>0.063354</td>\n",
-              "      <td>0.011215</td>\n",
-              "      <td>0.008095</td>\n",
-              "      <td>0.014961</td>\n",
-              "      <td>-0.099731</td>\n",
-              "      <td>-0.213135</td>\n",
-              "      <td>0.257812</td>\n",
-              "      <td>0.398926</td>\n",
-              "      <td>0.274170</td>\n",
-              "      <td>0.223389</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>-0.058105</td>\n",
-              "      <td>0.311035</td>\n",
-              "      <td>0.307373</td>\n",
-              "      <td>0.076355</td>\n",
-              "      <td>0.416260</td>\n",
-              "      <td>0.398682</td>\n",
-              "      <td>-0.243042</td>\n",
-              "      <td>0.160278</td>\n",
-              "      <td>-0.098389</td>\n",
-              "      <td>0.036224</td>\n",
-              "      <td>-0.036987</td>\n",
-              "      <td>-0.351318</td>\n",
-              "      <td>0.334229</td>\n",
-              "      <td>0.256836</td>\n",
-              "      <td>0.176514</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>763</th>\n",
-              "      <td>0.455078</td>\n",
-              "      <td>0.499756</td>\n",
-              "      <td>0.428711</td>\n",
-              "      <td>0.464600</td>\n",
-              "      <td>0.513184</td>\n",
-              "      <td>0.464600</td>\n",
-              "      <td>0.291504</td>\n",
-              "      <td>0.306641</td>\n",
-              "      <td>0.328613</td>\n",
-              "      <td>0.367188</td>\n",
-              "      <td>0.238281</td>\n",
-              "      <td>0.497070</td>\n",
-              "      <td>0.384521</td>\n",
-              "      <td>0.507324</td>\n",
-              "      <td>0.436523</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>764</th>\n",
-              "      <td>0.267334</td>\n",
-              "      <td>0.156128</td>\n",
-              "      <td>0.244629</td>\n",
-              "      <td>0.340088</td>\n",
-              "      <td>0.174561</td>\n",
-              "      <td>0.487549</td>\n",
-              "      <td>0.038269</td>\n",
-              "      <td>0.058563</td>\n",
-              "      <td>0.609375</td>\n",
-              "      <td>0.080261</td>\n",
-              "      <td>-0.004959</td>\n",
-              "      <td>0.007370</td>\n",
-              "      <td>0.403564</td>\n",
-              "      <td>0.544922</td>\n",
-              "      <td>-0.016052</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>765</th>\n",
-              "      <td>0.260254</td>\n",
-              "      <td>0.039520</td>\n",
-              "      <td>-0.106445</td>\n",
-              "      <td>0.114807</td>\n",
-              "      <td>0.439697</td>\n",
-              "      <td>0.103333</td>\n",
-              "      <td>0.186890</td>\n",
-              "      <td>0.013084</td>\n",
-              "      <td>0.176880</td>\n",
-              "      <td>0.337646</td>\n",
-              "      <td>0.200439</td>\n",
-              "      <td>-0.355469</td>\n",
-              "      <td>0.326660</td>\n",
-              "      <td>0.316895</td>\n",
-              "      <td>0.338135</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>766</th>\n",
-              "      <td>0.001040</td>\n",
-              "      <td>0.315430</td>\n",
-              "      <td>0.045349</td>\n",
-              "      <td>0.113403</td>\n",
-              "      <td>0.190552</td>\n",
-              "      <td>-0.038116</td>\n",
-              "      <td>0.299072</td>\n",
-              "      <td>0.256348</td>\n",
-              "      <td>0.316650</td>\n",
-              "      <td>0.420410</td>\n",
-              "      <td>0.342529</td>\n",
-              "      <td>0.062683</td>\n",
-              "      <td>0.035522</td>\n",
-              "      <td>0.094238</td>\n",
-              "      <td>0.180420</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>767</th>\n",
-              "      <td>0.117249</td>\n",
-              "      <td>0.250244</td>\n",
-              "      <td>0.145020</td>\n",
-              "      <td>0.096802</td>\n",
-              "      <td>0.245605</td>\n",
-              "      <td>-0.057373</td>\n",
-              "      <td>-0.201904</td>\n",
-              "      <td>-0.123169</td>\n",
-              "      <td>-0.248047</td>\n",
-              "      <td>-0.034363</td>\n",
-              "      <td>0.039124</td>\n",
-              "      <td>-0.110229</td>\n",
-              "      <td>0.125610</td>\n",
-              "      <td>0.179321</td>\n",
-              "      <td>-0.066345</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>768 rows × 15 columns</p>\n",
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-            ],
-            "text/plain": [
-              "     When was YouTube officially launched, and by whom?  \\\n",
-              "0                                             0.184692    \n",
-              "1                                             0.262451    \n",
-              "2                                            -0.314697    \n",
-              "3                                             0.098145    \n",
-              "4                                            -0.058105    \n",
-              "..                                                 ...    \n",
-              "763                                           0.455078    \n",
-              "764                                           0.267334    \n",
-              "765                                           0.260254    \n",
-              "766                                           0.001040    \n",
-              "767                                           0.117249    \n",
-              "\n",
-              "     What is YouTube's slogan introduced after Google's acquisition?  \\\n",
-              "0                                             0.181519                 \n",
-              "1                                             0.019638                 \n",
-              "2                                             0.134399                 \n",
-              "3                                             0.179077                 \n",
-              "4                                             0.311035                 \n",
-              "..                                                 ...                 \n",
-              "763                                           0.499756                 \n",
-              "764                                           0.156128                 \n",
-              "765                                           0.039520                 \n",
-              "766                                           0.315430                 \n",
-              "767                                           0.250244                 \n",
-              "\n",
-              "     How many hours of videos are collectively watched on YouTube daily?  \\\n",
-              "0                                             0.124939                     \n",
-              "1                                             0.022446                     \n",
-              "2                                             0.049500                     \n",
-              "3                                             0.499756                     \n",
-              "4                                             0.307373                     \n",
-              "..                                                 ...                     \n",
-              "763                                           0.428711                     \n",
-              "764                                           0.244629                     \n",
-              "765                                          -0.106445                     \n",
-              "766                                           0.045349                     \n",
-              "767                                           0.145020                     \n",
-              "\n",
-              "     Which was the first video uploaded to YouTube, and when was it uploaded?  \\\n",
-              "0                                             0.226074                          \n",
-              "1                                             0.130493                          \n",
-              "2                                            -0.228027                          \n",
-              "3                                             0.089600                          \n",
-              "4                                             0.076355                          \n",
-              "..                                                 ...                          \n",
-              "763                                           0.464600                          \n",
-              "764                                           0.340088                          \n",
-              "765                                           0.114807                          \n",
-              "766                                           0.113403                          \n",
-              "767                                           0.096802                          \n",
-              "\n",
-              "     What was the acquisition cost of YouTube by Google, and when was the deal finalized?  \\\n",
-              "0                                             0.415771                                      \n",
-              "1                                             0.049530                                      \n",
-              "2                                             0.077454                                      \n",
-              "3                                             0.000246                                      \n",
-              "4                                             0.416260                                      \n",
-              "..                                                 ...                                      \n",
-              "763                                           0.513184                                      \n",
-              "764                                           0.174561                                      \n",
-              "765                                           0.439697                                      \n",
-              "766                                           0.190552                                      \n",
-              "767                                           0.245605                                      \n",
-              "\n",
-              "     What was the first YouTube video to reach one million views, and when did it happen?  \\\n",
-              "0                                             0.127075                                      \n",
-              "1                                             0.195923                                      \n",
-              "2                                            -0.062683                                      \n",
-              "3                                             0.063354                                      \n",
-              "4                                             0.398682                                      \n",
-              "..                                                 ...                                      \n",
-              "763                                           0.464600                                      \n",
-              "764                                           0.487549                                      \n",
-              "765                                           0.103333                                      \n",
-              "766                                          -0.038116                                      \n",
-              "767                                          -0.057373                                      \n",
-              "\n",
-              "     What are the three separate branches of the United States government?  \\\n",
-              "0                                             0.237915                       \n",
-              "1                                             0.418945                       \n",
-              "2                                             0.057587                       \n",
-              "3                                             0.011215                       \n",
-              "4                                            -0.243042                       \n",
-              "..                                                 ...                       \n",
-              "763                                           0.291504                       \n",
-              "764                                           0.038269                       \n",
-              "765                                           0.186890                       \n",
-              "766                                           0.299072                       \n",
-              "767                                          -0.201904                       \n",
-              "\n",
-              "     Which country has the highest documented incarceration rate and prison population?  \\\n",
-              "0                                             0.524902                                    \n",
-              "1                                            -0.256348                                    \n",
-              "2                                            -0.576660                                    \n",
-              "3                                             0.008095                                    \n",
-              "4                                             0.160278                                    \n",
-              "..                                                 ...                                    \n",
-              "763                                           0.306641                                    \n",
-              "764                                           0.058563                                    \n",
-              "765                                           0.013084                                    \n",
-              "766                                           0.256348                                    \n",
-              "767                                          -0.123169                                    \n",
-              "\n",
-              "     How many executions have occurred in the United States since 1977, and which countries have more?  \\\n",
-              "0                                             0.396484                                                   \n",
-              "1                                            -0.112061                                                   \n",
-              "2                                             0.051880                                                   \n",
-              "3                                             0.014961                                                   \n",
-              "4                                            -0.098389                                                   \n",
-              "..                                                 ...                                                   \n",
-              "763                                           0.328613                                                   \n",
-              "764                                           0.609375                                                   \n",
-              "765                                           0.176880                                                   \n",
-              "766                                           0.316650                                                   \n",
-              "767                                          -0.248047                                                   \n",
-              "\n",
-              "     What percentage of the global military spending did the United States account for in 2019?  \\\n",
-              "0                                             0.342529                                            \n",
-              "1                                            -0.033020                                            \n",
-              "2                                            -0.189575                                            \n",
-              "3                                            -0.099731                                            \n",
-              "4                                             0.036224                                            \n",
-              "..                                                 ...                                            \n",
-              "763                                           0.367188                                            \n",
-              "764                                           0.080261                                            \n",
-              "765                                           0.337646                                            \n",
-              "766                                           0.420410                                            \n",
-              "767                                          -0.034363                                            \n",
-              "\n",
-              "     How is the U.S. president elected?  \\\n",
-              "0                              0.375488   \n",
-              "1                              0.158081   \n",
-              "2                             -0.151855   \n",
-              "3                             -0.213135   \n",
-              "4                             -0.036987   \n",
-              "..                                  ...   \n",
-              "763                            0.238281   \n",
-              "764                           -0.004959   \n",
-              "765                            0.200439   \n",
-              "766                            0.342529   \n",
-              "767                            0.039124   \n",
-              "\n",
-              "     What cooling system innovation was included in the proposed venues for the World Cup in Qatar?  \\\n",
-              "0                                             0.490234                                                \n",
-              "1                                             0.331055                                                \n",
-              "2                                            -0.245117                                                \n",
-              "3                                             0.257812                                                \n",
-              "4                                            -0.351318                                                \n",
-              "..                                                 ...                                                \n",
-              "763                                           0.497070                                                \n",
-              "764                                           0.007370                                                \n",
-              "765                                          -0.355469                                                \n",
-              "766                                           0.062683                                                \n",
-              "767                                          -0.110229                                                \n",
-              "\n",
-              "     What lawsuit was filed against Google in June 2020, and what was it about?  \\\n",
-              "0                                             0.118774                            \n",
-              "1                                             0.199219                            \n",
-              "2                                            -0.053436                            \n",
-              "3                                             0.398926                            \n",
-              "4                                             0.334229                            \n",
-              "..                                                 ...                            \n",
-              "763                                           0.384521                            \n",
-              "764                                           0.403564                            \n",
-              "765                                           0.326660                            \n",
-              "766                                           0.035522                            \n",
-              "767                                           0.125610                            \n",
-              "\n",
-              "     How much was Google fined by CNIL in January 2022, and for what reason?  \\\n",
-              "0                                             0.138428                         \n",
-              "1                                            -0.065125                         \n",
-              "2                                            -0.451172                         \n",
-              "3                                             0.274170                         \n",
-              "4                                             0.256836                         \n",
-              "..                                                 ...                         \n",
-              "763                                           0.507324                         \n",
-              "764                                           0.544922                         \n",
-              "765                                           0.316895                         \n",
-              "766                                           0.094238                         \n",
-              "767                                           0.179321                         \n",
-              "\n",
-              "     When did YouTube join the NSA's PRISM program, according to reports?  \n",
-              "0                                             0.477783                     \n",
-              "1                                             0.128540                     \n",
-              "2                                            -0.274658                     \n",
-              "3                                             0.223389                     \n",
-              "4                                             0.176514                     \n",
-              "..                                                 ...                     \n",
-              "763                                           0.436523                     \n",
-              "764                                          -0.016052                     \n",
-              "765                                           0.338135                     \n",
-              "766                                           0.180420                     \n",
-              "767                                          -0.066345                     \n",
-              "\n",
-              "[768 rows x 15 columns]"
-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "# View the first 5 query embeddings as a dataframe\n",
         "pd.DataFrame(query_embeddings_dict)"
@@ -2603,6 +2159,2780 @@
     }
   ],
   "metadata": {
+    "cells": [
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "# Vector Database Comparison For AI Workloads\n",
+          "-----\n",
+          "\n",
+          "This notebook implements and benchmarks a standard AI workload that involves vector embeddings and the retreival of semantically similar documents from a database. The system uses two different vector databases:\n",
+          "- PostgreSQL with pgvector: A vector database extension for PostgreSQL that enables vector search on the database.\n",
+          "- MongoDB Atlas Vector Search: A vector search feature for MongoDB Database that enables vector search on the database.\n"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "## Key Information\n",
+          "\n",
+          "1. **System Configuration**\n",
+          "\n",
+          "| Component | Specification |\n",
+          "|-----------|---------------|\n",
+          "| **Hardware** ||\n",
+          "| System | MacBook Pro 16-inch, 2023 |\n",
+          "| CPU | Apple M2 Pro |\n",
+          "| Memory | 16GB |\n",
+          "| **Database Tools** ||\n",
+          "| PostgreSQL Management | pgAdmin 4 (v8.12) |\n",
+          "| MongoDB Management | MongoDB Compass (v1.41.0) |\n",
+          "| **Database Versions** ||\n",
+          "| PostgreSQL | 15.4 with pgvector extension (Single Node) |\n",
+          "| - Index Configuration | HNSW index with m=16, ef_construction=64 and cosine similarity |\n",
+          "| MongoDB | 8.0.1 with Atlas CLI (Single Node) |\n",
+          "| - Index Configuration | Vector Search Index (HNSW) with 768 dimensions and cosine similarity |\n",
+          "| **Container Environment** ||\n",
+          "| Docker Desktop | 4.34.3 |\n",
+          "| Docker Engine | 27.2.0 |\n",
+          "| Docker Compose | v2.29.2-desktop.2 |\n",
+          "| Kubernetes | v1.30.2 |\n",
+          "| **Docker Resource Pool** ||\n",
+          "| Total CPU | 12 cores |\n",
+          "| Total Memory | 7.9 GB |\n",
+          "| Total Swap | 1 GB |\n",
+          "| Virtual Disk | 64 GB |\n",
+          "| Container Memory Limit | 7.65GB |\n",
+          "| **Development Environment** ||\n",
+          "| Python | 3.11.5 |\n",
+          "| Pip | 24.2 |\n",
+          "| IDE | Jupyter Notebook |\n",
+          "| Operating System | macOS Sonoma 14.2.1 |\n",
+          "2. **Data Processing**\n",
+          "   - Uses Wikipedia dataset (100,000 entries) with embeddings(Precision: float32, Dimensions: 768) generated by Cohere\n",
+          "   - JSON data is generated from the dataset and stored in the databases\n",
+          "   - Stores data in both PostgreSQL and MongoDB\n",
+          "\n",
+          "3. **Performance Testing**\n",
+          "   - Tests different sizes of concurrent queries (1-400 queries)\n",
+          "   - Tests different insertion batch sizes and speed of insertion\n",
+          "\n",
+          "| Operation  | Metric | Description |\n",
+          "|------------|--------|-------------|\n",
+          "| Insertion  | Latency | Time taken to insert the data (average response time) |\n",
+          "|            | Throughput | Number of queries processed per second |\n",
+          "| Retrieval  | Latency | Time taken to retrieve the top n results (average response time) |\n",
+          "|            | Throughput | Number of queries processed per second |\n",
+          "|            | P95 Latency | Time taken to retrieve the top n results for 95% of the queries |\n",
+          "|            | Standard Deviation | How much the latency varies from the average latency |\n",
+          "\n",
+          "4. **Results Visualization**\n",
+          "   - Interactive animations showing request-response cycles\n",
+          "   - Comparative charts for latency and throughput\n",
+          "   - Performance analysis across different batch sizes"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "## Part 1: Data Setup"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "Setting up the benchmark results dictionary `benchmark_results` and the batch sizes to test `CONCURRENT_QUERIES` and `TOTAL_QUERIES`\n",
+          "\n",
+          "- `benchmark_results` is a dictionary that will store the results of the benchmark tests\n",
+          "- `CONCURRENT_QUERIES` is a list of the number of queries that are run concurrently\n",
+          "- `TOTAL_QUERIES` is the total number of queries that are run\n",
+          "\n",
+          "Benchmark Configuration Example:\n",
+          "When testing with a concurrency level of 10:\n",
+          "- We run 100 iterations\n",
+          "- Each iteration runs 10 concurrent queries\n",
+          "- Total queries = 1,000 queries (TOTAL_ITERATIONS * CONCURRENT_QUERIES)\n",
+          "\n",
+          "NOTE: For each concurrency level in CONCURRENT_QUERIES:\n",
+          "1. Run TOTAL_QUERIES iterations\n",
+          "2. In each iteration, execute that many concurrent queries\n",
+          "3. Measure and collect latencies for all queries\n",
+          "\n"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 1,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "# Initialize the benchmark results dictionary\n",
+          "benchmark_results = {\"PostgreSQL\": {}, \"MongoDB\": {}}\n",
+          "\n",
+          "# The concurrency levels for benchmark testing\n",
+          "# Each level represents the number of simultaneous queries to execute\n",
+          "CONCURRENT_QUERIES = [\n",
+          "    1,\n",
+          "    2,\n",
+          "    4,\n",
+          "    5,\n",
+          "    8,\n",
+          "    10,\n",
+          "    12,\n",
+          "    16,\n",
+          "    20,\n",
+          "    24,\n",
+          "    32,\n",
+          "    40,\n",
+          "    48,\n",
+          "    50,\n",
+          "    56,\n",
+          "    64,\n",
+          "    72,\n",
+          "    80,\n",
+          "    88,\n",
+          "    96,\n",
+          "    100,\n",
+          "    200,\n",
+          "    400,\n",
+          "]\n",
+          "\n",
+          "# The total number of iterations to run for each concurrency level\n",
+          "TOTAL_QUERIES = 100"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 2,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "import getpass\n",
+          "import os\n",
+          "\n",
+          "\n",
+          "# Function to securely get and set environment variables\n",
+          "def set_env_securely(var_name, prompt):\n",
+          "    value = getpass.getpass(prompt)\n",
+          "    os.environ[var_name] = value"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 1: Install Libraries\n",
+          "\n",
+          "All the libraries are installed using pip and facilitate the sourcing of data, embedding generation, and data visualization.\n",
+          "\n",
+          "- `datasets`: Hugging Face library for managing and preprocessing datasets across text, image, and audio (https://huggingface.co/datasets)\n",
+          "- `sentence_transformers`: For creating sentence embeddings for tasks like semantic search and clustering. (https://www.sbert.net/)\n",
+          "- `pandas`: A library for data manipulation and analysis with DataFrames and Series (https://pandas.pydata.org/)\n",
+          "- `matplotlib`: A library for creating static, interactive, and animated data visualizations (https://matplotlib.org/)\n",
+          "- `seaborn`: A library for creating statistical data visualizations (https://seaborn.pydata.org/)\n",
+          "- `cohere`: A library for generating embeddings and accessing the Cohere API or models (https://cohere.ai/)"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 11,
+        "metadata": {},
+        "outputs": [
+          {
+            "name": "stdout",
+            "output_type": "stream",
+            "text": [
+              "\n",
+              "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
+              "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+            ]
+          }
+        ],
+        "source": [
+          "%pip install --upgrade --quiet datasets sentence_transformers pandas matplotlib seaborn cohere"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {
+          "id": "LyWZVRIqxVnV"
+        },
+        "source": [
+          "### Step 2: Data Loading\n",
+          "\n",
+          "The dataset for the benchmark is sourced from the Hugging Face Cohere Wikipedia dataset.\n",
+          "\n",
+          "The [Cohere/wikipedia-22-12-en-embeddings](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings) dataset on Hugging Face comprises English Wikipedia articles embedded using Cohere's multilingual-22-12 model. Each entry includes the article's title, text, URL, Wikipedia ID, view count, paragraph ID, language codes, and a 768-dimensional embedding vector. This dataset is valuable for tasks like semantic search, information retrieval, and NLP model training.\n",
+          "\n",
+          "For this benchmark, we are using 100,000 rows of the dataset and have removed the id, wiki_id, paragraph_id, langs and views columns."
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": null,
+        "metadata": {
+          "colab": {
+            "base_uri": "https://localhost:8080/",
+            "height": 206
+          },
+          "id": "rCXZhJ9-vymv",
+          "outputId": "7f4210d7-5b8b-4c1d-89ab-7288623a8ca7"
+        },
+        "outputs": [],
+        "source": [
+          "import pandas as pd\n",
+          "from datasets import load_dataset\n",
+          "\n",
+          "# Using 100,000 rows for testing, feel free to change this to any number of rows you want to test\n",
+          "# The wikipedia-22-12-en-embeddings dataset has approximately 35,000,000 rows and requires 120GB of memory to load\n",
+          "MAX_ROWS = 100000\n",
+          "\n",
+          "dataset = load_dataset(\n",
+          "    \"Cohere/wikipedia-22-12-en-embeddings\", split=\"train\", streaming=True\n",
+          ")\n",
+          "dataset_segment = dataset.take(MAX_ROWS)\n",
+          "\n",
+          "# Convert the dataset to a pandas dataframe\n",
+          "dataset_df = pd.DataFrame(dataset_segment)"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 4,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "# Add a JSON attribute to the dataset consisting of the title, text and url\n",
+          "dataset_df[\"json_data\"] = dataset_df.apply(\n",
+          "    lambda row: {\"title\": row[\"title\"], \"text\": row[\"text\"], \"url\": row[\"url\"]}, axis=1\n",
+          ")"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 5,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "# Remove the id field, wiki_id, paragraph_id, langs and views from the dataset\n",
+          "# This is to replicate the structure of dataset usually encountered in AI workloads, particularly in RAG systems where metadata is extracted from documents and stored.\n",
+          "dataset_df = dataset_df.drop(\n",
+          "    columns=[\"id\", \"wiki_id\", \"paragraph_id\", \"langs\", \"views\"]\n",
+          ")"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 6,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "# Change the emb colomn name to embedding\n",
+          "dataset_df = dataset_df.rename(columns={\"emb\": \"embedding\"})"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 7,
+        "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>title</th>\n",
+                "      <th>text</th>\n",
+                "      <th>url</th>\n",
+                "      <th>embedding</th>\n",
+                "      <th>json_data</th>\n",
+                "    </tr>\n",
+                "  </thead>\n",
+                "  <tbody>\n",
+                "    <tr>\n",
+                "      <th>0</th>\n",
+                "      <td>Deaths in 2022</td>\n",
+                "      <td>The following notable deaths occurred in 2022....</td>\n",
+                "      <td>https://en.wikipedia.org/wiki?curid=69407798</td>\n",
+                "      <td>[0.2865696847438812, -0.03181683272123337, 0.0...</td>\n",
+                "      <td>{'title': 'Deaths in 2022', 'text': 'The follo...</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>1</th>\n",
+                "      <td>YouTube</td>\n",
+                "      <td>YouTube is a global online video sharing and s...</td>\n",
+                "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
+                "      <td>[-0.09689381718635559, 0.1619211882352829, -0....</td>\n",
+                "      <td>{'title': 'YouTube', 'text': 'YouTube is a glo...</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>2</th>\n",
+                "      <td>YouTube</td>\n",
+                "      <td>In October 2006, YouTube was bought by Google ...</td>\n",
+                "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
+                "      <td>[0.1302049309015274, 0.265736848115921, 0.4018...</td>\n",
+                "      <td>{'title': 'YouTube', 'text': 'In October 2006,...</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>3</th>\n",
+                "      <td>YouTube</td>\n",
+                "      <td>Since its purchase by Google, YouTube has expa...</td>\n",
+                "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
+                "      <td>[-0.09791257232427597, 0.13586106896400452, -0...</td>\n",
+                "      <td>{'title': 'YouTube', 'text': 'Since its purcha...</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>4</th>\n",
+                "      <td>YouTube</td>\n",
+                "      <td>YouTube has had an unprecedented social impact...</td>\n",
+                "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
+                "      <td>[-0.2641527056694031, 0.06968216598033905, -0....</td>\n",
+                "      <td>{'title': 'YouTube', 'text': 'YouTube has had ...</td>\n",
+                "    </tr>\n",
+                "  </tbody>\n",
+                "</table>\n",
+                "</div>"
+              ],
+              "text/plain": [
+                "            title                                               text  \\\n",
+                "0  Deaths in 2022  The following notable deaths occurred in 2022....   \n",
+                "1         YouTube  YouTube is a global online video sharing and s...   \n",
+                "2         YouTube  In October 2006, YouTube was bought by Google ...   \n",
+                "3         YouTube  Since its purchase by Google, YouTube has expa...   \n",
+                "4         YouTube  YouTube has had an unprecedented social impact...   \n",
+                "\n",
+                "                                            url  \\\n",
+                "0  https://en.wikipedia.org/wiki?curid=69407798   \n",
+                "1   https://en.wikipedia.org/wiki?curid=3524766   \n",
+                "2   https://en.wikipedia.org/wiki?curid=3524766   \n",
+                "3   https://en.wikipedia.org/wiki?curid=3524766   \n",
+                "4   https://en.wikipedia.org/wiki?curid=3524766   \n",
+                "\n",
+                "                                           embedding  \\\n",
+                "0  [0.2865696847438812, -0.03181683272123337, 0.0...   \n",
+                "1  [-0.09689381718635559, 0.1619211882352829, -0....   \n",
+                "2  [0.1302049309015274, 0.265736848115921, 0.4018...   \n",
+                "3  [-0.09791257232427597, 0.13586106896400452, -0...   \n",
+                "4  [-0.2641527056694031, 0.06968216598033905, -0....   \n",
+                "\n",
+                "                                           json_data  \n",
+                "0  {'title': 'Deaths in 2022', 'text': 'The follo...  \n",
+                "1  {'title': 'YouTube', 'text': 'YouTube is a glo...  \n",
+                "2  {'title': 'YouTube', 'text': 'In October 2006,...  \n",
+                "3  {'title': 'YouTube', 'text': 'Since its purcha...  \n",
+                "4  {'title': 'YouTube', 'text': 'YouTube has had ...  "
+              ]
+            },
+            "execution_count": 7,
+            "metadata": {},
+            "output_type": "execute_result"
+          }
+        ],
+        "source": [
+          "dataset_df.head(5)"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {
+          "id": "wOwXzXnjxHfb"
+        },
+        "source": [
+          "### Step 3: Embedding Generation"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 8,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "# Set Cohere API key\n",
+          "set_env_securely(\"COHERE_API_KEY\", \"Enter your Cohere API key: \")"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "Using the Cohere API to generate embeddings for the test queries.\n",
+          "\n",
+          "Using the `embed-multilingual-v2.0` model. This is the same model used in the Cohere Wikipedia dataset.\n",
+          "\n",
+          "Embedding size is 768 dimensions and the precision is float32."
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 9,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "from typing import List, Tuple\n",
+          "\n",
+          "import cohere\n",
+          "\n",
+          "# Initialize Cohere Client\n",
+          "co = cohere.Client()\n",
+          "\n",
+          "\n",
+          "def get_cohere_embeddings(\n",
+          "    sentences: List[str],\n",
+          "    model: str = \"embed-multilingual-v2.0\",\n",
+          "    input_type: str = \"search_document\",\n",
+          ") -> Tuple[List[float], List[int]]:\n",
+          "    \"\"\"\n",
+          "    Generates embeddings for the provided sentences using Cohere's embedding model.\n",
+          "\n",
+          "    Args:\n",
+          "    sentences (list of str): List of sentences to generate embeddings for.\n",
+          "\n",
+          "    Returns:\n",
+          "    Tuple[List[float], List[int]]: A tuple containing two lists of embeddings (float and int8).\n",
+          "    \"\"\"\n",
+          "    generated_embedding = co.embed(\n",
+          "        texts=sentences,\n",
+          "        model=\"embed-multilingual-v2.0\",\n",
+          "        input_type=\"search_document\",\n",
+          "        embedding_types=[\"float\"],\n",
+          "    ).embeddings\n",
+          "\n",
+          "    return generated_embedding.float[0]"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "Generate embeddings for the query templates used in benchmarking process\n",
+          "\n",
+          "Note: Doing this to avoid the overhead of generating embeddings for each query during the benchmark process\n",
+          "\n",
+          "Note: Feel free to add more queries to the query_templates list to test the performance of the vector database with a larger number of queries"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 10,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "query_templates = [\n",
+          "    \"When was YouTube officially launched, and by whom?\",\n",
+          "    \"What is YouTube's slogan introduced after Google's acquisition?\",\n",
+          "    \"How many hours of videos are collectively watched on YouTube daily?\",\n",
+          "    \"Which was the first video uploaded to YouTube, and when was it uploaded?\",\n",
+          "    \"What was the acquisition cost of YouTube by Google, and when was the deal finalized?\",\n",
+          "    \"What was the first YouTube video to reach one million views, and when did it happen?\",\n",
+          "    \"What are the three separate branches of the United States government?\",\n",
+          "    \"Which country has the highest documented incarceration rate and prison population?\",\n",
+          "    \"How many executions have occurred in the United States since 1977, and which countries have more?\",\n",
+          "    \"What percentage of the global military spending did the United States account for in 2019?\",\n",
+          "    \"How is the U.S. president elected?\",\n",
+          "    \"What cooling system innovation was included in the proposed venues for the World Cup in Qatar?\",\n",
+          "    \"What lawsuit was filed against Google in June 2020, and what was it about?\",\n",
+          "    \"How much was Google fined by CNIL in January 2022, and for what reason?\",\n",
+          "    \"When did YouTube join the NSA's PRISM program, according to reports?\",\n",
+          "]\n",
+          "\n",
+          "# For each query template question, generate an embedding\n",
+          "# NOTE: Doing this to avoid the overhead of generating embeddings for each query during the benchmark process\n",
+          "query_embeddings = [\n",
+          "    get_cohere_embeddings(sentences=[query], input_type=\"search_query\")\n",
+          "    for query in query_templates\n",
+          "]"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 11,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "# Create a dictionary with the query templates and their corresponding embeddings\n",
+          "query_embeddings_dict = {\n",
+          "    query: embedding for query, embedding in zip(query_templates, query_embeddings)\n",
+          "}"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 12,
+        "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>When was YouTube officially launched, and by whom?</th>\n",
+                "      <th>What is YouTube's slogan introduced after Google's acquisition?</th>\n",
+                "      <th>How many hours of videos are collectively watched on YouTube daily?</th>\n",
+                "      <th>Which was the first video uploaded to YouTube, and when was it uploaded?</th>\n",
+                "      <th>What was the acquisition cost of YouTube by Google, and when was the deal finalized?</th>\n",
+                "      <th>What was the first YouTube video to reach one million views, and when did it happen?</th>\n",
+                "      <th>What are the three separate branches of the United States government?</th>\n",
+                "      <th>Which country has the highest documented incarceration rate and prison population?</th>\n",
+                "      <th>How many executions have occurred in the United States since 1977, and which countries have more?</th>\n",
+                "      <th>What percentage of the global military spending did the United States account for in 2019?</th>\n",
+                "      <th>How is the U.S. president elected?</th>\n",
+                "      <th>What cooling system innovation was included in the proposed venues for the World Cup in Qatar?</th>\n",
+                "      <th>What lawsuit was filed against Google in June 2020, and what was it about?</th>\n",
+                "      <th>How much was Google fined by CNIL in January 2022, and for what reason?</th>\n",
+                "      <th>When did YouTube join the NSA's PRISM program, according to reports?</th>\n",
+                "    </tr>\n",
+                "  </thead>\n",
+                "  <tbody>\n",
+                "    <tr>\n",
+                "      <th>0</th>\n",
+                "      <td>0.184692</td>\n",
+                "      <td>0.181519</td>\n",
+                "      <td>0.124939</td>\n",
+                "      <td>0.226074</td>\n",
+                "      <td>0.415771</td>\n",
+                "      <td>0.127075</td>\n",
+                "      <td>0.237915</td>\n",
+                "      <td>0.524902</td>\n",
+                "      <td>0.396484</td>\n",
+                "      <td>0.342529</td>\n",
+                "      <td>0.375488</td>\n",
+                "      <td>0.490234</td>\n",
+                "      <td>0.118774</td>\n",
+                "      <td>0.138428</td>\n",
+                "      <td>0.477783</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>1</th>\n",
+                "      <td>0.262451</td>\n",
+                "      <td>0.019638</td>\n",
+                "      <td>0.022446</td>\n",
+                "      <td>0.130493</td>\n",
+                "      <td>0.049530</td>\n",
+                "      <td>0.195923</td>\n",
+                "      <td>0.418945</td>\n",
+                "      <td>-0.256348</td>\n",
+                "      <td>-0.112061</td>\n",
+                "      <td>-0.033020</td>\n",
+                "      <td>0.158081</td>\n",
+                "      <td>0.331055</td>\n",
+                "      <td>0.199219</td>\n",
+                "      <td>-0.065125</td>\n",
+                "      <td>0.128540</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>2</th>\n",
+                "      <td>-0.314697</td>\n",
+                "      <td>0.134399</td>\n",
+                "      <td>0.049500</td>\n",
+                "      <td>-0.228027</td>\n",
+                "      <td>0.077454</td>\n",
+                "      <td>-0.062683</td>\n",
+                "      <td>0.057587</td>\n",
+                "      <td>-0.576660</td>\n",
+                "      <td>0.051880</td>\n",
+                "      <td>-0.189575</td>\n",
+                "      <td>-0.151855</td>\n",
+                "      <td>-0.245117</td>\n",
+                "      <td>-0.053436</td>\n",
+                "      <td>-0.451172</td>\n",
+                "      <td>-0.274658</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>3</th>\n",
+                "      <td>0.098145</td>\n",
+                "      <td>0.179077</td>\n",
+                "      <td>0.499756</td>\n",
+                "      <td>0.089600</td>\n",
+                "      <td>0.000246</td>\n",
+                "      <td>0.063354</td>\n",
+                "      <td>0.011215</td>\n",
+                "      <td>0.008095</td>\n",
+                "      <td>0.014961</td>\n",
+                "      <td>-0.099731</td>\n",
+                "      <td>-0.213135</td>\n",
+                "      <td>0.257812</td>\n",
+                "      <td>0.398926</td>\n",
+                "      <td>0.274170</td>\n",
+                "      <td>0.223389</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>4</th>\n",
+                "      <td>-0.058105</td>\n",
+                "      <td>0.311035</td>\n",
+                "      <td>0.307373</td>\n",
+                "      <td>0.076355</td>\n",
+                "      <td>0.416260</td>\n",
+                "      <td>0.398682</td>\n",
+                "      <td>-0.243042</td>\n",
+                "      <td>0.160278</td>\n",
+                "      <td>-0.098389</td>\n",
+                "      <td>0.036224</td>\n",
+                "      <td>-0.036987</td>\n",
+                "      <td>-0.351318</td>\n",
+                "      <td>0.334229</td>\n",
+                "      <td>0.256836</td>\n",
+                "      <td>0.176514</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>...</th>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "      <td>...</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>763</th>\n",
+                "      <td>0.455078</td>\n",
+                "      <td>0.499756</td>\n",
+                "      <td>0.428711</td>\n",
+                "      <td>0.464600</td>\n",
+                "      <td>0.513184</td>\n",
+                "      <td>0.464600</td>\n",
+                "      <td>0.291504</td>\n",
+                "      <td>0.306641</td>\n",
+                "      <td>0.328613</td>\n",
+                "      <td>0.367188</td>\n",
+                "      <td>0.238281</td>\n",
+                "      <td>0.497070</td>\n",
+                "      <td>0.384521</td>\n",
+                "      <td>0.507324</td>\n",
+                "      <td>0.436523</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>764</th>\n",
+                "      <td>0.267334</td>\n",
+                "      <td>0.156128</td>\n",
+                "      <td>0.244629</td>\n",
+                "      <td>0.340088</td>\n",
+                "      <td>0.174561</td>\n",
+                "      <td>0.487549</td>\n",
+                "      <td>0.038269</td>\n",
+                "      <td>0.058563</td>\n",
+                "      <td>0.609375</td>\n",
+                "      <td>0.080261</td>\n",
+                "      <td>-0.004959</td>\n",
+                "      <td>0.007370</td>\n",
+                "      <td>0.403564</td>\n",
+                "      <td>0.544922</td>\n",
+                "      <td>-0.016052</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>765</th>\n",
+                "      <td>0.260254</td>\n",
+                "      <td>0.039520</td>\n",
+                "      <td>-0.106445</td>\n",
+                "      <td>0.114807</td>\n",
+                "      <td>0.439697</td>\n",
+                "      <td>0.103333</td>\n",
+                "      <td>0.186890</td>\n",
+                "      <td>0.013084</td>\n",
+                "      <td>0.176880</td>\n",
+                "      <td>0.337646</td>\n",
+                "      <td>0.200439</td>\n",
+                "      <td>-0.355469</td>\n",
+                "      <td>0.326660</td>\n",
+                "      <td>0.316895</td>\n",
+                "      <td>0.338135</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>766</th>\n",
+                "      <td>0.001040</td>\n",
+                "      <td>0.315430</td>\n",
+                "      <td>0.045349</td>\n",
+                "      <td>0.113403</td>\n",
+                "      <td>0.190552</td>\n",
+                "      <td>-0.038116</td>\n",
+                "      <td>0.299072</td>\n",
+                "      <td>0.256348</td>\n",
+                "      <td>0.316650</td>\n",
+                "      <td>0.420410</td>\n",
+                "      <td>0.342529</td>\n",
+                "      <td>0.062683</td>\n",
+                "      <td>0.035522</td>\n",
+                "      <td>0.094238</td>\n",
+                "      <td>0.180420</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>767</th>\n",
+                "      <td>0.117249</td>\n",
+                "      <td>0.250244</td>\n",
+                "      <td>0.145020</td>\n",
+                "      <td>0.096802</td>\n",
+                "      <td>0.245605</td>\n",
+                "      <td>-0.057373</td>\n",
+                "      <td>-0.201904</td>\n",
+                "      <td>-0.123169</td>\n",
+                "      <td>-0.248047</td>\n",
+                "      <td>-0.034363</td>\n",
+                "      <td>0.039124</td>\n",
+                "      <td>-0.110229</td>\n",
+                "      <td>0.125610</td>\n",
+                "      <td>0.179321</td>\n",
+                "      <td>-0.066345</td>\n",
+                "    </tr>\n",
+                "  </tbody>\n",
+                "</table>\n",
+                "<p>768 rows × 15 columns</p>\n",
+                "</div>"
+              ],
+              "text/plain": [
+                "     When was YouTube officially launched, and by whom?  \\\n",
+                "0                                             0.184692    \n",
+                "1                                             0.262451    \n",
+                "2                                            -0.314697    \n",
+                "3                                             0.098145    \n",
+                "4                                            -0.058105    \n",
+                "..                                                 ...    \n",
+                "763                                           0.455078    \n",
+                "764                                           0.267334    \n",
+                "765                                           0.260254    \n",
+                "766                                           0.001040    \n",
+                "767                                           0.117249    \n",
+                "\n",
+                "     What is YouTube's slogan introduced after Google's acquisition?  \\\n",
+                "0                                             0.181519                 \n",
+                "1                                             0.019638                 \n",
+                "2                                             0.134399                 \n",
+                "3                                             0.179077                 \n",
+                "4                                             0.311035                 \n",
+                "..                                                 ...                 \n",
+                "763                                           0.499756                 \n",
+                "764                                           0.156128                 \n",
+                "765                                           0.039520                 \n",
+                "766                                           0.315430                 \n",
+                "767                                           0.250244                 \n",
+                "\n",
+                "     How many hours of videos are collectively watched on YouTube daily?  \\\n",
+                "0                                             0.124939                     \n",
+                "1                                             0.022446                     \n",
+                "2                                             0.049500                     \n",
+                "3                                             0.499756                     \n",
+                "4                                             0.307373                     \n",
+                "..                                                 ...                     \n",
+                "763                                           0.428711                     \n",
+                "764                                           0.244629                     \n",
+                "765                                          -0.106445                     \n",
+                "766                                           0.045349                     \n",
+                "767                                           0.145020                     \n",
+                "\n",
+                "     Which was the first video uploaded to YouTube, and when was it uploaded?  \\\n",
+                "0                                             0.226074                          \n",
+                "1                                             0.130493                          \n",
+                "2                                            -0.228027                          \n",
+                "3                                             0.089600                          \n",
+                "4                                             0.076355                          \n",
+                "..                                                 ...                          \n",
+                "763                                           0.464600                          \n",
+                "764                                           0.340088                          \n",
+                "765                                           0.114807                          \n",
+                "766                                           0.113403                          \n",
+                "767                                           0.096802                          \n",
+                "\n",
+                "     What was the acquisition cost of YouTube by Google, and when was the deal finalized?  \\\n",
+                "0                                             0.415771                                      \n",
+                "1                                             0.049530                                      \n",
+                "2                                             0.077454                                      \n",
+                "3                                             0.000246                                      \n",
+                "4                                             0.416260                                      \n",
+                "..                                                 ...                                      \n",
+                "763                                           0.513184                                      \n",
+                "764                                           0.174561                                      \n",
+                "765                                           0.439697                                      \n",
+                "766                                           0.190552                                      \n",
+                "767                                           0.245605                                      \n",
+                "\n",
+                "     What was the first YouTube video to reach one million views, and when did it happen?  \\\n",
+                "0                                             0.127075                                      \n",
+                "1                                             0.195923                                      \n",
+                "2                                            -0.062683                                      \n",
+                "3                                             0.063354                                      \n",
+                "4                                             0.398682                                      \n",
+                "..                                                 ...                                      \n",
+                "763                                           0.464600                                      \n",
+                "764                                           0.487549                                      \n",
+                "765                                           0.103333                                      \n",
+                "766                                          -0.038116                                      \n",
+                "767                                          -0.057373                                      \n",
+                "\n",
+                "     What are the three separate branches of the United States government?  \\\n",
+                "0                                             0.237915                       \n",
+                "1                                             0.418945                       \n",
+                "2                                             0.057587                       \n",
+                "3                                             0.011215                       \n",
+                "4                                            -0.243042                       \n",
+                "..                                                 ...                       \n",
+                "763                                           0.291504                       \n",
+                "764                                           0.038269                       \n",
+                "765                                           0.186890                       \n",
+                "766                                           0.299072                       \n",
+                "767                                          -0.201904                       \n",
+                "\n",
+                "     Which country has the highest documented incarceration rate and prison population?  \\\n",
+                "0                                             0.524902                                    \n",
+                "1                                            -0.256348                                    \n",
+                "2                                            -0.576660                                    \n",
+                "3                                             0.008095                                    \n",
+                "4                                             0.160278                                    \n",
+                "..                                                 ...                                    \n",
+                "763                                           0.306641                                    \n",
+                "764                                           0.058563                                    \n",
+                "765                                           0.013084                                    \n",
+                "766                                           0.256348                                    \n",
+                "767                                          -0.123169                                    \n",
+                "\n",
+                "     How many executions have occurred in the United States since 1977, and which countries have more?  \\\n",
+                "0                                             0.396484                                                   \n",
+                "1                                            -0.112061                                                   \n",
+                "2                                             0.051880                                                   \n",
+                "3                                             0.014961                                                   \n",
+                "4                                            -0.098389                                                   \n",
+                "..                                                 ...                                                   \n",
+                "763                                           0.328613                                                   \n",
+                "764                                           0.609375                                                   \n",
+                "765                                           0.176880                                                   \n",
+                "766                                           0.316650                                                   \n",
+                "767                                          -0.248047                                                   \n",
+                "\n",
+                "     What percentage of the global military spending did the United States account for in 2019?  \\\n",
+                "0                                             0.342529                                            \n",
+                "1                                            -0.033020                                            \n",
+                "2                                            -0.189575                                            \n",
+                "3                                            -0.099731                                            \n",
+                "4                                             0.036224                                            \n",
+                "..                                                 ...                                            \n",
+                "763                                           0.367188                                            \n",
+                "764                                           0.080261                                            \n",
+                "765                                           0.337646                                            \n",
+                "766                                           0.420410                                            \n",
+                "767                                          -0.034363                                            \n",
+                "\n",
+                "     How is the U.S. president elected?  \\\n",
+                "0                              0.375488   \n",
+                "1                              0.158081   \n",
+                "2                             -0.151855   \n",
+                "3                             -0.213135   \n",
+                "4                             -0.036987   \n",
+                "..                                  ...   \n",
+                "763                            0.238281   \n",
+                "764                           -0.004959   \n",
+                "765                            0.200439   \n",
+                "766                            0.342529   \n",
+                "767                            0.039124   \n",
+                "\n",
+                "     What cooling system innovation was included in the proposed venues for the World Cup in Qatar?  \\\n",
+                "0                                             0.490234                                                \n",
+                "1                                             0.331055                                                \n",
+                "2                                            -0.245117                                                \n",
+                "3                                             0.257812                                                \n",
+                "4                                            -0.351318                                                \n",
+                "..                                                 ...                                                \n",
+                "763                                           0.497070                                                \n",
+                "764                                           0.007370                                                \n",
+                "765                                          -0.355469                                                \n",
+                "766                                           0.062683                                                \n",
+                "767                                          -0.110229                                                \n",
+                "\n",
+                "     What lawsuit was filed against Google in June 2020, and what was it about?  \\\n",
+                "0                                             0.118774                            \n",
+                "1                                             0.199219                            \n",
+                "2                                            -0.053436                            \n",
+                "3                                             0.398926                            \n",
+                "4                                             0.334229                            \n",
+                "..                                                 ...                            \n",
+                "763                                           0.384521                            \n",
+                "764                                           0.403564                            \n",
+                "765                                           0.326660                            \n",
+                "766                                           0.035522                            \n",
+                "767                                           0.125610                            \n",
+                "\n",
+                "     How much was Google fined by CNIL in January 2022, and for what reason?  \\\n",
+                "0                                             0.138428                         \n",
+                "1                                            -0.065125                         \n",
+                "2                                            -0.451172                         \n",
+                "3                                             0.274170                         \n",
+                "4                                             0.256836                         \n",
+                "..                                                 ...                         \n",
+                "763                                           0.507324                         \n",
+                "764                                           0.544922                         \n",
+                "765                                           0.316895                         \n",
+                "766                                           0.094238                         \n",
+                "767                                           0.179321                         \n",
+                "\n",
+                "     When did YouTube join the NSA's PRISM program, according to reports?  \n",
+                "0                                             0.477783                     \n",
+                "1                                             0.128540                     \n",
+                "2                                            -0.274658                     \n",
+                "3                                             0.223389                     \n",
+                "4                                             0.176514                     \n",
+                "..                                                 ...                     \n",
+                "763                                           0.436523                     \n",
+                "764                                          -0.016052                     \n",
+                "765                                           0.338135                     \n",
+                "766                                           0.180420                     \n",
+                "767                                          -0.066345                     \n",
+                "\n",
+                "[768 rows x 15 columns]"
+              ]
+            },
+            "execution_count": 12,
+            "metadata": {},
+            "output_type": "execute_result"
+          }
+        ],
+        "source": [
+          "# View the first 5 query embeddings as a dataframe\n",
+          "pd.DataFrame(query_embeddings_dict)"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "## Part 2: Semantic Search with PostgreSQL and PgVector\n",
+          "\n",
+          "In this section, we create a PostgreSQL database with the PgVector extension and insert the dataset into the database.\n",
+          "\n",
+          "The table `wikipedia_data` is created with the following columns:\n",
+          "- `id`: The unique identifier for each row\n",
+          "- `title`: The title of the Wikipedia article\n",
+          "- `text`: The text of the Wikipedia article\n",
+          "- `url`: The URL of the Wikipedia article\n",
+          "- `json_data`: The JSON data of the Wikipedia article\n",
+          "- `embedding`: The embedding vector for the Wikipedia article\n",
+          "\n",
+          "The table is created with a HNSW index with m=16, ef_construction=64 and cosine similarity (these are the default parameters for the HNSW index in pgvector).\n",
+          "- `HNSW`: Hierarchical Navigable Small World graphs are a type of graph-based index that are used for efficient similarity search.\n",
+          "- `m=16`: The number of edges per node in the graph\n",
+          "- `ef_construction=64`: Short for exploration factor construction, is the number of edges to build during the index construction phase\n",
+          "- `ef_search=100`: Short for exploration factor search, is the number of edges to search during the index search phase\n",
+          "- `cosine similarity`: The similarity metric used for the index (formula: dot product(A, B) / (|A||B|))\n",
+          "- `cosine distance`: The distance metric calculated using cosine similarity (1 - cosine similarity)\n",
+          "\n",
+          "We perform a semantic search on the database using a single data point of the query templates and their corresponding embeddings.\n"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {
+          "id": "0PM-dnDtxQBW"
+        },
+        "source": [
+          "### Step 1: Install Libraries\n",
+          "\n",
+          "- `pgvector` (0.3.6): A PostgreSQL extension for vector similarity search (https://github.com/pgvector/pgvector)\n",
+          "- `psycopg` (3.2.3): A PostgreSQL database adapter for Python (https://www.psycopg.org/)\n"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": null,
+        "metadata": {
+          "colab": {
+            "base_uri": "https://localhost:8080/"
+          },
+          "id": "A5GwoxiSfWgv",
+          "outputId": "157e4760-e25c-45cd-d1d5-3eb3fdfea593"
+        },
+        "outputs": [],
+        "source": [
+          "%pip install --upgrade --quiet pgvector \"psycopg[binary]\""
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 2: Installing PostgreSQL and PgVector\n",
+          "\n",
+          "PostgreSQL and PgVector are installed using docker.\n",
+          "\n",
+          "The PostgreSQL docker image is pulled from the [postgres](https://hub.docker.com/_/postgres) repository.\n",
+          "\n",
+          "The PgVector docker image is pulled from the [pgvector/pgvector](https://hub.docker.com/r/pgvector/pgvector) repository.\n",
+          "\n",
+          "Find more instructions on installing PostgreSQL and PgVector [here](https://github.com/pgvector/pgvector?tab=readme-ov-file#docker): "
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 3: Create Postgres Table\n",
+          "\n",
+          "- `id`: The unique identifier for each row\n",
+          "- `title`: The title of the Wikipedia article\n",
+          "- `text`: The text of the Wikipedia article\n",
+          "- `url`: The URL of the Wikipedia article\n",
+          "- `json_data`: The JSON data of the Wikipedia article\n",
+          "- `embedding`: The embedding vector for the Wikipedia article\n",
+          "\n",
+          "NOTE: JSON data `json_data` in the dataset is stored as a JSONB column in Postgres to mirror the use of binary formatted data in MongoDB via BSON."
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": null,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "def create_table(connection):\n",
+          "    with connection.cursor() as cur:\n",
+          "        # Drop table if it already exists\n",
+          "        cur.execute(\"DROP TABLE IF EXISTS wikipedia_data\")\n",
+          "\n",
+          "        # Create the table with the appropriate structure\n",
+          "        cur.execute(\n",
+          "            \"\"\"\n",
+          "            CREATE TABLE wikipedia_data (\n",
+          "                id bigserial PRIMARY KEY,\n",
+          "                title text,\n",
+          "                text text,\n",
+          "                url text,\n",
+          "                json_data jsonb,\n",
+          "                embedding vector(768)\n",
+          "            )\n",
+          "        \"\"\"\n",
+          "        )\n",
+          "\n",
+          "        # Create HNSW index for vector similarity search with cosine similarity\n",
+          "        cur.execute(\n",
+          "            \"\"\"\n",
+          "            CREATE INDEX ON wikipedia_data \n",
+          "            USING hnsw (embedding vector_cosine_ops) \n",
+          "            WITH (m = 16, ef_construction = 64);\n",
+          "        \"\"\"\n",
+          "        )\n",
+          "\n",
+          "        print(\"Table and index created successfully\")\n",
+          "        connection.commit()"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 4: Define insert function\n",
+          "\n",
+          "For inserting JSON data, we convert the Python Dictionary in the `json_data` attribute to a JSON string using the `json.dumps()` function.\n",
+          "\n",
+          "This is a serilization process that converts the Python Dictionary in the `json_data` attribute to a JSON string that is stored as binary data in the database."
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 16,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "import json\n",
+          "import time\n",
+          "\n",
+          "import numpy as np\n",
+          "\n",
+          "\n",
+          "def insert_data_to_postgres(dataframe, connection, database_type=\"PostgreSQL\"):\n",
+          "    \"\"\"\n",
+          "    Insert data into the PostgreSQL database.\n",
+          "\n",
+          "    Args:\n",
+          "    dataframe (pandas.DataFrame): The dataframe containing the data to insert.\n",
+          "    connection (psycopg.extensions.connection): The connection to the PostgreSQL database.\n",
+          "    database_type (str): The type of database (default: \"PostgreSQL\").\n",
+          "    \"\"\"\n",
+          "    start_time = time.time()\n",
+          "    total_rows = len(dataframe)\n",
+          "\n",
+          "    try:\n",
+          "        with connection.cursor() as cur:\n",
+          "            # Create a list of tuples for insertion, filtering out rows with invalid embeddings\n",
+          "            data_tuples = []\n",
+          "            for _, row in dataframe.iterrows():\n",
+          "                data_tuple = (\n",
+          "                    row[\"title\"],\n",
+          "                    row[\"text\"],\n",
+          "                    row[\"url\"],\n",
+          "                    json.dumps(row[\"json_data\"]),  # Convert dict to JSON string\n",
+          "                    row[\"embedding\"],\n",
+          "                )\n",
+          "                data_tuples.append(data_tuple)\n",
+          "\n",
+          "            if not data_tuples:\n",
+          "                raise ValueError(\"No valid data tuples to insert\")\n",
+          "\n",
+          "            cur.executemany(\n",
+          "                \"\"\"\n",
+          "                INSERT INTO wikipedia_data \n",
+          "                (title, text, url, json_data, embedding)\n",
+          "                VALUES (%s, %s, %s, %s, %s)\n",
+          "                \"\"\",\n",
+          "                data_tuples,\n",
+          "            )\n",
+          "\n",
+          "            connection.commit()\n",
+          "\n",
+          "    except Exception as e:\n",
+          "        print(f\"Error during bulk insert: {e}\")\n",
+          "        connection.rollback()\n",
+          "        raise e\n",
+          "\n",
+          "    end_time = time.time()\n",
+          "    total_time = end_time - start_time\n",
+          "    rows_per_second = len(data_tuples) / total_time\n",
+          "\n",
+          "    # print(f\"\\nInsertion Statistics:\")\n",
+          "    # print(f\"Total time: {total_time:.2f} seconds\")\n",
+          "    # print(f\"Average insertion rate: {rows_per_second:.2f} rows/second\")\n",
+          "    # print(f\"Total rows inserted: {len(data_tuples)}\")\n",
+          "    # print(f\"Rows skipped: {total_rows - len(data_tuples)}\")\n",
+          "\n",
+          "    # Store results in benchmark dictionary\n",
+          "    if database_type not in benchmark_results:\n",
+          "        benchmark_results[database_type] = {}\n",
+          "\n",
+          "    benchmark_results[database_type][\"insert_time\"] = {\n",
+          "        \"total_time\": total_time,\n",
+          "        \"rows_per_second\": rows_per_second,\n",
+          "        \"total_rows\": total_rows,\n",
+          "    }"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 5: Insert Data into Postgres"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 20,
+        "metadata": {},
+        "outputs": [
+          {
+            "name": "stdout",
+            "output_type": "stream",
+            "text": [
+              "Table and index created successfully\n",
+              "\n",
+              "Insertion Statistics:\n",
+              "Total time: 462.10 seconds\n",
+              "Average insertion rate: 216.40 rows/second\n",
+              "Total rows inserted: 100000\n",
+              "Rows skipped: 0\n",
+              "Connection closed\n"
+            ]
+          }
+        ],
+        "source": [
+          "import psycopg\n",
+          "from pgvector.psycopg import register_vector\n",
+          "\n",
+          "try:\n",
+          "    # Connect to PostgreSQL\n",
+          "    conn = psycopg.connect(\n",
+          "        \"dbname=vector_db user=postgres password=test host=127.0.0.1\"\n",
+          "    )\n",
+          "\n",
+          "    # Enable the pgvector extension\n",
+          "    conn.execute(\"CREATE EXTENSION IF NOT EXISTS vector\")\n",
+          "\n",
+          "    # Register vector type to handle embedding data as vector data types\n",
+          "    register_vector(conn)\n",
+          "\n",
+          "    # Step 1: Create the table\n",
+          "    create_table(conn)\n",
+          "\n",
+          "    # Step 2: Insert the expanded dataset into the table\n",
+          "    insert_data_to_postgres(dataset_df, conn)\n",
+          "\n",
+          "except Exception as e:\n",
+          "    print(\"Failed to execute:\", e)\n",
+          "finally:\n",
+          "    # Close the connection\n",
+          "    conn.close()\n",
+          "    print(\"Connection closed\")"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 6: Define semantic search function\n",
+          "\n",
+          "To avoid exhasuting API key usage, we will fetch the query embedding from the `query_embeddings_dict` dictionary.\n",
+          "\n",
+          "In the `semantic_search_with_postgres` function, we set the HNSW ef parameter to 100 using the `execute_command` function.\n",
+          "\n",
+          "This is to set the exploration factor for the HNSW index to 100. And corresponds to the number of nodes/candidates to search during the index search phase.\n",
+          "A node corresponds to a vector in the index.\n"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 75,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "def semantic_search_with_postgres(query, connection, top_n=5):\n",
+          "    # Take a query embedding from the query_embeddings_dict\n",
+          "    query_embedding = query_embeddings_dict[query]\n",
+          "\n",
+          "    with connection.cursor() as cur:\n",
+          "        # Set the HNSW ef parameter using execute_command\n",
+          "        cur.execute(\"SET hnsw.ef_search = 100\")\n",
+          "        connection.commit()\n",
+          "\n",
+          "        # Then perform the semantic search query\n",
+          "        cur.execute(\n",
+          "            \"\"\"\n",
+          "            SELECT title, text, url, json_data,\n",
+          "                   embedding <=> %s::vector AS similarity\n",
+          "            FROM wikipedia_data\n",
+          "            ORDER BY similarity ASC\n",
+          "            LIMIT %s\n",
+          "            \"\"\",\n",
+          "            (query_embedding, top_n),\n",
+          "        )\n",
+          "\n",
+          "        # Fetch and return the top results\n",
+          "        results = cur.fetchall()\n",
+          "\n",
+          "        # Format results as list of dictionaries for easier handling\n",
+          "        formatted_results = []\n",
+          "        for r in results:\n",
+          "            formatted_results.append(\n",
+          "                {\n",
+          "                    \"title\": r[0],\n",
+          "                    \"text\": r[1],\n",
+          "                    \"url\": r[2],\n",
+          "                    \"json_data\": r[3],\n",
+          "                    \"similarity\": r[4],\n",
+          "                }\n",
+          "            )\n",
+          "\n",
+          "        return formatted_results"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 7: Running a quick example of semantic search with postgres and pgvector"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 23,
+        "metadata": {},
+        "outputs": [
+          {
+            "name": "stdout",
+            "output_type": "stream",
+            "text": [
+              "\n",
+              "Title: YouTube\n",
+              "Text: YouTube announced the project in September 2016 at an event in India. It was launched in India in February 2017, and expanded in November 2017 to 14 other countries, including Nigeria, Indonesia, Thailand, Malaysia, Vietnam, the Philippines, Kenya, and South Africa. It was rolled out in 130 countries worldwide, including Brazil, Mexico, Turkey, and Iraq on February 1, 2018. Before it shut down, the app was available to around 60% of the world's population.\n",
+              "URL: https://en.wikipedia.org/wiki?curid=3524766\n",
+              "JSON Data: {'url': 'https://en.wikipedia.org/wiki?curid=3524766', 'text': \"YouTube announced the project in September 2016 at an event in India. It was launched in India in February 2017, and expanded in November 2017 to 14 other countries, including Nigeria, Indonesia, Thailand, Malaysia, Vietnam, the Philippines, Kenya, and South Africa. It was rolled out in 130 countries worldwide, including Brazil, Mexico, Turkey, and Iraq on February 1, 2018. Before it shut down, the app was available to around 60% of the world's population.\", 'title': 'YouTube'}\n",
+              "Similarity Score: 0.9034\n",
+              "--------------------------------------------------------------------------------\n",
+              "\n",
+              "Title: YouTube\n",
+              "Text: The mobile version of the site was relaunched based on HTML5 in July 2010, avoiding the need to use Adobe Flash Player and optimized for use with touch screen controls. The mobile version is also available as an app for the Android platform.\n",
+              "URL: https://en.wikipedia.org/wiki?curid=3524766\n",
+              "JSON Data: {'url': 'https://en.wikipedia.org/wiki?curid=3524766', 'text': 'The mobile version of the site was relaunched based on HTML5 in July 2010, avoiding the need to use Adobe Flash Player and optimized for use with touch screen controls. The mobile version is also available as an app for the Android platform.', 'title': 'YouTube'}\n",
+              "Similarity Score: 0.8969\n",
+              "--------------------------------------------------------------------------------\n",
+              "\n",
+              "Title: YouTube\n",
+              "Text: In January 2009, YouTube launched \"YouTube for TV\", a version of the website tailored for set-top boxes and other TV-based media devices with web browsers, initially allowing its videos to be viewed on the PlayStation 3 and Wii video game consoles.\n",
+              "URL: https://en.wikipedia.org/wiki?curid=3524766\n",
+              "JSON Data: {'url': 'https://en.wikipedia.org/wiki?curid=3524766', 'text': 'In January 2009, YouTube launched \"YouTube for TV\", a version of the website tailored for set-top boxes and other TV-based media devices with web browsers, initially allowing its videos to be viewed on the PlayStation 3 and Wii video game consoles.', 'title': 'YouTube'}\n",
+              "Similarity Score: 0.8967\n",
+              "--------------------------------------------------------------------------------\n",
+              "\n",
+              "Title: YouTube\n",
+              "Text: Later the same year, \"YouTube Feather\" was introduced as a lightweight alternative website for countries with limited internet speeds.\n",
+              "URL: https://en.wikipedia.org/wiki?curid=3524766\n",
+              "JSON Data: {'url': 'https://en.wikipedia.org/wiki?curid=3524766', 'text': 'Later the same year, \"YouTube Feather\" was introduced as a lightweight alternative website for countries with limited internet speeds.', 'title': 'YouTube'}\n",
+              "Similarity Score: 0.8951\n",
+              "--------------------------------------------------------------------------------\n",
+              "\n",
+              "Title: Twitch (service)\n",
+              "Text: On May 18, 2014, \"Variety\" first reported that Google had reached a preliminary deal to acquire Twitch through its YouTube subsidiary for approximately .\n",
+              "URL: https://en.wikipedia.org/wiki?curid=33548254\n",
+              "JSON Data: {'url': 'https://en.wikipedia.org/wiki?curid=33548254', 'text': 'On May 18, 2014, \"Variety\" first reported that Google had reached a preliminary deal to acquire Twitch through its YouTube subsidiary for approximately .', 'title': 'Twitch (service)'}\n",
+              "Similarity Score: 0.8928\n",
+              "--------------------------------------------------------------------------------\n",
+              "Connection closed\n"
+            ]
+          }
+        ],
+        "source": [
+          "# Connect to PostgreSQL\n",
+          "try:\n",
+          "    conn = psycopg.connect(\n",
+          "        \"dbname=vector_db user=postgres password=test host=127.0.0.1\"\n",
+          "    )\n",
+          "\n",
+          "    # Run semantic search with a sample query\n",
+          "    query_text = \"When was YouTube officially launched, and by whom?\"\n",
+          "    results = semantic_search_with_postgres(query_text, conn, top_n=5)\n",
+          "\n",
+          "    # Print results in a formatted way\n",
+          "    for result in results:\n",
+          "        print(f\"\\nTitle: {result['title']}\")\n",
+          "        print(f\"Text: {result['text']}\")\n",
+          "        print(f\"URL: {result['url']}\")\n",
+          "        print(f\"JSON Data: {result['json_data']}\")\n",
+          "        print(f\"Similarity Score: {1- result['similarity']:.4f}\")\n",
+          "        print(\"-\" * 80)\n",
+          "\n",
+          "except Exception as e:\n",
+          "    print(\"Failed to connect or execute query:\", e)\n",
+          "finally:\n",
+          "    conn.close()\n",
+          "    print(\"Connection closed\")"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "## Part 3: Semantic Search with MongoDB Atlas Vector Search"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 1: Install Libraries\n",
+          "\n",
+          "- `pymongo` (4.10.1): A Python driver for MongoDB (https://pymongo.readthedocs.io/en/stable/)"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": null,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "%pip install --quiet --upgrade pymongo"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 2: Installing MongoDB via Atlas CLI\n",
+          "\n",
+          "The Atlas CLI is a command line interface built specifically for MongoDB Atlas. \n",
+          "Interact with your Atlas database deployments and Atlas Search from the terminal with short, intuitive commands, so you can accomplish complex database management tasks in seconds.\n",
+          "\n",
+          "You can follow the instructions [here](https://www.mongodb.com/docs/atlas/cli/current/install-atlas-cli/#complete-the-prerequisites-3) to install the Atlas CLI using docker(other options are available) and get a local MongoDB database instance running.\n",
+          "\n",
+          "Follow the steps [here](https://www.mongodb.com/docs/atlas/cli/current/atlas-cli-docker/#follow-these-steps) to run Altas CLI commands with Docker.\n",
+          "\n",
+          "Find more information on the Atlas CLI [here](https://www.mongodb.com/docs/atlas/cli/): "
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 3: Connect to MongoDB and Create Database and Collection"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "After installing the Atlas CLI, you can run the following command to connect to your MongoDB database:\n",
+          "1. atlas deployments connect\n",
+          "2. You will be prompted to specificy \"How would you like to connect to local9410\"\n",
+          "3. Select connectionString\n",
+          "4. Copy the connection string and paste it into the MONGO_URI environment variable\n",
+          "\n",
+          "More information [here](https://www.mongodb.com/docs/atlas/cli/current/atlas-cli-deploy-fts/#connect-to-the-deployment)."
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 52,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "# Set MongoDB URI\n",
+          "# Example: mongodb://localhost:54516/?directConnection=true\n",
+          "set_env_securely(\"MONGO_URI\", \"Enter your MONGO URI: \")"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "In the following code blocks below we do the following:\n",
+          "1. Establish a connection to the MongoDB database\n",
+          "2. Create a database and collection if they do not already exist\n",
+          "3. Delete all data in the collection if it already exists\n"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 53,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "import pymongo\n",
+          "\n",
+          "\n",
+          "def get_mongo_client(mongo_uri):\n",
+          "    \"\"\"Establish and validate connection to the MongoDB.\"\"\"\n",
+          "\n",
+          "    client = pymongo.MongoClient(\n",
+          "        mongo_uri, appname=\"devrel.showcase.postgres_vs_mongodb.python\"\n",
+          "    )\n",
+          "\n",
+          "    # Validate the connection\n",
+          "    ping_result = client.admin.command(\"ping\")\n",
+          "    if ping_result.get(\"ok\") == 1.0:\n",
+          "        # Connection successful\n",
+          "        print(\"Connection to MongoDB successful\")\n",
+          "        return client\n",
+          "    else:\n",
+          "        print(\"Connection to MongoDB failed\")\n",
+          "    return None\n",
+          "\n",
+          "\n",
+          "MONGO_URI = os.environ[\"MONGO_URI\"]\n",
+          "if not MONGO_URI:\n",
+          "    print(\"MONGO_URI not set in environment variables\")"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 29,
+        "metadata": {},
+        "outputs": [
+          {
+            "name": "stdout",
+            "output_type": "stream",
+            "text": [
+              "Connection to MongoDB successful\n",
+              "Collection 'wikipedia_data' already exists.\n"
+            ]
+          }
+        ],
+        "source": [
+          "from pymongo.errors import CollectionInvalid\n",
+          "\n",
+          "mongo_client = get_mongo_client(MONGO_URI)\n",
+          "\n",
+          "DB_NAME = \"vector_db\"\n",
+          "COLLECTION_NAME = \"wikipedia_data\"\n",
+          "\n",
+          "# Create or get the database\n",
+          "db = mongo_client[DB_NAME]\n",
+          "\n",
+          "# Check if the collection exists\n",
+          "if COLLECTION_NAME not in db.list_collection_names():\n",
+          "    try:\n",
+          "        # Create the collection\n",
+          "        db.create_collection(COLLECTION_NAME)\n",
+          "        print(f\"Collection '{COLLECTION_NAME}' created successfully.\")\n",
+          "    except CollectionInvalid as e:\n",
+          "        print(f\"Error creating collection: {e}\")\n",
+          "else:\n",
+          "    print(f\"Collection '{COLLECTION_NAME}' already exists.\")\n",
+          "\n",
+          "# Assign the collection\n",
+          "collection = db[COLLECTION_NAME]"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 30,
+        "metadata": {},
+        "outputs": [
+          {
+            "data": {
+              "text/plain": [
+                "DeleteResult({'n': 0, 'electionId': ObjectId('7fffffff0000000000000005'), 'opTime': {'ts': Timestamp(1733982920, 1), 't': 5}, 'ok': 1.0, '$clusterTime': {'clusterTime': Timestamp(1733982920, 1), 'signature': {'hash': b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00', 'keyId': 0}}, 'operationTime': Timestamp(1733982920, 1)}, acknowledged=True)"
+              ]
+            },
+            "execution_count": 30,
+            "metadata": {},
+            "output_type": "execute_result"
+          }
+        ],
+        "source": [
+          "collection.delete_many({})"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 4: Vector Index Creation\n",
+          "\n",
+          "The `setup_vector_search_index` function creates a vector search index for the MongoDB collection.\n",
+          "\n",
+          "The `index_name` parameter is the name of the index to create.\n",
+          "\n",
+          "The `embedding_field_name` parameter is the name of the field containing the text embeddings on each document within the wikipedia_data collection.\n"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 31,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "embedding_field_name = \"embedding\"\n",
+          "vector_search_index_name = \"vector_index\""
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 32,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "import time\n",
+          "\n",
+          "from pymongo.operations import SearchIndexModel\n",
+          "\n",
+          "\n",
+          "def setup_vector_search_index(collection, index_name=\"vector_index\"):\n",
+          "    \"\"\"\n",
+          "    Setup a vector search index for a MongoDB collection and wait for 30 seconds.\n",
+          "\n",
+          "    Args:\n",
+          "    collection: MongoDB collection object\n",
+          "    index_definition: Dictionary containing the index definition\n",
+          "    index_name: Name of the index (default: \"vector_index\")\n",
+          "    \"\"\"\n",
+          "    new_vector_search_index_model = SearchIndexModel(\n",
+          "        definition={\n",
+          "            \"fields\": [\n",
+          "                {\n",
+          "                    \"type\": \"vector\",\n",
+          "                    \"path\": \"embedding\",\n",
+          "                    \"numDimensions\": 768,\n",
+          "                    \"similarity\": \"cosine\",\n",
+          "                }\n",
+          "            ]\n",
+          "        },\n",
+          "        name=index_name,\n",
+          "        type=\"vectorSearch\",\n",
+          "    )\n",
+          "\n",
+          "    # Create the new index\n",
+          "    try:\n",
+          "        result = collection.create_search_index(model=new_vector_search_index_model)\n",
+          "        print(f\"Creating index '{index_name}'...\")\n",
+          "\n",
+          "        # Wait for 30 seconds\n",
+          "        print(f\"Waiting for 30 seconds to allow index '{index_name}' to be created...\")\n",
+          "        time.sleep(30)\n",
+          "\n",
+          "        print(f\"30-second wait completed for index '{index_name}'.\")\n",
+          "        return result\n",
+          "\n",
+          "    except Exception as e:\n",
+          "        print(f\"Error creating new vector search index '{index_name}': {e!s}\")\n",
+          "        return None"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 33,
+        "metadata": {},
+        "outputs": [
+          {
+            "name": "stdout",
+            "output_type": "stream",
+            "text": [
+              "Creating index 'vector_index'...\n",
+              "Waiting for 30 seconds to allow index 'vector_index' to be created...\n",
+              "30-second wait completed for index 'vector_index'.\n"
+            ]
+          },
+          {
+            "data": {
+              "text/plain": [
+                "'vector_index'"
+              ]
+            },
+            "execution_count": 33,
+            "metadata": {},
+            "output_type": "execute_result"
+          }
+        ],
+        "source": [
+          "setup_vector_search_index(collection, \"vector_index\")"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 5: Define Insert Data Function\n",
+          "\n",
+          "Because of the affinity of MongoDB for JSON data, we don't have to convert the Python Dictionary in the `json_data` attribute to a JSON string using the `json.dumps()` function. Instead, we can directly insert the Python Dictionary into the MongoDB collection.\n",
+          "\n",
+          "This reduced the operational overhead of the insertion processes in AI workloads.\n"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 34,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "def insert_data_to_mongodb(dataframe, collection, database_type=\"MongoDB\"):\n",
+          "    start_time = time.time()\n",
+          "    total_rows = len(dataframe)\n",
+          "\n",
+          "    try:\n",
+          "        # Convert DataFrame to list of dictionaries for MongoDB insertion\n",
+          "        documents = dataframe.to_dict(\"records\")\n",
+          "\n",
+          "        # Use insert_many for better performance\n",
+          "        result = collection.insert_many(documents)\n",
+          "\n",
+          "        end_time = time.time()\n",
+          "        total_time = end_time - start_time\n",
+          "        rows_per_second = total_rows / total_time\n",
+          "\n",
+          "        # print(f\"\\nMongoDB Insertion Statistics:\")\n",
+          "        # print(f\"Total time: {total_time:.2f} seconds\")\n",
+          "        # print(f\"Average insertion rate: {rows_per_second:.2f} rows/second\")\n",
+          "        # print(f\"Total rows inserted: {len(result.inserted_ids)}\")\n",
+          "\n",
+          "        # Store results in benchmark dictionary\n",
+          "        if database_type not in benchmark_results:\n",
+          "            benchmark_results[database_type] = {}\n",
+          "\n",
+          "        benchmark_results[database_type][\"insert_time\"] = {\n",
+          "            \"total_time\": total_time,\n",
+          "            \"rows_per_second\": rows_per_second,\n",
+          "            \"total_rows\": total_rows,\n",
+          "        }\n",
+          "\n",
+          "        return True\n",
+          "\n",
+          "    except Exception as e:\n",
+          "        print(f\"Error during MongoDB insertion: {e}\")\n",
+          "        return False"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 6: Insert Data into MongoDB\n"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 35,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "documents = dataset_df.to_dict(\"records\")\n",
+          "success = insert_data_to_mongodb(dataset_df, collection)"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 36,
+        "metadata": {},
+        "outputs": [
+          {
+            "name": "stdout",
+            "output_type": "stream",
+            "text": [
+              "{'insert_time': {'total_time': 90.82945108413696, 'rows_per_second': 1100.96448680911, 'total_rows': 100000}}\n"
+            ]
+          }
+        ],
+        "source": [
+          "print(benchmark_results[\"MongoDB\"])"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### Step 7: Define Semantic Search Function\n",
+          "\n",
+          "The `semantic_search_with_mongodb` function performs a vector search in the MongoDB collection based on the user query.\n",
+          "\n",
+          "- `user_query` parameter is the user's query string.\n",
+          "- `collection` parameter is the MongoDB collection to search.\n",
+          "- `top_n` parameter is the number of top results to return.\n",
+          "- `vector_search_index_name` parameter is the name of the vector search index to use for the search.\n",
+          "\n",
+          "The `numCandidates` parameter is the number of candidate matches to consider. This is set to 100 to match the number of candidate matches to consider in the PostgreSQL vector search.\n",
+          "\n",
+          "Another point to note is the queries in MongoDB are performed using the `aggregate` function enabled by the MongoDB Query Language(MQL).\n",
+          "\n",
+          "This allows for more flexibility in the queries and the ability to perform more complex searches. And data processing opreations can be defined as stages in the pipeline. If you are a data engineer, data scientist or ML Engineer, the concept of pipeline processing is a key concept.\n",
+          "\n",
+          "\n"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": null,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "def semantic_search_with_mongodb(\n",
+          "    user_query, collection, top_n=5, vector_search_index_name=\"vector_index\"\n",
+          "):\n",
+          "    \"\"\"\n",
+          "    Perform a vector search in the MongoDB collection based on the user query.\n",
+          "\n",
+          "    Args:\n",
+          "    user_query (str): The user's query string.\n",
+          "    collection (MongoCollection): The MongoDB collection to search.\n",
+          "    additional_stages (list): Additional aggregation stages to include in the pipeline.\n",
+          "    vector_search_index_name (str): The name of the vector search index.\n",
+          "\n",
+          "    Returns:\n",
+          "    list: A list of matching documents.\n",
+          "    \"\"\"\n",
+          "\n",
+          "    # Take a query embedding from the query_embeddings_dict\n",
+          "    query_embedding = query_embeddings_dict[user_query]\n",
+          "\n",
+          "    if query_embedding is None:\n",
+          "        return \"Invalid query or embedding generation failed.\"\n",
+          "\n",
+          "    # Define the vector search stage\n",
+          "    vector_search_stage = {\n",
+          "        \"$vectorSearch\": {\n",
+          "            \"index\": vector_search_index_name,  # specifies the index to use for the search\n",
+          "            \"queryVector\": query_embedding,  # the vector representing the query\n",
+          "            \"path\": \"embedding\",  # field in the documents containing the vectors to search against\n",
+          "            \"numCandidates\": 100,  # number of candidate matches to consider\n",
+          "            \"limit\": top_n,  # return top n matches\n",
+          "        }\n",
+          "    }\n",
+          "\n",
+          "    project_stage = {\n",
+          "        \"$project\": {\n",
+          "            \"_id\": 0,  # Exclude the _id field\n",
+          "            \"title\": 1,\n",
+          "            \"text\": 1,\n",
+          "            \"url\": 1,\n",
+          "            \"score\": {\"$meta\": \"vectorSearchScore\"},  # Include the search score\n",
+          "        }\n",
+          "    }\n",
+          "\n",
+          "    # Define the aggregate pipeline with the vector search stage\n",
+          "    pipeline = [vector_search_stage, project_stage]\n",
+          "\n",
+          "    # Execute the search\n",
+          "    results = collection.aggregate(pipeline)\n",
+          "    return list(results)"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 39,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "query_text = \"When was YouTube officially launched, and by whom?\"\n",
+          "\n",
+          "get_knowledge = semantic_search_with_mongodb(query_text, collection)"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 40,
+        "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>title</th>\n",
+                "      <th>text</th>\n",
+                "      <th>url</th>\n",
+                "      <th>score</th>\n",
+                "    </tr>\n",
+                "  </thead>\n",
+                "  <tbody>\n",
+                "    <tr>\n",
+                "      <th>0</th>\n",
+                "      <td>YouTube</td>\n",
+                "      <td>YouTube announced the project in September 201...</td>\n",
+                "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
+                "      <td>0.951712</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>1</th>\n",
+                "      <td>YouTube</td>\n",
+                "      <td>The mobile version of the site was relaunched ...</td>\n",
+                "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
+                "      <td>0.948441</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>2</th>\n",
+                "      <td>YouTube</td>\n",
+                "      <td>In January 2009, YouTube launched \"YouTube for...</td>\n",
+                "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
+                "      <td>0.948370</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>3</th>\n",
+                "      <td>YouTube</td>\n",
+                "      <td>Later the same year, \"YouTube Feather\" was int...</td>\n",
+                "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
+                "      <td>0.947532</td>\n",
+                "    </tr>\n",
+                "    <tr>\n",
+                "      <th>4</th>\n",
+                "      <td>Twitch (service)</td>\n",
+                "      <td>On May 18, 2014, \"Variety\" first reported that...</td>\n",
+                "      <td>https://en.wikipedia.org/wiki?curid=33548254</td>\n",
+                "      <td>0.946378</td>\n",
+                "    </tr>\n",
+                "  </tbody>\n",
+                "</table>\n",
+                "</div>"
+              ],
+              "text/plain": [
+                "              title                                               text  \\\n",
+                "0           YouTube  YouTube announced the project in September 201...   \n",
+                "1           YouTube  The mobile version of the site was relaunched ...   \n",
+                "2           YouTube  In January 2009, YouTube launched \"YouTube for...   \n",
+                "3           YouTube  Later the same year, \"YouTube Feather\" was int...   \n",
+                "4  Twitch (service)  On May 18, 2014, \"Variety\" first reported that...   \n",
+                "\n",
+                "                                            url     score  \n",
+                "0   https://en.wikipedia.org/wiki?curid=3524766  0.951712  \n",
+                "1   https://en.wikipedia.org/wiki?curid=3524766  0.948441  \n",
+                "2   https://en.wikipedia.org/wiki?curid=3524766  0.948370  \n",
+                "3   https://en.wikipedia.org/wiki?curid=3524766  0.947532  \n",
+                "4  https://en.wikipedia.org/wiki?curid=33548254  0.946378  "
+              ]
+            },
+            "execution_count": 40,
+            "metadata": {},
+            "output_type": "execute_result"
+          }
+        ],
+        "source": [
+          "pd.DataFrame(get_knowledge).head()"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "## Part 4: Vector Database Benchmarking"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### 1. Insertion Benchmark Process\n",
+          "\n",
+          "We are inserting data incrementally with doubling batch sizes and record performance metrics.\n",
+          "Notably, we will be measuring the time it takes to insert data incrementally and the number of rows inserted per second.\n",
+          "\n",
+          "We are using the `insert_data_incrementally` function to insert data incrementally.\n",
+          "\n",
+          "It starts with a batch size of 1 and doubles the batch size until it has inserted all the data, recording the time it takes to insert the data and the number of rows inserted per second.\n",
+          "\n",
+          "The key component we are interested in is the time it takes to insert the data and the number of rows inserted per second. In AI Workloads, there are data ingestion processes that are performned in batches from various data sources. So in practice, we are interested in the time it takes to insert the data and the number of rows inserted per second."
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 21,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "import time\n",
+          "\n",
+          "\n",
+          "def insert_data_incrementally(dataframe, connection, database_type=\"PostgreSQL\"):\n",
+          "    \"\"\"\n",
+          "    Insert data incrementally with doubling batch sizes and record performance metrics.\n",
+          "    \"\"\"\n",
+          "    incremental_metrics = {}\n",
+          "    total_rows = len(dataframe)\n",
+          "    remaining_rows = total_rows\n",
+          "    start_idx = 0\n",
+          "\n",
+          "    # Define batch sizes (1, 10, then doubling)\n",
+          "    batch_sizes = [1, 10]\n",
+          "    current_size = 20\n",
+          "    while current_size < total_rows:\n",
+          "        batch_sizes.append(current_size)\n",
+          "        current_size *= 2\n",
+          "\n",
+          "    for batch_size in batch_sizes:\n",
+          "        # Skip if we've already inserted all data\n",
+          "        if remaining_rows <= 0:\n",
+          "            break\n",
+          "\n",
+          "        # Calculate actual batch size based on remaining rows\n",
+          "        actual_batch_size = min(batch_size, remaining_rows)\n",
+          "        end_idx = start_idx + actual_batch_size\n",
+          "\n",
+          "        # Get the batch of data\n",
+          "        batch_df = dataframe.iloc[start_idx:end_idx]\n",
+          "\n",
+          "        # Record start time\n",
+          "        start_time = time.time()\n",
+          "\n",
+          "        try:\n",
+          "            # Insert data using existing function\n",
+          "            if database_type == \"PostgreSQL\":\n",
+          "                insert_data_to_postgres(batch_df, connection, database_type)\n",
+          "            else:  # MongoDB\n",
+          "                insert_data_to_mongodb(batch_df, connection, database_type)\n",
+          "\n",
+          "            # Record end time and calculate metrics\n",
+          "            end_time = time.time()\n",
+          "            total_time = end_time - start_time\n",
+          "            rows_per_second = actual_batch_size / total_time\n",
+          "\n",
+          "            # Store metrics\n",
+          "            incremental_metrics[actual_batch_size] = {\n",
+          "                \"total_time\": total_time,\n",
+          "                \"rows_per_second\": rows_per_second,\n",
+          "                \"batch_size\": actual_batch_size,\n",
+          "            }\n",
+          "\n",
+          "            # print(f\"\\nBatch Size {batch_size} Statistics:\")\n",
+          "            # print(f\"Total time: {total_time:.2f} seconds\")\n",
+          "            # print(f\"Average insertion rate: {rows_per_second:.2f} rows/second\")\n",
+          "            # print(f\"Actual rows inserted: {actual_batch_size}\")\n",
+          "\n",
+          "        except Exception as e:\n",
+          "            print(f\"Error during batch insertion (size {batch_size}): {e}\")\n",
+          "            raise e\n",
+          "\n",
+          "        # Update counters\n",
+          "        start_idx = end_idx\n",
+          "        remaining_rows -= actual_batch_size\n",
+          "\n",
+          "    # Store results in benchmark dictionary\n",
+          "    if database_type not in benchmark_results:\n",
+          "        benchmark_results[database_type] = {}\n",
+          "\n",
+          "    benchmark_results[database_type][\"incremental_insert\"] = incremental_metrics\n",
+          "\n",
+          "    return incremental_metrics"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "#### 1.1 PostgreSQL Insertion Benchmark"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 23,
+        "metadata": {},
+        "outputs": [
+          {
+            "name": "stdout",
+            "output_type": "stream",
+            "text": [
+              "Table and index created successfully\n",
+              "\n",
+              "Connection closed\n"
+            ]
+          }
+        ],
+        "source": [
+          "import psycopg\n",
+          "from pgvector.psycopg import register_vector\n",
+          "\n",
+          "try:\n",
+          "    conn = psycopg.connect(\n",
+          "        \"dbname=vector_db user=postgres password=test host=127.0.0.1\"\n",
+          "    )\n",
+          "    register_vector(conn)\n",
+          "\n",
+          "    # Create fresh table\n",
+          "    create_table(conn)\n",
+          "\n",
+          "    postgres_metrics = insert_data_incrementally(dataset_df, conn, \"PostgreSQL\")\n",
+          "\n",
+          "except Exception as e:\n",
+          "    print(\"Failed to execute:\", e)\n",
+          "finally:\n",
+          "    conn.close()\n",
+          "    print(\"\\nConnection closed\")"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "#### 1.2 MongoDB Insertion Benchmark"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 91,
+        "metadata": {},
+        "outputs": [
+          {
+            "name": "stdout",
+            "output_type": "stream",
+            "text": [
+              "Connection to MongoDB successful\n",
+              "\n",
+              "MongoDB connection closed\n"
+            ]
+          }
+        ],
+        "source": [
+          "try:\n",
+          "    mongo_client = get_mongo_client(MONGO_URI)\n",
+          "    db = mongo_client[DB_NAME]\n",
+          "    collection = db[COLLECTION_NAME]\n",
+          "\n",
+          "    # Clear collection\n",
+          "    collection.delete_many({})\n",
+          "\n",
+          "    mongo_metrics = insert_data_incrementally(dataset_df, collection, \"MongoDB\")\n",
+          "\n",
+          "except Exception as e:\n",
+          "    print(\"MongoDB operation failed:\", e)\n",
+          "finally:\n",
+          "    mongo_client.close()\n",
+          "    print(\"\\nMongoDB connection closed\")"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "#### 1.3 Visualize Insertion Benchmark\n"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 95,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "import matplotlib.pyplot as plt\n",
+          "\n",
+          "\n",
+          "def plot_combined_insertion_metrics(postgres_metrics, mongo_metrics):\n",
+          "    \"\"\"\n",
+          "    Creates a combined line plot comparing PostgreSQL and MongoDB insertion metrics.\n",
+          "    \"\"\"\n",
+          "    # Create figure\n",
+          "    plt.figure(figsize=(12, 6))\n",
+          "\n",
+          "    # Extract data for both databases\n",
+          "    batch_sizes = [\n",
+          "        1,\n",
+          "        10,\n",
+          "        20,\n",
+          "        40,\n",
+          "        80,\n",
+          "        160,\n",
+          "        320,\n",
+          "        640,\n",
+          "        1280,\n",
+          "        2560,\n",
+          "        5120,\n",
+          "        10240,\n",
+          "        20480,\n",
+          "        40960,\n",
+          "    ]\n",
+          "    postgres_times = [\n",
+          "        postgres_metrics[size][\"total_time\"]\n",
+          "        for size in batch_sizes\n",
+          "        if size in postgres_metrics\n",
+          "    ]\n",
+          "    mongo_times = [\n",
+          "        mongo_metrics[size][\"total_time\"]\n",
+          "        for size in batch_sizes\n",
+          "        if size in mongo_metrics\n",
+          "    ]\n",
+          "\n",
+          "    # Create the line plots\n",
+          "    plt.plot(\n",
+          "        batch_sizes[: len(postgres_times)],\n",
+          "        postgres_times,\n",
+          "        marker=\"o\",\n",
+          "        label=\"PostgreSQL\",\n",
+          "        color=\"blue\",\n",
+          "        linewidth=2,\n",
+          "    )\n",
+          "    plt.plot(\n",
+          "        batch_sizes[: len(mongo_times)],\n",
+          "        mongo_times,\n",
+          "        marker=\"s\",\n",
+          "        label=\"MongoDB\",\n",
+          "        color=\"green\",\n",
+          "        linewidth=2,\n",
+          "    )\n",
+          "\n",
+          "    # Customize the plot\n",
+          "    plt.title(\"Database Insertion Time Comparison\", fontsize=14)\n",
+          "    plt.xlabel(\"Batch Size\", fontsize=12)\n",
+          "    plt.ylabel(\"Time (seconds)\", fontsize=12)\n",
+          "    plt.grid(True, linestyle=\"--\", alpha=0.7)\n",
+          "    plt.legend(fontsize=10)\n",
+          "\n",
+          "    # Use log scale for x-axis\n",
+          "    plt.xscale(\"log\", base=2)\n",
+          "\n",
+          "    # Define custom tick positions\n",
+          "    custom_ticks = batch_sizes\n",
+          "    plt.xticks(custom_ticks, custom_ticks, rotation=45, ha=\"right\")\n",
+          "\n",
+          "    # Add value annotations\n",
+          "    for i, (size, time) in enumerate(\n",
+          "        zip(batch_sizes[: len(postgres_times)], postgres_times)\n",
+          "    ):\n",
+          "        plt.annotate(\n",
+          "            f\"{time:.1f}s\",\n",
+          "            (size, time),\n",
+          "            textcoords=\"offset points\",\n",
+          "            xytext=(0, 10),\n",
+          "            ha=\"center\",\n",
+          "            fontsize=8,\n",
+          "        )\n",
+          "\n",
+          "    for i, (size, time) in enumerate(zip(batch_sizes[: len(mongo_times)], mongo_times)):\n",
+          "        plt.annotate(\n",
+          "            f\"{time:.1f}s\",\n",
+          "            (size, time),\n",
+          "            textcoords=\"offset points\",\n",
+          "            xytext=(0, -15),\n",
+          "            ha=\"center\",\n",
+          "            fontsize=8,\n",
+          "        )\n",
+          "\n",
+          "    # Add throughput information in a text box\n",
+          "    postgres_throughput = [\n",
+          "        metrics[\"rows_per_second\"] for metrics in postgres_metrics.values()\n",
+          "    ]\n",
+          "    mongo_throughput = [\n",
+          "        metrics[\"rows_per_second\"] for metrics in mongo_metrics.values()\n",
+          "    ]\n",
+          "\n",
+          "    text_info = (\n",
+          "        f\"Max Throughput:\\n\"\n",
+          "        f\"PostgreSQL: {max(postgres_throughput):.0f} rows/s\\n\"\n",
+          "        f\"MongoDB: {max(mongo_throughput):.0f} rows/s\"\n",
+          "    )\n",
+          "\n",
+          "    plt.text(\n",
+          "        0.02,\n",
+          "        0.98,\n",
+          "        text_info,\n",
+          "        transform=plt.gca().transAxes,\n",
+          "        bbox=dict(facecolor=\"white\", alpha=0.8),\n",
+          "        verticalalignment=\"top\",\n",
+          "        fontsize=10,\n",
+          "    )\n",
+          "\n",
+          "    plt.tight_layout()\n",
+          "    plt.show()"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 96,
+        "metadata": {},
+        "outputs": [
+          {
+            "data": {
+              "image/png": 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",
+              "text/plain": [
+                "<Figure size 1200x600 with 1 Axes>"
+              ]
+            },
+            "metadata": {},
+            "output_type": "display_data"
+          }
+        ],
+        "source": [
+          "plot_combined_insertion_metrics(postgres_metrics, mongo_metrics)"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "### 2. Benchmarking Semantic Search with PostgreSQL and PgVector"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "#### 2.1 PostgreSQL Semantic Search Benchmark"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": null,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "import concurrent.futures\n",
+          "import random\n",
+          "from concurrent.futures import ThreadPoolExecutor\n",
+          "from statistics import mean, stdev\n",
+          "\n",
+          "import psycopg\n",
+          "from pgvector.psycopg import register_vector\n",
+          "\n",
+          "\n",
+          "def benchmark_search_postgres(\n",
+          "    connection,\n",
+          "    database_type=\"PostgreSQL\",\n",
+          "    num_queries=100,\n",
+          "    concurrent_queries=[1, 10, 50, 100],\n",
+          "):\n",
+          "    \"\"\"\n",
+          "    Benchmark the vector database performance with true concurrent queries.\n",
+          "\n",
+          "    Args:\n",
+          "        connection: Database connection\n",
+          "        database_type: Type of database (e.g., 'PostgreSQL', 'MongoDB')\n",
+          "        num_queries: Number of benchmark iterations for statistical significance\n",
+          "        concurrent_queries: Different concurrency levels to test\n",
+          "    \"\"\"\n",
+          "    query_templates = [\n",
+          "        \"When was YouTube officially launched, and by whom?\",\n",
+          "        \"What is YouTube's slogan introduced after Google's acquisition?\",\n",
+          "        \"How many hours of videos are collectively watched on YouTube daily?\",\n",
+          "        \"Which was the first video uploaded to YouTube, and when was it uploaded?\",\n",
+          "        \"What was the acquisition cost of YouTube by Google, and when was the deal finalized?\",\n",
+          "        \"What was the first YouTube video to reach one million views, and when did it happen?\",\n",
+          "        \"What are the three separate branches of the United States government?\",\n",
+          "        \"Which country has the highest documented incarceration rate and prison population?\",\n",
+          "        \"How many executions have occurred in the United States since 1977, and which countries have more?\",\n",
+          "        \"What percentage of the global military spending did the United States account for in 2019?\",\n",
+          "        \"How is the U.S. president elected?\",\n",
+          "        \"What cooling system innovation was included in the proposed venues for the World Cup in Qatar?\",\n",
+          "        \"What lawsuit was filed against Google in June 2020, and what was it about?\",\n",
+          "        \"How much was Google fined by CNIL in January 2022, and for what reason?\",\n",
+          "        \"When did YouTube join the NSA's PRISM program, according to reports?\",\n",
+          "    ]\n",
+          "\n",
+          "    if database_type not in benchmark_results:\n",
+          "        benchmark_results[database_type] = {}\n",
+          "\n",
+          "    benchmark_results[database_type][\"specific\"] = {}\n",
+          "\n",
+          "    def execute_single_query():\n",
+          "        \"\"\"Execute a single query and measure its latency\"\"\"\n",
+          "        query = random.choice(query_templates)\n",
+          "        start_time = time.time()\n",
+          "        result = semantic_search_with_postgres(query, connection, top_n=5)\n",
+          "        end_time = time.time()\n",
+          "        return end_time - start_time\n",
+          "\n",
+          "    for number_of_queries in concurrent_queries:\n",
+          "        latencies = []\n",
+          "\n",
+          "        for _ in range(num_queries):\n",
+          "            with ThreadPoolExecutor(max_workers=number_of_queries) as executor:\n",
+          "                # Submit queries and get individual latencies\n",
+          "                futures = [\n",
+          "                    executor.submit(execute_single_query)\n",
+          "                    for _ in range(number_of_queries)\n",
+          "                ]\n",
+          "                # Collect individual query latencies as they complete\n",
+          "                batch_latencies = [\n",
+          "                    future.result()\n",
+          "                    for future in concurrent.futures.as_completed(futures)\n",
+          "                ]\n",
+          "                latencies.extend(batch_latencies)\n",
+          "\n",
+          "        # Calculate metrics using individual query latencies\n",
+          "        avg_latency = mean(latencies)\n",
+          "        throughput = 1 / avg_latency  # Base queries per second per query\n",
+          "        p95_latency = sorted(latencies)[int(len(latencies) * 0.95)]\n",
+          "        std_dev_latency = stdev(latencies)\n",
+          "\n",
+          "        benchmark_results[database_type][\"specific\"][number_of_queries] = {\n",
+          "            \"avg_latency\": avg_latency,\n",
+          "            \"throughput\": throughput * number_of_queries,  # Scale by concurrent queries\n",
+          "            \"p95_latency\": p95_latency,\n",
+          "            \"std_dev\": std_dev_latency,\n",
+          "        }\n",
+          "\n",
+          "    return benchmark_results"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 80,
+        "metadata": {},
+        "outputs": [
+          {
+            "name": "stdout",
+            "output_type": "stream",
+            "text": [
+              "Running benchmark...\n",
+              "\n",
+              "Connection closed\n"
+            ]
+          }
+        ],
+        "source": [
+          "# Run the benchmark\n",
+          "try:\n",
+          "    conn = psycopg.connect(\n",
+          "        \"dbname=vector_db user=postgres password=test host=127.0.0.1\"\n",
+          "    )\n",
+          "    register_vector(conn)\n",
+          "\n",
+          "    print(\"Running benchmark...\")\n",
+          "    results = benchmark_search_postgres(\n",
+          "        conn,\n",
+          "        database_type=\"PostgreSQL\",\n",
+          "        num_queries=TOTAL_QUERIES,\n",
+          "        concurrent_queries=CONCURRENT_QUERIES,\n",
+          "    )\n",
+          "\n",
+          "except Exception as e:\n",
+          "    print(\"Benchmark failed:\", e)\n",
+          "finally:\n",
+          "    conn.close()\n",
+          "    print(\"\\nConnection closed\")"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": null,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "import pprint\n",
+          "\n",
+          "pprint.pprint(benchmark_results[\"PostgreSQL\"])"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "#### 2.2 MongoDB Semantic Search Benchmark"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 64,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "def benchmark_search_mongo(\n",
+          "    collection,\n",
+          "    database_type=\"MongoDB\",\n",
+          "    num_queries=100,\n",
+          "    concurrent_queries=[1, 10, 50, 100],\n",
+          "):\n",
+          "    \"\"\"\n",
+          "    Benchmark MongoDB vector search with true concurrency.\n",
+          "\n",
+          "    Args:\n",
+          "        collection: MongoDB collection object\n",
+          "        database_type: Type of database (default: \"MongoDB\")\n",
+          "        num_queries: Number of benchmark iterations for statistical significance\n",
+          "        batch_sizes: Different concurrency levels to test\n",
+          "    \"\"\"\n",
+          "    query_templates = [\n",
+          "        \"When was YouTube officially launched, and by whom?\",\n",
+          "        \"What is YouTube's slogan introduced after Google's acquisition?\",\n",
+          "        \"How many hours of videos are collectively watched on YouTube daily?\",\n",
+          "        \"Which was the first video uploaded to YouTube, and when was it uploaded?\",\n",
+          "        \"What was the acquisition cost of YouTube by Google, and when was the deal finalized?\",\n",
+          "        \"What was the first YouTube video to reach one million views, and when did it happen?\",\n",
+          "        \"What are the three separate branches of the United States government?\",\n",
+          "        \"Which country has the highest documented incarceration rate and prison population?\",\n",
+          "        \"How many executions have occurred in the United States since 1977, and which countries have more?\",\n",
+          "        \"What percentage of the global military spending did the United States account for in 2019?\",\n",
+          "        \"How is the U.S. president elected?\",\n",
+          "        \"What cooling system innovation was included in the proposed venues for the World Cup in Qatar?\",\n",
+          "        \"What lawsuit was filed against Google in June 2020, and what was it about?\",\n",
+          "        \"How much was Google fined by CNIL in January 2022, and for what reason?\",\n",
+          "        \"When did YouTube join the NSA's PRISM program, according to reports?\",\n",
+          "    ]\n",
+          "\n",
+          "    if database_type not in benchmark_results:\n",
+          "        benchmark_results[database_type] = {}\n",
+          "\n",
+          "    benchmark_results[database_type][\"specific\"] = {}\n",
+          "\n",
+          "    def execute_single_query():\n",
+          "        \"\"\"Execute a single query with MongoDB connection\"\"\"\n",
+          "        query = random.choice(query_templates)\n",
+          "        start_time = time.time()\n",
+          "        result = semantic_search_with_mongodb(query, collection, top_n=5)\n",
+          "        end_time = time.time()\n",
+          "        return end_time - start_time\n",
+          "\n",
+          "    for number_of_queries in concurrent_queries:\n",
+          "        latencies = []\n",
+          "\n",
+          "        for _ in range(num_queries):\n",
+          "            with ThreadPoolExecutor(max_workers=number_of_queries) as executor:\n",
+          "                # Submit queries and get individual latencies\n",
+          "                futures = [\n",
+          "                    executor.submit(execute_single_query)\n",
+          "                    for _ in range(number_of_queries)\n",
+          "                ]\n",
+          "                # Collect individual query latencies\n",
+          "                batch_latencies = [\n",
+          "                    future.result()\n",
+          "                    for future in concurrent.futures.as_completed(futures)\n",
+          "                ]\n",
+          "                latencies.extend(batch_latencies)\n",
+          "\n",
+          "        # Calculate metrics using individual query latencies\n",
+          "        avg_latency = mean(latencies)\n",
+          "        throughput = 1 / avg_latency  # Queries per second per query\n",
+          "        p95_latency = sorted(latencies)[int(len(latencies) * 0.95)]\n",
+          "        std_dev_latency = stdev(latencies)\n",
+          "\n",
+          "        # Store results\n",
+          "        benchmark_results[database_type][\"specific\"][number_of_queries] = {\n",
+          "            \"avg_latency\": avg_latency,\n",
+          "            \"throughput\": throughput * number_of_queries,  # Scale by concurrent queries\n",
+          "            \"p95_latency\": p95_latency,\n",
+          "            \"std_dev\": std_dev_latency,\n",
+          "        }\n",
+          "\n",
+          "    return benchmark_results[database_type]"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 65,
+        "metadata": {},
+        "outputs": [
+          {
+            "name": "stdout",
+            "output_type": "stream",
+            "text": [
+              "Connection to MongoDB successful\n",
+              "Running MongoDB benchmark...\n",
+              "\n",
+              "MongoDB connection closed\n"
+            ]
+          }
+        ],
+        "source": [
+          "# Run the benchmark for MongoDB\n",
+          "try:\n",
+          "    mongo_client = get_mongo_client(MONGO_URI)\n",
+          "    db = mongo_client[DB_NAME]\n",
+          "    collection = db[COLLECTION_NAME]\n",
+          "\n",
+          "    print(\"Running MongoDB benchmark...\")\n",
+          "    results = benchmark_search_mongo(\n",
+          "        collection, num_queries=TOTAL_QUERIES, concurrent_queries=CONCURRENT_QUERIES\n",
+          "    )\n",
+          "\n",
+          "except Exception as e:\n",
+          "    print(\"MongoDB benchmark failed:\", e)\n",
+          "finally:\n",
+          "    mongo_client.close()\n",
+          "    print(\"\\nMongoDB connection closed\")"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": null,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "print(benchmark_results)"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "#### 2.3 Visualize Semantic Search Benchmark"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 93,
+        "metadata": {},
+        "outputs": [],
+        "source": [
+          "def bar_chart_benchmark_comparison(\n",
+          "    benchmark_results, metric=\"avg_latency\", metric_label=\"Average Latency (ms)\"\n",
+          "):\n",
+          "    \"\"\"\n",
+          "    Generates bar charts to compare benchmark results for each metric across databases.\n",
+          "\n",
+          "    Args:\n",
+          "        benchmark_results (dict): Benchmark results structured as a nested dictionary.\n",
+          "        metric (str): The performance metric to visualize (e.g., \"avg_latency\", \"throughput\").\n",
+          "        metric_label (str): The label to display for the metric.\n",
+          "    \"\"\"\n",
+          "    # Extract data for the bar chart\n",
+          "    query_types = [\"specific\"]\n",
+          "    batch_sizes = CONCURRENT_QUERIES\n",
+          "    data = []\n",
+          "\n",
+          "    for query_type in query_types:\n",
+          "        for batch_size in batch_sizes:\n",
+          "            row = {\"Batch Size\": batch_size, \"Query Type\": query_type}\n",
+          "            for db_type in benchmark_results:\n",
+          "                # Safely extract metric values or default to None\n",
+          "                value = (\n",
+          "                    benchmark_results[db_type]\n",
+          "                    .get(query_type, {})\n",
+          "                    .get(batch_size, {})\n",
+          "                    .get(metric, None)\n",
+          "                )\n",
+          "                # Scale values if necessary\n",
+          "                if value is not None and metric in [\"avg_latency\", \"p95_latency\"]:\n",
+          "                    value *= 1000  # Convert to ms\n",
+          "                row[db_type] = value\n",
+          "            data.append(row)\n",
+          "\n",
+          "    # Convert the data into a plot-friendly structure\n",
+          "    for query_type in query_types:\n",
+          "        labels = [str(batch_size) for batch_size in batch_sizes]\n",
+          "        mongodb_values = [\n",
+          "            row[\"MongoDB\"] for row in data if row[\"Query Type\"] == query_type\n",
+          "        ]\n",
+          "        postgres_values = [\n",
+          "            row[\"PostgreSQL\"] for row in data if row[\"Query Type\"] == query_type\n",
+          "        ]\n",
+          "\n",
+          "        # Create the bar chart\n",
+          "        fig, ax = plt.subplots(figsize=(15, 6))\n",
+          "\n",
+          "        # Set the width of each bar and positions of the bars\n",
+          "        width = 0.35\n",
+          "        x = np.arange(len(batch_sizes))\n",
+          "\n",
+          "        # Create bars\n",
+          "        postgres_bars = ax.bar(\n",
+          "            x - width / 2,\n",
+          "            postgres_values,\n",
+          "            width,\n",
+          "            label=\"PostgreSQL\",\n",
+          "            color=\"lightblue\",\n",
+          "            edgecolor=\"blue\",\n",
+          "        )\n",
+          "        mongodb_bars = ax.bar(\n",
+          "            x + width / 2,\n",
+          "            mongodb_values,\n",
+          "            width,\n",
+          "            label=\"MongoDB\",\n",
+          "            color=\"lightgreen\",\n",
+          "            edgecolor=\"green\",\n",
+          "        )\n",
+          "\n",
+          "        # Add grid\n",
+          "        ax.grid(True, linestyle=\"--\", alpha=0.7, axis=\"y\")\n",
+          "\n",
+          "        # Add titles and labels\n",
+          "        ax.set_title(\n",
+          "            f\"{metric_label} Comparison for {query_type.capitalize()} Queries\", pad=20\n",
+          "        )\n",
+          "        ax.set_xlabel(\"Concurrent Queries\", labelpad=10)\n",
+          "        ax.set_ylabel(metric_label, labelpad=10)\n",
+          "\n",
+          "        # Set x-axis ticks\n",
+          "        ax.set_xticks(x)\n",
+          "        ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n",
+          "\n",
+          "        # Add legend\n",
+          "        ax.legend(fontsize=10)\n",
+          "\n",
+          "        # Add value labels on top of bars\n",
+          "        def autolabel(rects):\n",
+          "            for rect in rects:\n",
+          "                height = rect.get_height()\n",
+          "                ax.annotate(\n",
+          "                    f\"{height:.2f}\",\n",
+          "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
+          "                    xytext=(0, 3),  # 3 points vertical offset\n",
+          "                    textcoords=\"offset points\",\n",
+          "                    ha=\"center\",\n",
+          "                    va=\"bottom\",\n",
+          "                    rotation=90,\n",
+          "                    fontsize=8,\n",
+          "                )\n",
+          "\n",
+          "        autolabel(postgres_bars)\n",
+          "        autolabel(mongodb_bars)\n",
+          "\n",
+          "        plt.tight_layout()\n",
+          "        plt.show()"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": 84,
+        "metadata": {},
+        "outputs": [
+          {
+            "data": {
+              "image/png": 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",
+              "text/plain": [
+                "<Figure size 1500x600 with 1 Axes>"
+              ]
+            },
+            "metadata": {},
+            "output_type": "display_data"
+          },
+          {
+            "data": {
+              "image/png": 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",
+              "text/plain": [
+                "<Figure size 1500x600 with 1 Axes>"
+              ]
+            },
+            "metadata": {},
+            "output_type": "display_data"
+          },
+          {
+            "data": {
+              "image/png": 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",
+              "text/plain": [
+                "<Figure size 1500x600 with 1 Axes>"
+              ]
+            },
+            "metadata": {},
+            "output_type": "display_data"
+          }
+        ],
+        "source": [
+          "# Generate bar charts for each metric\n",
+          "bar_chart_benchmark_comparison(\n",
+          "    benchmark_results, metric=\"avg_latency\", metric_label=\"Average Latency (ms)\"\n",
+          ")\n",
+          "bar_chart_benchmark_comparison(\n",
+          "    benchmark_results, metric=\"throughput\", metric_label=\"Throughput (queries/sec)\"\n",
+          ")\n",
+          "bar_chart_benchmark_comparison(\n",
+          "    benchmark_results, metric=\"p95_latency\", metric_label=\"P95 Latency (ms)\"\n",
+          ")"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "## Part 5: Extra Notes"
+        ]
+      },
+      {
+        "cell_type": "markdown",
+        "metadata": {},
+        "source": [
+          "\n",
+          "### 5.1 PostgreSQL JSONB vs MongoDB BSON\n",
+          "\n",
+          "| Feature                    | **PostgreSQL JSONB**                              | **MongoDB BSON**                              |\n",
+          "|----------------------------|--------------------------------------------------|----------------------------------------------|\n",
+          "| **Integration**            | An extension to a relational database system.    | Native to MongoDB, a document database.      |\n",
+          "| **Query Language**         | Uses SQL with JSONB-specific operators/functions. | Uses MongoDB Query Language (MQL), a JSON-like query syntax. |\n",
+          "| **Storage Optimization**   | Optimized for relational data alongside JSONB.   | Fully optimized for JSON-like document storage. |\n",
+          "| **Data Type Support**      | Stores standard JSON data types (e.g., strings, numbers). | Includes additional types not in standard JSON (e.g., `Date`, `ObjectId`, `Binary`). |\n",
+          "| **Use Case**               | Best for hybrid relational/JSON use cases.       | Designed for flexible schemas, document-based databases. |\n",
+          "| **Updates**                | JSONB supports in-place updates for specific keys or paths. | BSON supports in-place updates with more native support for field-level atomic operations. |\n",
+          "| **Size Overhead**          | Slightly more compact than BSON in some cases.   | Includes metadata like type information, leading to slightly larger size. |\n"
+        ]
+      },
+      {
+        "cell_type": "code",
+        "execution_count": null,
+        "metadata": {},
+        "outputs": [],
+        "source": []
+      }
+    ],
     "colab": {
       "provenance": []
     },
@@ -2622,60 +4952,4533 @@
       "pygments_lexer": "ipython3",
       "version": "3.11.5"
     },
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@@ -7071,7252 +13874,8 @@
           }
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       }
-    },
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Vector Database Comparison For AI Workloads\n",
-    "-----\n",
-    "\n",
-    "This notebook implements and benchmarks a standard AI workload that involves vector embeddings and the retreival of semantically similar documents from a database. The system uses two different vector databases:\n",
-    "- PostgreSQL with pgvector: A vector database extension for PostgreSQL that enables vector search on the database.\n",
-    "- MongoDB Atlas Vector Search: A vector search feature for MongoDB Database that enables vector search on the database.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Key Information\n",
-    "\n",
-    "1. **System Configuration**\n",
-    "\n",
-    "| Component | Specification |\n",
-    "|-----------|---------------|\n",
-    "| **Hardware** ||\n",
-    "| System | MacBook Pro 16-inch, 2023 |\n",
-    "| CPU | Apple M2 Pro |\n",
-    "| Memory | 16GB |\n",
-    "| **Database Tools** ||\n",
-    "| PostgreSQL Management | pgAdmin 4 (v8.12) |\n",
-    "| MongoDB Management | MongoDB Compass (v1.41.0) |\n",
-    "| **Database Versions** ||\n",
-    "| PostgreSQL | 15.4 with pgvector extension (Single Node) |\n",
-    "| - Index Configuration | HNSW index with m=16, ef_construction=64 and cosine similarity |\n",
-    "| MongoDB | 8.0.1 with Atlas CLI (Single Node) |\n",
-    "| - Index Configuration | Vector Search Index (HNSW) with 768 dimensions and cosine similarity |\n",
-    "| **Container Environment** ||\n",
-    "| Docker Desktop | 4.34.3 |\n",
-    "| Docker Engine | 27.2.0 |\n",
-    "| Docker Compose | v2.29.2-desktop.2 |\n",
-    "| Kubernetes | v1.30.2 |\n",
-    "| **Docker Resource Pool** ||\n",
-    "| Total CPU | 12 cores |\n",
-    "| Total Memory | 7.9 GB |\n",
-    "| Total Swap | 1 GB |\n",
-    "| Virtual Disk | 64 GB |\n",
-    "| Container Memory Limit | 7.65GB |\n",
-    "| **Development Environment** ||\n",
-    "| Python | 3.11.5 |\n",
-    "| Pip | 24.2 |\n",
-    "| IDE | Jupyter Notebook |\n",
-    "| Operating System | macOS Sonoma 14.2.1 |\n",
-    "2. **Data Processing**\n",
-    "   - Uses Wikipedia dataset (100,000 entries) with embeddings(Precision: float32, Dimensions: 768) generated by Cohere\n",
-    "   - JSON data is generated from the dataset and stored in the databases\n",
-    "   - Stores data in both PostgreSQL and MongoDB\n",
-    "\n",
-    "3. **Performance Testing**\n",
-    "   - Tests different sizes of concurrent queries (1-400 queries)\n",
-    "   - Tests different insertion batch sizes and speed of insertion\n",
-    "\n",
-    "| Operation  | Metric | Description |\n",
-    "|------------|--------|-------------|\n",
-    "| Insertion  | Latency | Time taken to insert the data (average response time) |\n",
-    "|            | Throughput | Number of queries processed per second |\n",
-    "| Retrieval  | Latency | Time taken to retrieve the top n results (average response time) |\n",
-    "|            | Throughput | Number of queries processed per second |\n",
-    "|            | P95 Latency | Time taken to retrieve the top n results for 95% of the queries |\n",
-    "|            | Standard Deviation | How much the latency varies from the average latency |\n",
-    "\n",
-    "4. **Results Visualization**\n",
-    "   - Interactive animations showing request-response cycles\n",
-    "   - Comparative charts for latency and throughput\n",
-    "   - Performance analysis across different batch sizes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Part 1: Data Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Setting up the benchmark results dictionary `benchmark_results` and the batch sizes to test `CONCURRENT_QUERIES` and `TOTAL_QUERIES`\n",
-    "\n",
-    "- `benchmark_results` is a dictionary that will store the results of the benchmark tests\n",
-    "- `CONCURRENT_QUERIES` is a list of the number of queries that are run concurrently\n",
-    "- `TOTAL_QUERIES` is the total number of queries that are run\n",
-    "\n",
-    "Benchmark Configuration Example:\n",
-    "When testing with a concurrency level of 10:\n",
-    "- We run 100 iterations\n",
-    "- Each iteration runs 10 concurrent queries\n",
-    "- Total queries = 1,000 queries (TOTAL_ITERATIONS * CONCURRENT_QUERIES)\n",
-    "\n",
-    "NOTE: For each concurrency level in CONCURRENT_QUERIES:\n",
-    "1. Run TOTAL_QUERIES iterations\n",
-    "2. In each iteration, execute that many concurrent queries\n",
-    "3. Measure and collect latencies for all queries\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize the benchmark results dictionary\n",
-    "benchmark_results = {\"PostgreSQL\": {}, \"MongoDB\": {}}\n",
-    "\n",
-    "# The concurrency levels for benchmark testing\n",
-    "# Each level represents the number of simultaneous queries to execute\n",
-    "CONCURRENT_QUERIES = [\n",
-    "    1,\n",
-    "    2,\n",
-    "    4,\n",
-    "    5,\n",
-    "    8,\n",
-    "    10,\n",
-    "    12,\n",
-    "    16,\n",
-    "    20,\n",
-    "    24,\n",
-    "    32,\n",
-    "    40,\n",
-    "    48,\n",
-    "    50,\n",
-    "    56,\n",
-    "    64,\n",
-    "    72,\n",
-    "    80,\n",
-    "    88,\n",
-    "    96,\n",
-    "    100,\n",
-    "    200,\n",
-    "    400,\n",
-    "]\n",
-    "\n",
-    "# The total number of iterations to run for each concurrency level\n",
-    "TOTAL_QUERIES = 100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "\n",
-    "# Function to securely get and set environment variables\n",
-    "def set_env_securely(var_name, prompt):\n",
-    "    value = getpass.getpass(prompt)\n",
-    "    os.environ[var_name] = value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 1: Install Libraries\n",
-    "\n",
-    "All the libraries are installed using pip and facilitate the sourcing of data, embedding generation, and data visualization.\n",
-    "\n",
-    "- `datasets`: Hugging Face library for managing and preprocessing datasets across text, image, and audio (https://huggingface.co/datasets)\n",
-    "- `sentence_transformers`: For creating sentence embeddings for tasks like semantic search and clustering. (https://www.sbert.net/)\n",
-    "- `pandas`: A library for data manipulation and analysis with DataFrames and Series (https://pandas.pydata.org/)\n",
-    "- `matplotlib`: A library for creating static, interactive, and animated data visualizations (https://matplotlib.org/)\n",
-    "- `seaborn`: A library for creating statistical data visualizations (https://seaborn.pydata.org/)\n",
-    "- `cohere`: A library for generating embeddings and accessing the Cohere API or models (https://cohere.ai/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install --upgrade --quiet datasets sentence_transformers pandas matplotlib seaborn cohere"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "LyWZVRIqxVnV"
-   },
-   "source": [
-    "### Step 2: Data Loading\n",
-    "\n",
-    "The dataset for the benchmark is sourced from the Hugging Face Cohere Wikipedia dataset.\n",
-    "\n",
-    "The [Cohere/wikipedia-22-12-en-embeddings](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings) dataset on Hugging Face comprises English Wikipedia articles embedded using Cohere's multilingual-22-12 model. Each entry includes the article's title, text, URL, Wikipedia ID, view count, paragraph ID, language codes, and a 768-dimensional embedding vector. This dataset is valuable for tasks like semantic search, information retrieval, and NLP model training.\n",
-    "\n",
-    "For this benchmark, we are using 100,000 rows of the dataset and have removed the id, wiki_id, paragraph_id, langs and views columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 206
-    },
-    "id": "rCXZhJ9-vymv",
-    "outputId": "7f4210d7-5b8b-4c1d-89ab-7288623a8ca7"
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# Using 100,000 rows for testing, feel free to change this to any number of rows you want to test\n",
-    "# The wikipedia-22-12-en-embeddings dataset has approximately 35,000,000 rows and requires 120GB of memory to load\n",
-    "MAX_ROWS = 100000\n",
-    "\n",
-    "dataset = load_dataset(\n",
-    "    \"Cohere/wikipedia-22-12-en-embeddings\", split=\"train\", streaming=True\n",
-    ")\n",
-    "dataset_segment = dataset.take(MAX_ROWS)\n",
-    "\n",
-    "# Convert the dataset to a pandas dataframe\n",
-    "dataset_df = pd.DataFrame(dataset_segment)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add a JSON attribute to the dataset consisting of the title, text and url\n",
-    "dataset_df[\"json_data\"] = dataset_df.apply(\n",
-    "    lambda row: {\"title\": row[\"title\"], \"text\": row[\"text\"], \"url\": row[\"url\"]}, axis=1\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Remove the id field, wiki_id, paragraph_id, langs and views from the dataset\n",
-    "# This is to replicate the structure of dataset usually encountered in AI workloads, particularly in RAG systems where metadata is extracted from documents and stored.\n",
-    "dataset_df = dataset_df.drop(\n",
-    "    columns=[\"id\", \"wiki_id\", \"paragraph_id\", \"langs\", \"views\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Change the emb colomn name to embedding\n",
-    "dataset_df = dataset_df.rename(columns={\"emb\": \"embedding\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    .dataframe tbody tr th {\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "      <td>Deaths in 2022</td>\n",
-       "      <td>The following notable deaths occurred in 2022....</td>\n",
-       "      <td>https://en.wikipedia.org/wiki?curid=69407798</td>\n",
-       "      <td>[0.2865696847438812, -0.03181683272123337, 0.0...</td>\n",
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-       "      <th>1</th>\n",
-       "      <td>YouTube</td>\n",
-       "      <td>YouTube is a global online video sharing and s...</td>\n",
-       "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
-       "      <td>[-0.09689381718635559, 0.1619211882352829, -0....</td>\n",
-       "      <td>{'title': 'YouTube', 'text': 'YouTube is a glo...</td>\n",
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-       "      <td>In October 2006, YouTube was bought by Google ...</td>\n",
-       "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
-       "      <td>[0.1302049309015274, 0.265736848115921, 0.4018...</td>\n",
-       "      <td>{'title': 'YouTube', 'text': 'In October 2006,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>YouTube</td>\n",
-       "      <td>Since its purchase by Google, YouTube has expa...</td>\n",
-       "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
-       "      <td>[-0.09791257232427597, 0.13586106896400452, -0...</td>\n",
-       "      <td>{'title': 'YouTube', 'text': 'Since its purcha...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>YouTube</td>\n",
-       "      <td>YouTube has had an unprecedented social impact...</td>\n",
-       "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
-       "      <td>[-0.2641527056694031, 0.06968216598033905, -0....</td>\n",
-       "      <td>{'title': 'YouTube', 'text': 'YouTube has had ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            title                                               text  \\\n",
-       "0  Deaths in 2022  The following notable deaths occurred in 2022....   \n",
-       "1         YouTube  YouTube is a global online video sharing and s...   \n",
-       "2         YouTube  In October 2006, YouTube was bought by Google ...   \n",
-       "3         YouTube  Since its purchase by Google, YouTube has expa...   \n",
-       "4         YouTube  YouTube has had an unprecedented social impact...   \n",
-       "\n",
-       "                                            url  \\\n",
-       "0  https://en.wikipedia.org/wiki?curid=69407798   \n",
-       "1   https://en.wikipedia.org/wiki?curid=3524766   \n",
-       "2   https://en.wikipedia.org/wiki?curid=3524766   \n",
-       "3   https://en.wikipedia.org/wiki?curid=3524766   \n",
-       "4   https://en.wikipedia.org/wiki?curid=3524766   \n",
-       "\n",
-       "                                           embedding  \\\n",
-       "0  [0.2865696847438812, -0.03181683272123337, 0.0...   \n",
-       "1  [-0.09689381718635559, 0.1619211882352829, -0....   \n",
-       "2  [0.1302049309015274, 0.265736848115921, 0.4018...   \n",
-       "3  [-0.09791257232427597, 0.13586106896400452, -0...   \n",
-       "4  [-0.2641527056694031, 0.06968216598033905, -0....   \n",
-       "\n",
-       "                                           json_data  \n",
-       "0  {'title': 'Deaths in 2022', 'text': 'The follo...  \n",
-       "1  {'title': 'YouTube', 'text': 'YouTube is a glo...  \n",
-       "2  {'title': 'YouTube', 'text': 'In October 2006,...  \n",
-       "3  {'title': 'YouTube', 'text': 'Since its purcha...  \n",
-       "4  {'title': 'YouTube', 'text': 'YouTube has had ...  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset_df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "wOwXzXnjxHfb"
-   },
-   "source": [
-    "### Step 3: Embedding Generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set Cohere API key\n",
-    "set_env_securely(\"COHERE_API_KEY\", \"Enter your Cohere API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the Cohere API to generate embeddings for the test queries.\n",
-    "\n",
-    "Using the `embed-multilingual-v2.0` model. This is the same model used in the Cohere Wikipedia dataset.\n",
-    "\n",
-    "Embedding size is 768 dimensions and the precision is float32."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import List, Tuple\n",
-    "\n",
-    "import cohere\n",
-    "\n",
-    "# Initialize Cohere Client\n",
-    "co = cohere.Client()\n",
-    "\n",
-    "\n",
-    "def get_cohere_embeddings(\n",
-    "    sentences: List[str],\n",
-    "    model: str = \"embed-multilingual-v2.0\",\n",
-    "    input_type: str = \"search_document\",\n",
-    ") -> Tuple[List[float], List[int]]:\n",
-    "    \"\"\"\n",
-    "    Generates embeddings for the provided sentences using Cohere's embedding model.\n",
-    "\n",
-    "    Args:\n",
-    "    sentences (list of str): List of sentences to generate embeddings for.\n",
-    "\n",
-    "    Returns:\n",
-    "    Tuple[List[float], List[int]]: A tuple containing two lists of embeddings (float and int8).\n",
-    "    \"\"\"\n",
-    "    generated_embedding = co.embed(\n",
-    "        texts=sentences,\n",
-    "        model=\"embed-multilingual-v2.0\",\n",
-    "        input_type=\"search_document\",\n",
-    "        embedding_types=[\"float\"],\n",
-    "    ).embeddings\n",
-    "\n",
-    "    return generated_embedding.float[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Generate embeddings for the query templates used in benchmarking process\n",
-    "\n",
-    "Note: Doing this to avoid the overhead of generating embeddings for each query during the benchmark process\n",
-    "\n",
-    "Note: Feel free to add more queries to the query_templates list to test the performance of the vector database with a larger number of queries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query_templates = [\n",
-    "    \"When was YouTube officially launched, and by whom?\",\n",
-    "    \"What is YouTube's slogan introduced after Google's acquisition?\",\n",
-    "    \"How many hours of videos are collectively watched on YouTube daily?\",\n",
-    "    \"Which was the first video uploaded to YouTube, and when was it uploaded?\",\n",
-    "    \"What was the acquisition cost of YouTube by Google, and when was the deal finalized?\",\n",
-    "    \"What was the first YouTube video to reach one million views, and when did it happen?\",\n",
-    "    \"What are the three separate branches of the United States government?\",\n",
-    "    \"Which country has the highest documented incarceration rate and prison population?\",\n",
-    "    \"How many executions have occurred in the United States since 1977, and which countries have more?\",\n",
-    "    \"What percentage of the global military spending did the United States account for in 2019?\",\n",
-    "    \"How is the U.S. president elected?\",\n",
-    "    \"What cooling system innovation was included in the proposed venues for the World Cup in Qatar?\",\n",
-    "    \"What lawsuit was filed against Google in June 2020, and what was it about?\",\n",
-    "    \"How much was Google fined by CNIL in January 2022, and for what reason?\",\n",
-    "    \"When did YouTube join the NSA's PRISM program, according to reports?\",\n",
-    "]\n",
-    "\n",
-    "# For each query template question, generate an embedding\n",
-    "# NOTE: Doing this to avoid the overhead of generating embeddings for each query during the benchmark process\n",
-    "query_embeddings = [\n",
-    "    get_cohere_embeddings(sentences=[query], input_type=\"search_query\")\n",
-    "    for query in query_templates\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a dictionary with the query templates and their corresponding embeddings\n",
-    "query_embeddings_dict = {\n",
-    "    query: embedding for query, embedding in zip(query_templates, query_embeddings)\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>When was YouTube officially launched, and by whom?</th>\n",
-       "      <th>What is YouTube's slogan introduced after Google's acquisition?</th>\n",
-       "      <th>How many hours of videos are collectively watched on YouTube daily?</th>\n",
-       "      <th>Which was the first video uploaded to YouTube, and when was it uploaded?</th>\n",
-       "      <th>What was the acquisition cost of YouTube by Google, and when was the deal finalized?</th>\n",
-       "      <th>What was the first YouTube video to reach one million views, and when did it happen?</th>\n",
-       "      <th>What are the three separate branches of the United States government?</th>\n",
-       "      <th>Which country has the highest documented incarceration rate and prison population?</th>\n",
-       "      <th>How many executions have occurred in the United States since 1977, and which countries have more?</th>\n",
-       "      <th>What percentage of the global military spending did the United States account for in 2019?</th>\n",
-       "      <th>How is the U.S. president elected?</th>\n",
-       "      <th>What cooling system innovation was included in the proposed venues for the World Cup in Qatar?</th>\n",
-       "      <th>What lawsuit was filed against Google in June 2020, and what was it about?</th>\n",
-       "      <th>How much was Google fined by CNIL in January 2022, and for what reason?</th>\n",
-       "      <th>When did YouTube join the NSA's PRISM program, according to reports?</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.184692</td>\n",
-       "      <td>0.181519</td>\n",
-       "      <td>0.124939</td>\n",
-       "      <td>0.226074</td>\n",
-       "      <td>0.415771</td>\n",
-       "      <td>0.127075</td>\n",
-       "      <td>0.237915</td>\n",
-       "      <td>0.524902</td>\n",
-       "      <td>0.396484</td>\n",
-       "      <td>0.342529</td>\n",
-       "      <td>0.375488</td>\n",
-       "      <td>0.490234</td>\n",
-       "      <td>0.118774</td>\n",
-       "      <td>0.138428</td>\n",
-       "      <td>0.477783</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.262451</td>\n",
-       "      <td>0.019638</td>\n",
-       "      <td>0.022446</td>\n",
-       "      <td>0.130493</td>\n",
-       "      <td>0.049530</td>\n",
-       "      <td>0.195923</td>\n",
-       "      <td>0.418945</td>\n",
-       "      <td>-0.256348</td>\n",
-       "      <td>-0.112061</td>\n",
-       "      <td>-0.033020</td>\n",
-       "      <td>0.158081</td>\n",
-       "      <td>0.331055</td>\n",
-       "      <td>0.199219</td>\n",
-       "      <td>-0.065125</td>\n",
-       "      <td>0.128540</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.314697</td>\n",
-       "      <td>0.134399</td>\n",
-       "      <td>0.049500</td>\n",
-       "      <td>-0.228027</td>\n",
-       "      <td>0.077454</td>\n",
-       "      <td>-0.062683</td>\n",
-       "      <td>0.057587</td>\n",
-       "      <td>-0.576660</td>\n",
-       "      <td>0.051880</td>\n",
-       "      <td>-0.189575</td>\n",
-       "      <td>-0.151855</td>\n",
-       "      <td>-0.245117</td>\n",
-       "      <td>-0.053436</td>\n",
-       "      <td>-0.451172</td>\n",
-       "      <td>-0.274658</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.098145</td>\n",
-       "      <td>0.179077</td>\n",
-       "      <td>0.499756</td>\n",
-       "      <td>0.089600</td>\n",
-       "      <td>0.000246</td>\n",
-       "      <td>0.063354</td>\n",
-       "      <td>0.011215</td>\n",
-       "      <td>0.008095</td>\n",
-       "      <td>0.014961</td>\n",
-       "      <td>-0.099731</td>\n",
-       "      <td>-0.213135</td>\n",
-       "      <td>0.257812</td>\n",
-       "      <td>0.398926</td>\n",
-       "      <td>0.274170</td>\n",
-       "      <td>0.223389</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.058105</td>\n",
-       "      <td>0.311035</td>\n",
-       "      <td>0.307373</td>\n",
-       "      <td>0.076355</td>\n",
-       "      <td>0.416260</td>\n",
-       "      <td>0.398682</td>\n",
-       "      <td>-0.243042</td>\n",
-       "      <td>0.160278</td>\n",
-       "      <td>-0.098389</td>\n",
-       "      <td>0.036224</td>\n",
-       "      <td>-0.036987</td>\n",
-       "      <td>-0.351318</td>\n",
-       "      <td>0.334229</td>\n",
-       "      <td>0.256836</td>\n",
-       "      <td>0.176514</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>763</th>\n",
-       "      <td>0.455078</td>\n",
-       "      <td>0.499756</td>\n",
-       "      <td>0.428711</td>\n",
-       "      <td>0.464600</td>\n",
-       "      <td>0.513184</td>\n",
-       "      <td>0.464600</td>\n",
-       "      <td>0.291504</td>\n",
-       "      <td>0.306641</td>\n",
-       "      <td>0.328613</td>\n",
-       "      <td>0.367188</td>\n",
-       "      <td>0.238281</td>\n",
-       "      <td>0.497070</td>\n",
-       "      <td>0.384521</td>\n",
-       "      <td>0.507324</td>\n",
-       "      <td>0.436523</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>764</th>\n",
-       "      <td>0.267334</td>\n",
-       "      <td>0.156128</td>\n",
-       "      <td>0.244629</td>\n",
-       "      <td>0.340088</td>\n",
-       "      <td>0.174561</td>\n",
-       "      <td>0.487549</td>\n",
-       "      <td>0.038269</td>\n",
-       "      <td>0.058563</td>\n",
-       "      <td>0.609375</td>\n",
-       "      <td>0.080261</td>\n",
-       "      <td>-0.004959</td>\n",
-       "      <td>0.007370</td>\n",
-       "      <td>0.403564</td>\n",
-       "      <td>0.544922</td>\n",
-       "      <td>-0.016052</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>765</th>\n",
-       "      <td>0.260254</td>\n",
-       "      <td>0.039520</td>\n",
-       "      <td>-0.106445</td>\n",
-       "      <td>0.114807</td>\n",
-       "      <td>0.439697</td>\n",
-       "      <td>0.103333</td>\n",
-       "      <td>0.186890</td>\n",
-       "      <td>0.013084</td>\n",
-       "      <td>0.176880</td>\n",
-       "      <td>0.337646</td>\n",
-       "      <td>0.200439</td>\n",
-       "      <td>-0.355469</td>\n",
-       "      <td>0.326660</td>\n",
-       "      <td>0.316895</td>\n",
-       "      <td>0.338135</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>766</th>\n",
-       "      <td>0.001040</td>\n",
-       "      <td>0.315430</td>\n",
-       "      <td>0.045349</td>\n",
-       "      <td>0.113403</td>\n",
-       "      <td>0.190552</td>\n",
-       "      <td>-0.038116</td>\n",
-       "      <td>0.299072</td>\n",
-       "      <td>0.256348</td>\n",
-       "      <td>0.316650</td>\n",
-       "      <td>0.420410</td>\n",
-       "      <td>0.342529</td>\n",
-       "      <td>0.062683</td>\n",
-       "      <td>0.035522</td>\n",
-       "      <td>0.094238</td>\n",
-       "      <td>0.180420</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>767</th>\n",
-       "      <td>0.117249</td>\n",
-       "      <td>0.250244</td>\n",
-       "      <td>0.145020</td>\n",
-       "      <td>0.096802</td>\n",
-       "      <td>0.245605</td>\n",
-       "      <td>-0.057373</td>\n",
-       "      <td>-0.201904</td>\n",
-       "      <td>-0.123169</td>\n",
-       "      <td>-0.248047</td>\n",
-       "      <td>-0.034363</td>\n",
-       "      <td>0.039124</td>\n",
-       "      <td>-0.110229</td>\n",
-       "      <td>0.125610</td>\n",
-       "      <td>0.179321</td>\n",
-       "      <td>-0.066345</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>768 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     When was YouTube officially launched, and by whom?  \\\n",
-       "0                                             0.184692    \n",
-       "1                                             0.262451    \n",
-       "2                                            -0.314697    \n",
-       "3                                             0.098145    \n",
-       "4                                            -0.058105    \n",
-       "..                                                 ...    \n",
-       "763                                           0.455078    \n",
-       "764                                           0.267334    \n",
-       "765                                           0.260254    \n",
-       "766                                           0.001040    \n",
-       "767                                           0.117249    \n",
-       "\n",
-       "     What is YouTube's slogan introduced after Google's acquisition?  \\\n",
-       "0                                             0.181519                 \n",
-       "1                                             0.019638                 \n",
-       "2                                             0.134399                 \n",
-       "3                                             0.179077                 \n",
-       "4                                             0.311035                 \n",
-       "..                                                 ...                 \n",
-       "763                                           0.499756                 \n",
-       "764                                           0.156128                 \n",
-       "765                                           0.039520                 \n",
-       "766                                           0.315430                 \n",
-       "767                                           0.250244                 \n",
-       "\n",
-       "     How many hours of videos are collectively watched on YouTube daily?  \\\n",
-       "0                                             0.124939                     \n",
-       "1                                             0.022446                     \n",
-       "2                                             0.049500                     \n",
-       "3                                             0.499756                     \n",
-       "4                                             0.307373                     \n",
-       "..                                                 ...                     \n",
-       "763                                           0.428711                     \n",
-       "764                                           0.244629                     \n",
-       "765                                          -0.106445                     \n",
-       "766                                           0.045349                     \n",
-       "767                                           0.145020                     \n",
-       "\n",
-       "     Which was the first video uploaded to YouTube, and when was it uploaded?  \\\n",
-       "0                                             0.226074                          \n",
-       "1                                             0.130493                          \n",
-       "2                                            -0.228027                          \n",
-       "3                                             0.089600                          \n",
-       "4                                             0.076355                          \n",
-       "..                                                 ...                          \n",
-       "763                                           0.464600                          \n",
-       "764                                           0.340088                          \n",
-       "765                                           0.114807                          \n",
-       "766                                           0.113403                          \n",
-       "767                                           0.096802                          \n",
-       "\n",
-       "     What was the acquisition cost of YouTube by Google, and when was the deal finalized?  \\\n",
-       "0                                             0.415771                                      \n",
-       "1                                             0.049530                                      \n",
-       "2                                             0.077454                                      \n",
-       "3                                             0.000246                                      \n",
-       "4                                             0.416260                                      \n",
-       "..                                                 ...                                      \n",
-       "763                                           0.513184                                      \n",
-       "764                                           0.174561                                      \n",
-       "765                                           0.439697                                      \n",
-       "766                                           0.190552                                      \n",
-       "767                                           0.245605                                      \n",
-       "\n",
-       "     What was the first YouTube video to reach one million views, and when did it happen?  \\\n",
-       "0                                             0.127075                                      \n",
-       "1                                             0.195923                                      \n",
-       "2                                            -0.062683                                      \n",
-       "3                                             0.063354                                      \n",
-       "4                                             0.398682                                      \n",
-       "..                                                 ...                                      \n",
-       "763                                           0.464600                                      \n",
-       "764                                           0.487549                                      \n",
-       "765                                           0.103333                                      \n",
-       "766                                          -0.038116                                      \n",
-       "767                                          -0.057373                                      \n",
-       "\n",
-       "     What are the three separate branches of the United States government?  \\\n",
-       "0                                             0.237915                       \n",
-       "1                                             0.418945                       \n",
-       "2                                             0.057587                       \n",
-       "3                                             0.011215                       \n",
-       "4                                            -0.243042                       \n",
-       "..                                                 ...                       \n",
-       "763                                           0.291504                       \n",
-       "764                                           0.038269                       \n",
-       "765                                           0.186890                       \n",
-       "766                                           0.299072                       \n",
-       "767                                          -0.201904                       \n",
-       "\n",
-       "     Which country has the highest documented incarceration rate and prison population?  \\\n",
-       "0                                             0.524902                                    \n",
-       "1                                            -0.256348                                    \n",
-       "2                                            -0.576660                                    \n",
-       "3                                             0.008095                                    \n",
-       "4                                             0.160278                                    \n",
-       "..                                                 ...                                    \n",
-       "763                                           0.306641                                    \n",
-       "764                                           0.058563                                    \n",
-       "765                                           0.013084                                    \n",
-       "766                                           0.256348                                    \n",
-       "767                                          -0.123169                                    \n",
-       "\n",
-       "     How many executions have occurred in the United States since 1977, and which countries have more?  \\\n",
-       "0                                             0.396484                                                   \n",
-       "1                                            -0.112061                                                   \n",
-       "2                                             0.051880                                                   \n",
-       "3                                             0.014961                                                   \n",
-       "4                                            -0.098389                                                   \n",
-       "..                                                 ...                                                   \n",
-       "763                                           0.328613                                                   \n",
-       "764                                           0.609375                                                   \n",
-       "765                                           0.176880                                                   \n",
-       "766                                           0.316650                                                   \n",
-       "767                                          -0.248047                                                   \n",
-       "\n",
-       "     What percentage of the global military spending did the United States account for in 2019?  \\\n",
-       "0                                             0.342529                                            \n",
-       "1                                            -0.033020                                            \n",
-       "2                                            -0.189575                                            \n",
-       "3                                            -0.099731                                            \n",
-       "4                                             0.036224                                            \n",
-       "..                                                 ...                                            \n",
-       "763                                           0.367188                                            \n",
-       "764                                           0.080261                                            \n",
-       "765                                           0.337646                                            \n",
-       "766                                           0.420410                                            \n",
-       "767                                          -0.034363                                            \n",
-       "\n",
-       "     How is the U.S. president elected?  \\\n",
-       "0                              0.375488   \n",
-       "1                              0.158081   \n",
-       "2                             -0.151855   \n",
-       "3                             -0.213135   \n",
-       "4                             -0.036987   \n",
-       "..                                  ...   \n",
-       "763                            0.238281   \n",
-       "764                           -0.004959   \n",
-       "765                            0.200439   \n",
-       "766                            0.342529   \n",
-       "767                            0.039124   \n",
-       "\n",
-       "     What cooling system innovation was included in the proposed venues for the World Cup in Qatar?  \\\n",
-       "0                                             0.490234                                                \n",
-       "1                                             0.331055                                                \n",
-       "2                                            -0.245117                                                \n",
-       "3                                             0.257812                                                \n",
-       "4                                            -0.351318                                                \n",
-       "..                                                 ...                                                \n",
-       "763                                           0.497070                                                \n",
-       "764                                           0.007370                                                \n",
-       "765                                          -0.355469                                                \n",
-       "766                                           0.062683                                                \n",
-       "767                                          -0.110229                                                \n",
-       "\n",
-       "     What lawsuit was filed against Google in June 2020, and what was it about?  \\\n",
-       "0                                             0.118774                            \n",
-       "1                                             0.199219                            \n",
-       "2                                            -0.053436                            \n",
-       "3                                             0.398926                            \n",
-       "4                                             0.334229                            \n",
-       "..                                                 ...                            \n",
-       "763                                           0.384521                            \n",
-       "764                                           0.403564                            \n",
-       "765                                           0.326660                            \n",
-       "766                                           0.035522                            \n",
-       "767                                           0.125610                            \n",
-       "\n",
-       "     How much was Google fined by CNIL in January 2022, and for what reason?  \\\n",
-       "0                                             0.138428                         \n",
-       "1                                            -0.065125                         \n",
-       "2                                            -0.451172                         \n",
-       "3                                             0.274170                         \n",
-       "4                                             0.256836                         \n",
-       "..                                                 ...                         \n",
-       "763                                           0.507324                         \n",
-       "764                                           0.544922                         \n",
-       "765                                           0.316895                         \n",
-       "766                                           0.094238                         \n",
-       "767                                           0.179321                         \n",
-       "\n",
-       "     When did YouTube join the NSA's PRISM program, according to reports?  \n",
-       "0                                             0.477783                     \n",
-       "1                                             0.128540                     \n",
-       "2                                            -0.274658                     \n",
-       "3                                             0.223389                     \n",
-       "4                                             0.176514                     \n",
-       "..                                                 ...                     \n",
-       "763                                           0.436523                     \n",
-       "764                                          -0.016052                     \n",
-       "765                                           0.338135                     \n",
-       "766                                           0.180420                     \n",
-       "767                                          -0.066345                     \n",
-       "\n",
-       "[768 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# View the first 5 query embeddings as a dataframe\n",
-    "pd.DataFrame(query_embeddings_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Part 2: Semantic Search with PostgreSQL and PgVector\n",
-    "\n",
-    "In this section, we create a PostgreSQL database with the PgVector extension and insert the dataset into the database.\n",
-    "\n",
-    "The table `wikipedia_data` is created with the following columns:\n",
-    "- `id`: The unique identifier for each row\n",
-    "- `title`: The title of the Wikipedia article\n",
-    "- `text`: The text of the Wikipedia article\n",
-    "- `url`: The URL of the Wikipedia article\n",
-    "- `json_data`: The JSON data of the Wikipedia article\n",
-    "- `embedding`: The embedding vector for the Wikipedia article\n",
-    "\n",
-    "The table is created with a HNSW index with m=16, ef_construction=64 and cosine similarity (these are the default parameters for the HNSW index in pgvector).\n",
-    "- `HNSW`: Hierarchical Navigable Small World graphs are a type of graph-based index that are used for efficient similarity search.\n",
-    "- `m=16`: The number of edges per node in the graph\n",
-    "- `ef_construction=64`: Short for exploration factor construction, is the number of edges to build during the index construction phase\n",
-    "- `ef_search=100`: Short for exploration factor search, is the number of edges to search during the index search phase\n",
-    "- `cosine similarity`: The similarity metric used for the index (formula: dot product(A, B) / (|A||B|))\n",
-    "- `cosine distance`: The distance metric calculated using cosine similarity (1 - cosine similarity)\n",
-    "\n",
-    "We perform a semantic search on the database using a single data point of the query templates and their corresponding embeddings.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0PM-dnDtxQBW"
-   },
-   "source": [
-    "### Step 1: Install Libraries\n",
-    "\n",
-    "- `pgvector` (0.3.6): A PostgreSQL extension for vector similarity search (https://github.com/pgvector/pgvector)\n",
-    "- `psycopg` (3.2.3): A PostgreSQL database adapter for Python (https://www.psycopg.org/)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "A5GwoxiSfWgv",
-    "outputId": "157e4760-e25c-45cd-d1d5-3eb3fdfea593"
-   },
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet pgvector \"psycopg[binary]\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 2: Installing PostgreSQL and PgVector\n",
-    "\n",
-    "PostgreSQL and PgVector are installed using docker.\n",
-    "\n",
-    "The PostgreSQL docker image is pulled from the [postgres](https://hub.docker.com/_/postgres) repository.\n",
-    "\n",
-    "The PgVector docker image is pulled from the [pgvector/pgvector](https://hub.docker.com/r/pgvector/pgvector) repository.\n",
-    "\n",
-    "Find more instructions on installing PostgreSQL and PgVector [here](https://github.com/pgvector/pgvector?tab=readme-ov-file#docker): "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 3: Create Postgres Table\n",
-    "\n",
-    "- `id`: The unique identifier for each row\n",
-    "- `title`: The title of the Wikipedia article\n",
-    "- `text`: The text of the Wikipedia article\n",
-    "- `url`: The URL of the Wikipedia article\n",
-    "- `json_data`: The JSON data of the Wikipedia article\n",
-    "- `embedding`: The embedding vector for the Wikipedia article\n",
-    "\n",
-    "NOTE: JSON data `json_data` in the dataset is stored as a JSONB column in Postgres to mirror the use of binary formatted data in MongoDB via BSON."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_table(connection):\n",
-    "    with connection.cursor() as cur:\n",
-    "        # Drop table if it already exists\n",
-    "        cur.execute(\"DROP TABLE IF EXISTS wikipedia_data\")\n",
-    "\n",
-    "        # Create the table with the appropriate structure\n",
-    "        cur.execute(\n",
-    "            \"\"\"\n",
-    "            CREATE TABLE wikipedia_data (\n",
-    "                id bigserial PRIMARY KEY,\n",
-    "                title text,\n",
-    "                text text,\n",
-    "                url text,\n",
-    "                json_data jsonb,\n",
-    "                embedding vector(768)\n",
-    "            )\n",
-    "        \"\"\"\n",
-    "        )\n",
-    "\n",
-    "        # Create HNSW index for vector similarity search with cosine similarity\n",
-    "        cur.execute(\n",
-    "            \"\"\"\n",
-    "            CREATE INDEX ON wikipedia_data \n",
-    "            USING hnsw (embedding vector_cosine_ops) \n",
-    "            WITH (m = 16, ef_construction = 64);\n",
-    "        \"\"\"\n",
-    "        )\n",
-    "\n",
-    "        print(\"Table and index created successfully\")\n",
-    "        connection.commit()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 4: Define insert function\n",
-    "\n",
-    "For inserting JSON data, we convert the Python Dictionary in the `json_data` attribute to a JSON string using the `json.dumps()` function.\n",
-    "\n",
-    "This is a serilization process that converts the Python Dictionary in the `json_data` attribute to a JSON string that is stored as binary data in the database."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import time\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def insert_data_to_postgres(dataframe, connection, database_type=\"PostgreSQL\"):\n",
-    "    \"\"\"\n",
-    "    Insert data into the PostgreSQL database.\n",
-    "\n",
-    "    Args:\n",
-    "    dataframe (pandas.DataFrame): The dataframe containing the data to insert.\n",
-    "    connection (psycopg.extensions.connection): The connection to the PostgreSQL database.\n",
-    "    database_type (str): The type of database (default: \"PostgreSQL\").\n",
-    "    \"\"\"\n",
-    "    start_time = time.time()\n",
-    "    total_rows = len(dataframe)\n",
-    "\n",
-    "    try:\n",
-    "        with connection.cursor() as cur:\n",
-    "            # Create a list of tuples for insertion, filtering out rows with invalid embeddings\n",
-    "            data_tuples = []\n",
-    "            for _, row in dataframe.iterrows():\n",
-    "                data_tuple = (\n",
-    "                    row[\"title\"],\n",
-    "                    row[\"text\"],\n",
-    "                    row[\"url\"],\n",
-    "                    json.dumps(row[\"json_data\"]),  # Convert dict to JSON string\n",
-    "                    row[\"embedding\"],\n",
-    "                )\n",
-    "                data_tuples.append(data_tuple)\n",
-    "\n",
-    "            if not data_tuples:\n",
-    "                raise ValueError(\"No valid data tuples to insert\")\n",
-    "\n",
-    "            cur.executemany(\n",
-    "                \"\"\"\n",
-    "                INSERT INTO wikipedia_data \n",
-    "                (title, text, url, json_data, embedding)\n",
-    "                VALUES (%s, %s, %s, %s, %s)\n",
-    "                \"\"\",\n",
-    "                data_tuples,\n",
-    "            )\n",
-    "\n",
-    "            connection.commit()\n",
-    "\n",
-    "    except Exception as e:\n",
-    "        print(f\"Error during bulk insert: {e}\")\n",
-    "        connection.rollback()\n",
-    "        raise e\n",
-    "\n",
-    "    end_time = time.time()\n",
-    "    total_time = end_time - start_time\n",
-    "    rows_per_second = len(data_tuples) / total_time\n",
-    "\n",
-    "    # print(f\"\\nInsertion Statistics:\")\n",
-    "    # print(f\"Total time: {total_time:.2f} seconds\")\n",
-    "    # print(f\"Average insertion rate: {rows_per_second:.2f} rows/second\")\n",
-    "    # print(f\"Total rows inserted: {len(data_tuples)}\")\n",
-    "    # print(f\"Rows skipped: {total_rows - len(data_tuples)}\")\n",
-    "\n",
-    "    # Store results in benchmark dictionary\n",
-    "    if database_type not in benchmark_results:\n",
-    "        benchmark_results[database_type] = {}\n",
-    "\n",
-    "    benchmark_results[database_type][\"insert_time\"] = {\n",
-    "        \"total_time\": total_time,\n",
-    "        \"rows_per_second\": rows_per_second,\n",
-    "        \"total_rows\": total_rows,\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 5: Insert Data into Postgres"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Table and index created successfully\n",
-      "\n",
-      "Insertion Statistics:\n",
-      "Total time: 462.10 seconds\n",
-      "Average insertion rate: 216.40 rows/second\n",
-      "Total rows inserted: 100000\n",
-      "Rows skipped: 0\n",
-      "Connection closed\n"
-     ]
     }
-   ],
-   "source": [
-    "import psycopg\n",
-    "from pgvector.psycopg import register_vector\n",
-    "\n",
-    "try:\n",
-    "    # Connect to PostgreSQL\n",
-    "    conn = psycopg.connect(\n",
-    "        \"dbname=vector_db user=postgres password=test host=127.0.0.1\"\n",
-    "    )\n",
-    "\n",
-    "    # Enable the pgvector extension\n",
-    "    conn.execute(\"CREATE EXTENSION IF NOT EXISTS vector\")\n",
-    "\n",
-    "    # Register vector type to handle embedding data as vector data types\n",
-    "    register_vector(conn)\n",
-    "\n",
-    "    # Step 1: Create the table\n",
-    "    create_table(conn)\n",
-    "\n",
-    "    # Step 2: Insert the expanded dataset into the table\n",
-    "    insert_data_to_postgres(dataset_df, conn)\n",
-    "\n",
-    "except Exception as e:\n",
-    "    print(\"Failed to execute:\", e)\n",
-    "finally:\n",
-    "    # Close the connection\n",
-    "    conn.close()\n",
-    "    print(\"Connection closed\")"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 6: Define semantic search function\n",
-    "\n",
-    "To avoid exhasuting API key usage, we will fetch the query embedding from the `query_embeddings_dict` dictionary.\n",
-    "\n",
-    "In the `semantic_search_with_postgres` function, we set the HNSW ef parameter to 100 using the `execute_command` function.\n",
-    "\n",
-    "This is to set the exploration factor for the HNSW index to 100. And corresponds to the number of nodes/candidates to search during the index search phase.\n",
-    "A node corresponds to a vector in the index.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def semantic_search_with_postgres(query, connection, top_n=5):\n",
-    "    # Take a query embedding from the query_embeddings_dict\n",
-    "    query_embedding = query_embeddings_dict[query]\n",
-    "\n",
-    "    with connection.cursor() as cur:\n",
-    "        # Set the HNSW ef parameter using execute_command\n",
-    "        cur.execute(\"SET hnsw.ef_search = 100\")\n",
-    "        connection.commit()\n",
-    "\n",
-    "        # Then perform the semantic search query\n",
-    "        cur.execute(\n",
-    "            \"\"\"\n",
-    "            SELECT title, text, url, json_data,\n",
-    "                   embedding <=> %s::vector AS similarity\n",
-    "            FROM wikipedia_data\n",
-    "            ORDER BY similarity ASC\n",
-    "            LIMIT %s\n",
-    "            \"\"\",\n",
-    "            (query_embedding, top_n),\n",
-    "        )\n",
-    "\n",
-    "        # Fetch and return the top results\n",
-    "        results = cur.fetchall()\n",
-    "\n",
-    "        # Format results as list of dictionaries for easier handling\n",
-    "        formatted_results = []\n",
-    "        for r in results:\n",
-    "            formatted_results.append(\n",
-    "                {\n",
-    "                    \"title\": r[0],\n",
-    "                    \"text\": r[1],\n",
-    "                    \"url\": r[2],\n",
-    "                    \"json_data\": r[3],\n",
-    "                    \"similarity\": r[4],\n",
-    "                }\n",
-    "            )\n",
-    "\n",
-    "        return formatted_results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 7: Running a quick example of semantic search with postgres and pgvector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Title: YouTube\n",
-      "Text: YouTube announced the project in September 2016 at an event in India. It was launched in India in February 2017, and expanded in November 2017 to 14 other countries, including Nigeria, Indonesia, Thailand, Malaysia, Vietnam, the Philippines, Kenya, and South Africa. It was rolled out in 130 countries worldwide, including Brazil, Mexico, Turkey, and Iraq on February 1, 2018. Before it shut down, the app was available to around 60% of the world's population.\n",
-      "URL: https://en.wikipedia.org/wiki?curid=3524766\n",
-      "JSON Data: {'url': 'https://en.wikipedia.org/wiki?curid=3524766', 'text': \"YouTube announced the project in September 2016 at an event in India. It was launched in India in February 2017, and expanded in November 2017 to 14 other countries, including Nigeria, Indonesia, Thailand, Malaysia, Vietnam, the Philippines, Kenya, and South Africa. It was rolled out in 130 countries worldwide, including Brazil, Mexico, Turkey, and Iraq on February 1, 2018. Before it shut down, the app was available to around 60% of the world's population.\", 'title': 'YouTube'}\n",
-      "Similarity Score: 0.9034\n",
-      "--------------------------------------------------------------------------------\n",
-      "\n",
-      "Title: YouTube\n",
-      "Text: The mobile version of the site was relaunched based on HTML5 in July 2010, avoiding the need to use Adobe Flash Player and optimized for use with touch screen controls. The mobile version is also available as an app for the Android platform.\n",
-      "URL: https://en.wikipedia.org/wiki?curid=3524766\n",
-      "JSON Data: {'url': 'https://en.wikipedia.org/wiki?curid=3524766', 'text': 'The mobile version of the site was relaunched based on HTML5 in July 2010, avoiding the need to use Adobe Flash Player and optimized for use with touch screen controls. The mobile version is also available as an app for the Android platform.', 'title': 'YouTube'}\n",
-      "Similarity Score: 0.8969\n",
-      "--------------------------------------------------------------------------------\n",
-      "\n",
-      "Title: YouTube\n",
-      "Text: In January 2009, YouTube launched \"YouTube for TV\", a version of the website tailored for set-top boxes and other TV-based media devices with web browsers, initially allowing its videos to be viewed on the PlayStation 3 and Wii video game consoles.\n",
-      "URL: https://en.wikipedia.org/wiki?curid=3524766\n",
-      "JSON Data: {'url': 'https://en.wikipedia.org/wiki?curid=3524766', 'text': 'In January 2009, YouTube launched \"YouTube for TV\", a version of the website tailored for set-top boxes and other TV-based media devices with web browsers, initially allowing its videos to be viewed on the PlayStation 3 and Wii video game consoles.', 'title': 'YouTube'}\n",
-      "Similarity Score: 0.8967\n",
-      "--------------------------------------------------------------------------------\n",
-      "\n",
-      "Title: YouTube\n",
-      "Text: Later the same year, \"YouTube Feather\" was introduced as a lightweight alternative website for countries with limited internet speeds.\n",
-      "URL: https://en.wikipedia.org/wiki?curid=3524766\n",
-      "JSON Data: {'url': 'https://en.wikipedia.org/wiki?curid=3524766', 'text': 'Later the same year, \"YouTube Feather\" was introduced as a lightweight alternative website for countries with limited internet speeds.', 'title': 'YouTube'}\n",
-      "Similarity Score: 0.8951\n",
-      "--------------------------------------------------------------------------------\n",
-      "\n",
-      "Title: Twitch (service)\n",
-      "Text: On May 18, 2014, \"Variety\" first reported that Google had reached a preliminary deal to acquire Twitch through its YouTube subsidiary for approximately .\n",
-      "URL: https://en.wikipedia.org/wiki?curid=33548254\n",
-      "JSON Data: {'url': 'https://en.wikipedia.org/wiki?curid=33548254', 'text': 'On May 18, 2014, \"Variety\" first reported that Google had reached a preliminary deal to acquire Twitch through its YouTube subsidiary for approximately .', 'title': 'Twitch (service)'}\n",
-      "Similarity Score: 0.8928\n",
-      "--------------------------------------------------------------------------------\n",
-      "Connection closed\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Connect to PostgreSQL\n",
-    "try:\n",
-    "    conn = psycopg.connect(\n",
-    "        \"dbname=vector_db user=postgres password=test host=127.0.0.1\"\n",
-    "    )\n",
-    "\n",
-    "    # Run semantic search with a sample query\n",
-    "    query_text = \"When was YouTube officially launched, and by whom?\"\n",
-    "    results = semantic_search_with_postgres(query_text, conn, top_n=5)\n",
-    "\n",
-    "    # Print results in a formatted way\n",
-    "    for result in results:\n",
-    "        print(f\"\\nTitle: {result['title']}\")\n",
-    "        print(f\"Text: {result['text']}\")\n",
-    "        print(f\"URL: {result['url']}\")\n",
-    "        print(f\"JSON Data: {result['json_data']}\")\n",
-    "        print(f\"Similarity Score: {1- result['similarity']:.4f}\")\n",
-    "        print(\"-\" * 80)\n",
-    "\n",
-    "except Exception as e:\n",
-    "    print(\"Failed to connect or execute query:\", e)\n",
-    "finally:\n",
-    "    conn.close()\n",
-    "    print(\"Connection closed\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Part 3: Semantic Search with MongoDB Atlas Vector Search"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 1: Install Libraries\n",
-    "\n",
-    "- `pymongo` (4.10.1): A Python driver for MongoDB (https://pymongo.readthedocs.io/en/stable/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --quiet --upgrade pymongo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 2: Installing MongoDB via Atlas CLI\n",
-    "\n",
-    "The Atlas CLI is a command line interface built specifically for MongoDB Atlas. \n",
-    "Interact with your Atlas database deployments and Atlas Search from the terminal with short, intuitive commands, so you can accomplish complex database management tasks in seconds.\n",
-    "\n",
-    "You can follow the instructions [here](https://www.mongodb.com/docs/atlas/cli/current/install-atlas-cli/#complete-the-prerequisites-3) to install the Atlas CLI using docker(other options are available) and get a local MongoDB database instance running.\n",
-    "\n",
-    "Follow the steps [here](https://www.mongodb.com/docs/atlas/cli/current/atlas-cli-docker/#follow-these-steps) to run Altas CLI commands with Docker.\n",
-    "\n",
-    "Find more information on the Atlas CLI [here](https://www.mongodb.com/docs/atlas/cli/): "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 3: Connect to MongoDB and Create Database and Collection"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After installing the Atlas CLI, you can run the following command to connect to your MongoDB database:\n",
-    "1. atlas deployments connect\n",
-    "2. You will be prompted to specificy \"How would you like to connect to local9410\"\n",
-    "3. Select connectionString\n",
-    "4. Copy the connection string and paste it into the MONGO_URI environment variable\n",
-    "\n",
-    "More information [here](https://www.mongodb.com/docs/atlas/cli/current/atlas-cli-deploy-fts/#connect-to-the-deployment)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set MongoDB URI\n",
-    "# Example: mongodb://localhost:54516/?directConnection=true\n",
-    "set_env_securely(\"MONGO_URI\", \"Enter your MONGO URI: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following code blocks below we do the following:\n",
-    "1. Establish a connection to the MongoDB database\n",
-    "2. Create a database and collection if they do not already exist\n",
-    "3. Delete all data in the collection if it already exists\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pymongo\n",
-    "\n",
-    "\n",
-    "def get_mongo_client(mongo_uri):\n",
-    "    \"\"\"Establish and validate connection to the MongoDB.\"\"\"\n",
-    "\n",
-    "    client = pymongo.MongoClient(\n",
-    "        mongo_uri, appname=\"devrel.showcase.postgres_vs_mongodb.python\"\n",
-    "    )\n",
-    "\n",
-    "    # Validate the connection\n",
-    "    ping_result = client.admin.command(\"ping\")\n",
-    "    if ping_result.get(\"ok\") == 1.0:\n",
-    "        # Connection successful\n",
-    "        print(\"Connection to MongoDB successful\")\n",
-    "        return client\n",
-    "    else:\n",
-    "        print(\"Connection to MongoDB failed\")\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "MONGO_URI = os.environ[\"MONGO_URI\"]\n",
-    "if not MONGO_URI:\n",
-    "    print(\"MONGO_URI not set in environment variables\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connection to MongoDB successful\n",
-      "Collection 'wikipedia_data' already exists.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pymongo.errors import CollectionInvalid\n",
-    "\n",
-    "mongo_client = get_mongo_client(MONGO_URI)\n",
-    "\n",
-    "DB_NAME = \"vector_db\"\n",
-    "COLLECTION_NAME = \"wikipedia_data\"\n",
-    "\n",
-    "# Create or get the database\n",
-    "db = mongo_client[DB_NAME]\n",
-    "\n",
-    "# Check if the collection exists\n",
-    "if COLLECTION_NAME not in db.list_collection_names():\n",
-    "    try:\n",
-    "        # Create the collection\n",
-    "        db.create_collection(COLLECTION_NAME)\n",
-    "        print(f\"Collection '{COLLECTION_NAME}' created successfully.\")\n",
-    "    except CollectionInvalid as e:\n",
-    "        print(f\"Error creating collection: {e}\")\n",
-    "else:\n",
-    "    print(f\"Collection '{COLLECTION_NAME}' already exists.\")\n",
-    "\n",
-    "# Assign the collection\n",
-    "collection = db[COLLECTION_NAME]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DeleteResult({'n': 0, 'electionId': ObjectId('7fffffff0000000000000005'), 'opTime': {'ts': Timestamp(1733982920, 1), 't': 5}, 'ok': 1.0, '$clusterTime': {'clusterTime': Timestamp(1733982920, 1), 'signature': {'hash': b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00', 'keyId': 0}}, 'operationTime': Timestamp(1733982920, 1)}, acknowledged=True)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "collection.delete_many({})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 4: Vector Index Creation\n",
-    "\n",
-    "The `setup_vector_search_index` function creates a vector search index for the MongoDB collection.\n",
-    "\n",
-    "The `index_name` parameter is the name of the index to create.\n",
-    "\n",
-    "The `embedding_field_name` parameter is the name of the field containing the text embeddings on each document within the wikipedia_data collection.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedding_field_name = \"embedding\"\n",
-    "vector_search_index_name = \"vector_index\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "\n",
-    "from pymongo.operations import SearchIndexModel\n",
-    "\n",
-    "\n",
-    "def setup_vector_search_index(collection, index_name=\"vector_index\"):\n",
-    "    \"\"\"\n",
-    "    Setup a vector search index for a MongoDB collection and wait for 30 seconds.\n",
-    "\n",
-    "    Args:\n",
-    "    collection: MongoDB collection object\n",
-    "    index_definition: Dictionary containing the index definition\n",
-    "    index_name: Name of the index (default: \"vector_index\")\n",
-    "    \"\"\"\n",
-    "    new_vector_search_index_model = SearchIndexModel(\n",
-    "        definition={\n",
-    "            \"fields\": [\n",
-    "                {\n",
-    "                    \"type\": \"vector\",\n",
-    "                    \"path\": \"embedding\",\n",
-    "                    \"numDimensions\": 768,\n",
-    "                    \"similarity\": \"cosine\",\n",
-    "                }\n",
-    "            ]\n",
-    "        },\n",
-    "        name=index_name,\n",
-    "        type=\"vectorSearch\",\n",
-    "    )\n",
-    "\n",
-    "    # Create the new index\n",
-    "    try:\n",
-    "        result = collection.create_search_index(model=new_vector_search_index_model)\n",
-    "        print(f\"Creating index '{index_name}'...\")\n",
-    "\n",
-    "        # Wait for 30 seconds\n",
-    "        print(f\"Waiting for 30 seconds to allow index '{index_name}' to be created...\")\n",
-    "        time.sleep(30)\n",
-    "\n",
-    "        print(f\"30-second wait completed for index '{index_name}'.\")\n",
-    "        return result\n",
-    "\n",
-    "    except Exception as e:\n",
-    "        print(f\"Error creating new vector search index '{index_name}': {e!s}\")\n",
-    "        return None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Creating index 'vector_index'...\n",
-      "Waiting for 30 seconds to allow index 'vector_index' to be created...\n",
-      "30-second wait completed for index 'vector_index'.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'vector_index'"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "setup_vector_search_index(collection, \"vector_index\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 5: Define Insert Data Function\n",
-    "\n",
-    "Because of the affinity of MongoDB for JSON data, we don't have to convert the Python Dictionary in the `json_data` attribute to a JSON string using the `json.dumps()` function. Instead, we can directly insert the Python Dictionary into the MongoDB collection.\n",
-    "\n",
-    "This reduced the operational overhead of the insertion processes in AI workloads.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def insert_data_to_mongodb(dataframe, collection, database_type=\"MongoDB\"):\n",
-    "    start_time = time.time()\n",
-    "    total_rows = len(dataframe)\n",
-    "\n",
-    "    try:\n",
-    "        # Convert DataFrame to list of dictionaries for MongoDB insertion\n",
-    "        documents = dataframe.to_dict(\"records\")\n",
-    "\n",
-    "        # Use insert_many for better performance\n",
-    "        result = collection.insert_many(documents)\n",
-    "\n",
-    "        end_time = time.time()\n",
-    "        total_time = end_time - start_time\n",
-    "        rows_per_second = total_rows / total_time\n",
-    "\n",
-    "        # print(f\"\\nMongoDB Insertion Statistics:\")\n",
-    "        # print(f\"Total time: {total_time:.2f} seconds\")\n",
-    "        # print(f\"Average insertion rate: {rows_per_second:.2f} rows/second\")\n",
-    "        # print(f\"Total rows inserted: {len(result.inserted_ids)}\")\n",
-    "\n",
-    "        # Store results in benchmark dictionary\n",
-    "        if database_type not in benchmark_results:\n",
-    "            benchmark_results[database_type] = {}\n",
-    "\n",
-    "        benchmark_results[database_type][\"insert_time\"] = {\n",
-    "            \"total_time\": total_time,\n",
-    "            \"rows_per_second\": rows_per_second,\n",
-    "            \"total_rows\": total_rows,\n",
-    "        }\n",
-    "\n",
-    "        return True\n",
-    "\n",
-    "    except Exception as e:\n",
-    "        print(f\"Error during MongoDB insertion: {e}\")\n",
-    "        return False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 6: Insert Data into MongoDB\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "documents = dataset_df.to_dict(\"records\")\n",
-    "success = insert_data_to_mongodb(dataset_df, collection)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'insert_time': {'total_time': 90.82945108413696, 'rows_per_second': 1100.96448680911, 'total_rows': 100000}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(benchmark_results[\"MongoDB\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 7: Define Semantic Search Function\n",
-    "\n",
-    "The `semantic_search_with_mongodb` function performs a vector search in the MongoDB collection based on the user query.\n",
-    "\n",
-    "- `user_query` parameter is the user's query string.\n",
-    "- `collection` parameter is the MongoDB collection to search.\n",
-    "- `top_n` parameter is the number of top results to return.\n",
-    "- `vector_search_index_name` parameter is the name of the vector search index to use for the search.\n",
-    "\n",
-    "The `numCandidates` parameter is the number of candidate matches to consider. This is set to 100 to match the number of candidate matches to consider in the PostgreSQL vector search.\n",
-    "\n",
-    "Another point to note is the queries in MongoDB are performed using the `aggregate` function enabled by the MongoDB Query Language(MQL).\n",
-    "\n",
-    "This allows for more flexibility in the queries and the ability to perform more complex searches. And data processing opreations can be defined as stages in the pipeline. If you are a data engineer, data scientist or ML Engineer, the concept of pipeline processing is a key concept.\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def semantic_search_with_mongodb(\n",
-    "    user_query, collection, top_n=5, vector_search_index_name=\"vector_index\"\n",
-    "):\n",
-    "    \"\"\"\n",
-    "    Perform a vector search in the MongoDB collection based on the user query.\n",
-    "\n",
-    "    Args:\n",
-    "    user_query (str): The user's query string.\n",
-    "    collection (MongoCollection): The MongoDB collection to search.\n",
-    "    additional_stages (list): Additional aggregation stages to include in the pipeline.\n",
-    "    vector_search_index_name (str): The name of the vector search index.\n",
-    "\n",
-    "    Returns:\n",
-    "    list: A list of matching documents.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    # Take a query embedding from the query_embeddings_dict\n",
-    "    query_embedding = query_embeddings_dict[user_query]\n",
-    "\n",
-    "    if query_embedding is None:\n",
-    "        return \"Invalid query or embedding generation failed.\"\n",
-    "\n",
-    "    # Define the vector search stage\n",
-    "    vector_search_stage = {\n",
-    "        \"$vectorSearch\": {\n",
-    "            \"index\": vector_search_index_name,  # specifies the index to use for the search\n",
-    "            \"queryVector\": query_embedding,  # the vector representing the query\n",
-    "            \"path\": \"embedding\",  # field in the documents containing the vectors to search against\n",
-    "            \"numCandidates\": 100,  # number of candidate matches to consider\n",
-    "            \"limit\": top_n,  # return top n matches\n",
-    "        }\n",
-    "    }\n",
-    "\n",
-    "    project_stage = {\n",
-    "        \"$project\": {\n",
-    "            \"_id\": 0,  # Exclude the _id field\n",
-    "            \"title\": 1,\n",
-    "            \"text\": 1,\n",
-    "            \"url\": 1,\n",
-    "            \"score\": {\"$meta\": \"vectorSearchScore\"},  # Include the search score\n",
-    "        }\n",
-    "    }\n",
-    "\n",
-    "    # Define the aggregate pipeline with the vector search stage\n",
-    "    pipeline = [vector_search_stage, project_stage]\n",
-    "\n",
-    "    # Execute the search\n",
-    "    results = collection.aggregate(pipeline)\n",
-    "    return list(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query_text = \"When was YouTube officially launched, and by whom?\"\n",
-    "\n",
-    "get_knowledge = semantic_search_with_mongodb(query_text, collection)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "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>title</th>\n",
-       "      <th>text</th>\n",
-       "      <th>url</th>\n",
-       "      <th>score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>YouTube</td>\n",
-       "      <td>YouTube announced the project in September 201...</td>\n",
-       "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
-       "      <td>0.951712</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>YouTube</td>\n",
-       "      <td>The mobile version of the site was relaunched ...</td>\n",
-       "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
-       "      <td>0.948441</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>YouTube</td>\n",
-       "      <td>In January 2009, YouTube launched \"YouTube for...</td>\n",
-       "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
-       "      <td>0.948370</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>YouTube</td>\n",
-       "      <td>Later the same year, \"YouTube Feather\" was int...</td>\n",
-       "      <td>https://en.wikipedia.org/wiki?curid=3524766</td>\n",
-       "      <td>0.947532</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Twitch (service)</td>\n",
-       "      <td>On May 18, 2014, \"Variety\" first reported that...</td>\n",
-       "      <td>https://en.wikipedia.org/wiki?curid=33548254</td>\n",
-       "      <td>0.946378</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              title                                               text  \\\n",
-       "0           YouTube  YouTube announced the project in September 201...   \n",
-       "1           YouTube  The mobile version of the site was relaunched ...   \n",
-       "2           YouTube  In January 2009, YouTube launched \"YouTube for...   \n",
-       "3           YouTube  Later the same year, \"YouTube Feather\" was int...   \n",
-       "4  Twitch (service)  On May 18, 2014, \"Variety\" first reported that...   \n",
-       "\n",
-       "                                            url     score  \n",
-       "0   https://en.wikipedia.org/wiki?curid=3524766  0.951712  \n",
-       "1   https://en.wikipedia.org/wiki?curid=3524766  0.948441  \n",
-       "2   https://en.wikipedia.org/wiki?curid=3524766  0.948370  \n",
-       "3   https://en.wikipedia.org/wiki?curid=3524766  0.947532  \n",
-       "4  https://en.wikipedia.org/wiki?curid=33548254  0.946378  "
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(get_knowledge).head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Part 4: Vector Database Benchmarking"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Insertion Benchmark Process\n",
-    "\n",
-    "We are inserting data incrementally with doubling batch sizes and record performance metrics.\n",
-    "Notably, we will be measuring the time it takes to insert data incrementally and the number of rows inserted per second.\n",
-    "\n",
-    "We are using the `insert_data_incrementally` function to insert data incrementally.\n",
-    "\n",
-    "It starts with a batch size of 1 and doubles the batch size until it has inserted all the data, recording the time it takes to insert the data and the number of rows inserted per second.\n",
-    "\n",
-    "The key component we are interested in is the time it takes to insert the data and the number of rows inserted per second. In AI Workloads, there are data ingestion processes that are performned in batches from various data sources. So in practice, we are interested in the time it takes to insert the data and the number of rows inserted per second."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "\n",
-    "\n",
-    "def insert_data_incrementally(dataframe, connection, database_type=\"PostgreSQL\"):\n",
-    "    \"\"\"\n",
-    "    Insert data incrementally with doubling batch sizes and record performance metrics.\n",
-    "    \"\"\"\n",
-    "    incremental_metrics = {}\n",
-    "    total_rows = len(dataframe)\n",
-    "    remaining_rows = total_rows\n",
-    "    start_idx = 0\n",
-    "\n",
-    "    # Define batch sizes (1, 10, then doubling)\n",
-    "    batch_sizes = [1, 10]\n",
-    "    current_size = 20\n",
-    "    while current_size < total_rows:\n",
-    "        batch_sizes.append(current_size)\n",
-    "        current_size *= 2\n",
-    "\n",
-    "    for batch_size in batch_sizes:\n",
-    "        # Skip if we've already inserted all data\n",
-    "        if remaining_rows <= 0:\n",
-    "            break\n",
-    "\n",
-    "        # Calculate actual batch size based on remaining rows\n",
-    "        actual_batch_size = min(batch_size, remaining_rows)\n",
-    "        end_idx = start_idx + actual_batch_size\n",
-    "\n",
-    "        # Get the batch of data\n",
-    "        batch_df = dataframe.iloc[start_idx:end_idx]\n",
-    "\n",
-    "        # Record start time\n",
-    "        start_time = time.time()\n",
-    "\n",
-    "        try:\n",
-    "            # Insert data using existing function\n",
-    "            if database_type == \"PostgreSQL\":\n",
-    "                insert_data_to_postgres(batch_df, connection, database_type)\n",
-    "            else:  # MongoDB\n",
-    "                insert_data_to_mongodb(batch_df, connection, database_type)\n",
-    "\n",
-    "            # Record end time and calculate metrics\n",
-    "            end_time = time.time()\n",
-    "            total_time = end_time - start_time\n",
-    "            rows_per_second = actual_batch_size / total_time\n",
-    "\n",
-    "            # Store metrics\n",
-    "            incremental_metrics[actual_batch_size] = {\n",
-    "                \"total_time\": total_time,\n",
-    "                \"rows_per_second\": rows_per_second,\n",
-    "                \"batch_size\": actual_batch_size,\n",
-    "            }\n",
-    "\n",
-    "            # print(f\"\\nBatch Size {batch_size} Statistics:\")\n",
-    "            # print(f\"Total time: {total_time:.2f} seconds\")\n",
-    "            # print(f\"Average insertion rate: {rows_per_second:.2f} rows/second\")\n",
-    "            # print(f\"Actual rows inserted: {actual_batch_size}\")\n",
-    "\n",
-    "        except Exception as e:\n",
-    "            print(f\"Error during batch insertion (size {batch_size}): {e}\")\n",
-    "            raise e\n",
-    "\n",
-    "        # Update counters\n",
-    "        start_idx = end_idx\n",
-    "        remaining_rows -= actual_batch_size\n",
-    "\n",
-    "    # Store results in benchmark dictionary\n",
-    "    if database_type not in benchmark_results:\n",
-    "        benchmark_results[database_type] = {}\n",
-    "\n",
-    "    benchmark_results[database_type][\"incremental_insert\"] = incremental_metrics\n",
-    "\n",
-    "    return incremental_metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1.1 PostgreSQL Insertion Benchmark"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Table and index created successfully\n",
-      "\n",
-      "Connection closed\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psycopg\n",
-    "from pgvector.psycopg import register_vector\n",
-    "\n",
-    "try:\n",
-    "    conn = psycopg.connect(\n",
-    "        \"dbname=vector_db user=postgres password=test host=127.0.0.1\"\n",
-    "    )\n",
-    "    register_vector(conn)\n",
-    "\n",
-    "    # Create fresh table\n",
-    "    create_table(conn)\n",
-    "\n",
-    "    postgres_metrics = insert_data_incrementally(dataset_df, conn, \"PostgreSQL\")\n",
-    "\n",
-    "except Exception as e:\n",
-    "    print(\"Failed to execute:\", e)\n",
-    "finally:\n",
-    "    conn.close()\n",
-    "    print(\"\\nConnection closed\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1.2 MongoDB Insertion Benchmark"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connection to MongoDB successful\n",
-      "\n",
-      "MongoDB connection closed\n"
-     ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    mongo_client = get_mongo_client(MONGO_URI)\n",
-    "    db = mongo_client[DB_NAME]\n",
-    "    collection = db[COLLECTION_NAME]\n",
-    "\n",
-    "    # Clear collection\n",
-    "    collection.delete_many({})\n",
-    "\n",
-    "    mongo_metrics = insert_data_incrementally(dataset_df, collection, \"MongoDB\")\n",
-    "\n",
-    "except Exception as e:\n",
-    "    print(\"MongoDB operation failed:\", e)\n",
-    "finally:\n",
-    "    mongo_client.close()\n",
-    "    print(\"\\nMongoDB connection closed\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1.3 Visualize Insertion Benchmark\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "def plot_combined_insertion_metrics(postgres_metrics, mongo_metrics):\n",
-    "    \"\"\"\n",
-    "    Creates a combined line plot comparing PostgreSQL and MongoDB insertion metrics.\n",
-    "    \"\"\"\n",
-    "    # Create figure\n",
-    "    plt.figure(figsize=(12, 6))\n",
-    "\n",
-    "    # Extract data for both databases\n",
-    "    batch_sizes = [\n",
-    "        1,\n",
-    "        10,\n",
-    "        20,\n",
-    "        40,\n",
-    "        80,\n",
-    "        160,\n",
-    "        320,\n",
-    "        640,\n",
-    "        1280,\n",
-    "        2560,\n",
-    "        5120,\n",
-    "        10240,\n",
-    "        20480,\n",
-    "        40960,\n",
-    "    ]\n",
-    "    postgres_times = [\n",
-    "        postgres_metrics[size][\"total_time\"]\n",
-    "        for size in batch_sizes\n",
-    "        if size in postgres_metrics\n",
-    "    ]\n",
-    "    mongo_times = [\n",
-    "        mongo_metrics[size][\"total_time\"]\n",
-    "        for size in batch_sizes\n",
-    "        if size in mongo_metrics\n",
-    "    ]\n",
-    "\n",
-    "    # Create the line plots\n",
-    "    plt.plot(\n",
-    "        batch_sizes[: len(postgres_times)],\n",
-    "        postgres_times,\n",
-    "        marker=\"o\",\n",
-    "        label=\"PostgreSQL\",\n",
-    "        color=\"blue\",\n",
-    "        linewidth=2,\n",
-    "    )\n",
-    "    plt.plot(\n",
-    "        batch_sizes[: len(mongo_times)],\n",
-    "        mongo_times,\n",
-    "        marker=\"s\",\n",
-    "        label=\"MongoDB\",\n",
-    "        color=\"green\",\n",
-    "        linewidth=2,\n",
-    "    )\n",
-    "\n",
-    "    # Customize the plot\n",
-    "    plt.title(\"Database Insertion Time Comparison\", fontsize=14)\n",
-    "    plt.xlabel(\"Batch Size\", fontsize=12)\n",
-    "    plt.ylabel(\"Time (seconds)\", fontsize=12)\n",
-    "    plt.grid(True, linestyle=\"--\", alpha=0.7)\n",
-    "    plt.legend(fontsize=10)\n",
-    "\n",
-    "    # Use log scale for x-axis\n",
-    "    plt.xscale(\"log\", base=2)\n",
-    "\n",
-    "    # Define custom tick positions\n",
-    "    custom_ticks = batch_sizes\n",
-    "    plt.xticks(custom_ticks, custom_ticks, rotation=45, ha=\"right\")\n",
-    "\n",
-    "    # Add value annotations\n",
-    "    for i, (size, time) in enumerate(\n",
-    "        zip(batch_sizes[: len(postgres_times)], postgres_times)\n",
-    "    ):\n",
-    "        plt.annotate(\n",
-    "            f\"{time:.1f}s\",\n",
-    "            (size, time),\n",
-    "            textcoords=\"offset points\",\n",
-    "            xytext=(0, 10),\n",
-    "            ha=\"center\",\n",
-    "            fontsize=8,\n",
-    "        )\n",
-    "\n",
-    "    for i, (size, time) in enumerate(zip(batch_sizes[: len(mongo_times)], mongo_times)):\n",
-    "        plt.annotate(\n",
-    "            f\"{time:.1f}s\",\n",
-    "            (size, time),\n",
-    "            textcoords=\"offset points\",\n",
-    "            xytext=(0, -15),\n",
-    "            ha=\"center\",\n",
-    "            fontsize=8,\n",
-    "        )\n",
-    "\n",
-    "    # Add throughput information in a text box\n",
-    "    postgres_throughput = [\n",
-    "        metrics[\"rows_per_second\"] for metrics in postgres_metrics.values()\n",
-    "    ]\n",
-    "    mongo_throughput = [\n",
-    "        metrics[\"rows_per_second\"] for metrics in mongo_metrics.values()\n",
-    "    ]\n",
-    "\n",
-    "    text_info = (\n",
-    "        f\"Max Throughput:\\n\"\n",
-    "        f\"PostgreSQL: {max(postgres_throughput):.0f} rows/s\\n\"\n",
-    "        f\"MongoDB: {max(mongo_throughput):.0f} rows/s\"\n",
-    "    )\n",
-    "\n",
-    "    plt.text(\n",
-    "        0.02,\n",
-    "        0.98,\n",
-    "        text_info,\n",
-    "        transform=plt.gca().transAxes,\n",
-    "        bbox=dict(facecolor=\"white\", alpha=0.8),\n",
-    "        verticalalignment=\"top\",\n",
-    "        fontsize=10,\n",
-    "    )\n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_combined_insertion_metrics(postgres_metrics, mongo_metrics)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Benchmarking Semantic Search with PostgreSQL and PgVector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.1 PostgreSQL Semantic Search Benchmark"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import concurrent.futures\n",
-    "import random\n",
-    "from concurrent.futures import ThreadPoolExecutor\n",
-    "from statistics import mean, stdev\n",
-    "\n",
-    "import psycopg\n",
-    "from pgvector.psycopg import register_vector\n",
-    "\n",
-    "\n",
-    "def benchmark_search_postgres(\n",
-    "    connection,\n",
-    "    database_type=\"PostgreSQL\",\n",
-    "    num_queries=100,\n",
-    "    concurrent_queries=[1, 10, 50, 100],\n",
-    "):\n",
-    "    \"\"\"\n",
-    "    Benchmark the vector database performance with true concurrent queries.\n",
-    "\n",
-    "    Args:\n",
-    "        connection: Database connection\n",
-    "        database_type: Type of database (e.g., 'PostgreSQL', 'MongoDB')\n",
-    "        num_queries: Number of benchmark iterations for statistical significance\n",
-    "        concurrent_queries: Different concurrency levels to test\n",
-    "    \"\"\"\n",
-    "    query_templates = [\n",
-    "        \"When was YouTube officially launched, and by whom?\",\n",
-    "        \"What is YouTube's slogan introduced after Google's acquisition?\",\n",
-    "        \"How many hours of videos are collectively watched on YouTube daily?\",\n",
-    "        \"Which was the first video uploaded to YouTube, and when was it uploaded?\",\n",
-    "        \"What was the acquisition cost of YouTube by Google, and when was the deal finalized?\",\n",
-    "        \"What was the first YouTube video to reach one million views, and when did it happen?\",\n",
-    "        \"What are the three separate branches of the United States government?\",\n",
-    "        \"Which country has the highest documented incarceration rate and prison population?\",\n",
-    "        \"How many executions have occurred in the United States since 1977, and which countries have more?\",\n",
-    "        \"What percentage of the global military spending did the United States account for in 2019?\",\n",
-    "        \"How is the U.S. president elected?\",\n",
-    "        \"What cooling system innovation was included in the proposed venues for the World Cup in Qatar?\",\n",
-    "        \"What lawsuit was filed against Google in June 2020, and what was it about?\",\n",
-    "        \"How much was Google fined by CNIL in January 2022, and for what reason?\",\n",
-    "        \"When did YouTube join the NSA's PRISM program, according to reports?\",\n",
-    "    ]\n",
-    "\n",
-    "    if database_type not in benchmark_results:\n",
-    "        benchmark_results[database_type] = {}\n",
-    "\n",
-    "    benchmark_results[database_type][\"specific\"] = {}\n",
-    "\n",
-    "    def execute_single_query():\n",
-    "        \"\"\"Execute a single query and measure its latency\"\"\"\n",
-    "        query = random.choice(query_templates)\n",
-    "        start_time = time.time()\n",
-    "        result = semantic_search_with_postgres(query, connection, top_n=5)\n",
-    "        end_time = time.time()\n",
-    "        return end_time - start_time\n",
-    "\n",
-    "    for number_of_queries in concurrent_queries:\n",
-    "        latencies = []\n",
-    "\n",
-    "        for _ in range(num_queries):\n",
-    "            with ThreadPoolExecutor(max_workers=number_of_queries) as executor:\n",
-    "                # Submit queries and get individual latencies\n",
-    "                futures = [\n",
-    "                    executor.submit(execute_single_query)\n",
-    "                    for _ in range(number_of_queries)\n",
-    "                ]\n",
-    "                # Collect individual query latencies as they complete\n",
-    "                batch_latencies = [\n",
-    "                    future.result()\n",
-    "                    for future in concurrent.futures.as_completed(futures)\n",
-    "                ]\n",
-    "                latencies.extend(batch_latencies)\n",
-    "\n",
-    "        # Calculate metrics using individual query latencies\n",
-    "        avg_latency = mean(latencies)\n",
-    "        throughput = 1 / avg_latency  # Base queries per second per query\n",
-    "        p95_latency = sorted(latencies)[int(len(latencies) * 0.95)]\n",
-    "        std_dev_latency = stdev(latencies)\n",
-    "\n",
-    "        benchmark_results[database_type][\"specific\"][number_of_queries] = {\n",
-    "            \"avg_latency\": avg_latency,\n",
-    "            \"throughput\": throughput * number_of_queries,  # Scale by concurrent queries\n",
-    "            \"p95_latency\": p95_latency,\n",
-    "            \"std_dev\": std_dev_latency,\n",
-    "        }\n",
-    "\n",
-    "    return benchmark_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running benchmark...\n",
-      "\n",
-      "Connection closed\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run the benchmark\n",
-    "try:\n",
-    "    conn = psycopg.connect(\n",
-    "        \"dbname=vector_db user=postgres password=test host=127.0.0.1\"\n",
-    "    )\n",
-    "    register_vector(conn)\n",
-    "\n",
-    "    print(\"Running benchmark...\")\n",
-    "    results = benchmark_search_postgres(\n",
-    "        conn,\n",
-    "        database_type=\"PostgreSQL\",\n",
-    "        num_queries=TOTAL_QUERIES,\n",
-    "        concurrent_queries=CONCURRENT_QUERIES,\n",
-    "    )\n",
-    "\n",
-    "except Exception as e:\n",
-    "    print(\"Benchmark failed:\", e)\n",
-    "finally:\n",
-    "    conn.close()\n",
-    "    print(\"\\nConnection closed\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pprint\n",
-    "\n",
-    "pprint.pprint(benchmark_results[\"PostgreSQL\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.2 MongoDB Semantic Search Benchmark"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def benchmark_search_mongo(\n",
-    "    collection,\n",
-    "    database_type=\"MongoDB\",\n",
-    "    num_queries=100,\n",
-    "    concurrent_queries=[1, 10, 50, 100],\n",
-    "):\n",
-    "    \"\"\"\n",
-    "    Benchmark MongoDB vector search with true concurrency.\n",
-    "\n",
-    "    Args:\n",
-    "        collection: MongoDB collection object\n",
-    "        database_type: Type of database (default: \"MongoDB\")\n",
-    "        num_queries: Number of benchmark iterations for statistical significance\n",
-    "        batch_sizes: Different concurrency levels to test\n",
-    "    \"\"\"\n",
-    "    query_templates = [\n",
-    "        \"When was YouTube officially launched, and by whom?\",\n",
-    "        \"What is YouTube's slogan introduced after Google's acquisition?\",\n",
-    "        \"How many hours of videos are collectively watched on YouTube daily?\",\n",
-    "        \"Which was the first video uploaded to YouTube, and when was it uploaded?\",\n",
-    "        \"What was the acquisition cost of YouTube by Google, and when was the deal finalized?\",\n",
-    "        \"What was the first YouTube video to reach one million views, and when did it happen?\",\n",
-    "        \"What are the three separate branches of the United States government?\",\n",
-    "        \"Which country has the highest documented incarceration rate and prison population?\",\n",
-    "        \"How many executions have occurred in the United States since 1977, and which countries have more?\",\n",
-    "        \"What percentage of the global military spending did the United States account for in 2019?\",\n",
-    "        \"How is the U.S. president elected?\",\n",
-    "        \"What cooling system innovation was included in the proposed venues for the World Cup in Qatar?\",\n",
-    "        \"What lawsuit was filed against Google in June 2020, and what was it about?\",\n",
-    "        \"How much was Google fined by CNIL in January 2022, and for what reason?\",\n",
-    "        \"When did YouTube join the NSA's PRISM program, according to reports?\",\n",
-    "    ]\n",
-    "\n",
-    "    if database_type not in benchmark_results:\n",
-    "        benchmark_results[database_type] = {}\n",
-    "\n",
-    "    benchmark_results[database_type][\"specific\"] = {}\n",
-    "\n",
-    "    def execute_single_query():\n",
-    "        \"\"\"Execute a single query with MongoDB connection\"\"\"\n",
-    "        query = random.choice(query_templates)\n",
-    "        start_time = time.time()\n",
-    "        result = semantic_search_with_mongodb(query, collection, top_n=5)\n",
-    "        end_time = time.time()\n",
-    "        return end_time - start_time\n",
-    "\n",
-    "    for number_of_queries in concurrent_queries:\n",
-    "        latencies = []\n",
-    "\n",
-    "        for _ in range(num_queries):\n",
-    "            with ThreadPoolExecutor(max_workers=number_of_queries) as executor:\n",
-    "                # Submit queries and get individual latencies\n",
-    "                futures = [\n",
-    "                    executor.submit(execute_single_query)\n",
-    "                    for _ in range(number_of_queries)\n",
-    "                ]\n",
-    "                # Collect individual query latencies\n",
-    "                batch_latencies = [\n",
-    "                    future.result()\n",
-    "                    for future in concurrent.futures.as_completed(futures)\n",
-    "                ]\n",
-    "                latencies.extend(batch_latencies)\n",
-    "\n",
-    "        # Calculate metrics using individual query latencies\n",
-    "        avg_latency = mean(latencies)\n",
-    "        throughput = 1 / avg_latency  # Queries per second per query\n",
-    "        p95_latency = sorted(latencies)[int(len(latencies) * 0.95)]\n",
-    "        std_dev_latency = stdev(latencies)\n",
-    "\n",
-    "        # Store results\n",
-    "        benchmark_results[database_type][\"specific\"][number_of_queries] = {\n",
-    "            \"avg_latency\": avg_latency,\n",
-    "            \"throughput\": throughput * number_of_queries,  # Scale by concurrent queries\n",
-    "            \"p95_latency\": p95_latency,\n",
-    "            \"std_dev\": std_dev_latency,\n",
-    "        }\n",
-    "\n",
-    "    return benchmark_results[database_type]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connection to MongoDB successful\n",
-      "Running MongoDB benchmark...\n",
-      "\n",
-      "MongoDB connection closed\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run the benchmark for MongoDB\n",
-    "try:\n",
-    "    mongo_client = get_mongo_client(MONGO_URI)\n",
-    "    db = mongo_client[DB_NAME]\n",
-    "    collection = db[COLLECTION_NAME]\n",
-    "\n",
-    "    print(\"Running MongoDB benchmark...\")\n",
-    "    results = benchmark_search_mongo(\n",
-    "        collection, num_queries=TOTAL_QUERIES, concurrent_queries=CONCURRENT_QUERIES\n",
-    "    )\n",
-    "\n",
-    "except Exception as e:\n",
-    "    print(\"MongoDB benchmark failed:\", e)\n",
-    "finally:\n",
-    "    mongo_client.close()\n",
-    "    print(\"\\nMongoDB connection closed\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(benchmark_results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.3 Visualize Semantic Search Benchmark"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bar_chart_benchmark_comparison(\n",
-    "    benchmark_results, metric=\"avg_latency\", metric_label=\"Average Latency (ms)\"\n",
-    "):\n",
-    "    \"\"\"\n",
-    "    Generates bar charts to compare benchmark results for each metric across databases.\n",
-    "\n",
-    "    Args:\n",
-    "        benchmark_results (dict): Benchmark results structured as a nested dictionary.\n",
-    "        metric (str): The performance metric to visualize (e.g., \"avg_latency\", \"throughput\").\n",
-    "        metric_label (str): The label to display for the metric.\n",
-    "    \"\"\"\n",
-    "    # Extract data for the bar chart\n",
-    "    query_types = [\"specific\"]\n",
-    "    batch_sizes = CONCURRENT_QUERIES\n",
-    "    data = []\n",
-    "\n",
-    "    for query_type in query_types:\n",
-    "        for batch_size in batch_sizes:\n",
-    "            row = {\"Batch Size\": batch_size, \"Query Type\": query_type}\n",
-    "            for db_type in benchmark_results:\n",
-    "                # Safely extract metric values or default to None\n",
-    "                value = (\n",
-    "                    benchmark_results[db_type]\n",
-    "                    .get(query_type, {})\n",
-    "                    .get(batch_size, {})\n",
-    "                    .get(metric, None)\n",
-    "                )\n",
-    "                # Scale values if necessary\n",
-    "                if value is not None and metric in [\"avg_latency\", \"p95_latency\"]:\n",
-    "                    value *= 1000  # Convert to ms\n",
-    "                row[db_type] = value\n",
-    "            data.append(row)\n",
-    "\n",
-    "    # Convert the data into a plot-friendly structure\n",
-    "    for query_type in query_types:\n",
-    "        labels = [str(batch_size) for batch_size in batch_sizes]\n",
-    "        mongodb_values = [\n",
-    "            row[\"MongoDB\"] for row in data if row[\"Query Type\"] == query_type\n",
-    "        ]\n",
-    "        postgres_values = [\n",
-    "            row[\"PostgreSQL\"] for row in data if row[\"Query Type\"] == query_type\n",
-    "        ]\n",
-    "\n",
-    "        # Create the bar chart\n",
-    "        fig, ax = plt.subplots(figsize=(15, 6))\n",
-    "\n",
-    "        # Set the width of each bar and positions of the bars\n",
-    "        width = 0.35\n",
-    "        x = np.arange(len(batch_sizes))\n",
-    "\n",
-    "        # Create bars\n",
-    "        postgres_bars = ax.bar(\n",
-    "            x - width / 2,\n",
-    "            postgres_values,\n",
-    "            width,\n",
-    "            label=\"PostgreSQL\",\n",
-    "            color=\"lightblue\",\n",
-    "            edgecolor=\"blue\",\n",
-    "        )\n",
-    "        mongodb_bars = ax.bar(\n",
-    "            x + width / 2,\n",
-    "            mongodb_values,\n",
-    "            width,\n",
-    "            label=\"MongoDB\",\n",
-    "            color=\"lightgreen\",\n",
-    "            edgecolor=\"green\",\n",
-    "        )\n",
-    "\n",
-    "        # Add grid\n",
-    "        ax.grid(True, linestyle=\"--\", alpha=0.7, axis=\"y\")\n",
-    "\n",
-    "        # Add titles and labels\n",
-    "        ax.set_title(\n",
-    "            f\"{metric_label} Comparison for {query_type.capitalize()} Queries\", pad=20\n",
-    "        )\n",
-    "        ax.set_xlabel(\"Concurrent Queries\", labelpad=10)\n",
-    "        ax.set_ylabel(metric_label, labelpad=10)\n",
-    "\n",
-    "        # Set x-axis ticks\n",
-    "        ax.set_xticks(x)\n",
-    "        ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n",
-    "\n",
-    "        # Add legend\n",
-    "        ax.legend(fontsize=10)\n",
-    "\n",
-    "        # Add value labels on top of bars\n",
-    "        def autolabel(rects):\n",
-    "            for rect in rects:\n",
-    "                height = rect.get_height()\n",
-    "                ax.annotate(\n",
-    "                    f\"{height:.2f}\",\n",
-    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
-    "                    xytext=(0, 3),  # 3 points vertical offset\n",
-    "                    textcoords=\"offset points\",\n",
-    "                    ha=\"center\",\n",
-    "                    va=\"bottom\",\n",
-    "                    rotation=90,\n",
-    "                    fontsize=8,\n",
-    "                )\n",
-    "\n",
-    "        autolabel(postgres_bars)\n",
-    "        autolabel(mongodb_bars)\n",
-    "\n",
-    "        plt.tight_layout()\n",
-    "        plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Generate bar charts for each metric\n",
-    "bar_chart_benchmark_comparison(\n",
-    "    benchmark_results, metric=\"avg_latency\", metric_label=\"Average Latency (ms)\"\n",
-    ")\n",
-    "bar_chart_benchmark_comparison(\n",
-    "    benchmark_results, metric=\"throughput\", metric_label=\"Throughput (queries/sec)\"\n",
-    ")\n",
-    "bar_chart_benchmark_comparison(\n",
-    "    benchmark_results, metric=\"p95_latency\", metric_label=\"P95 Latency (ms)\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Part 5: Extra Notes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "### 5.1 PostgreSQL JSONB vs MongoDB BSON\n",
-    "\n",
-    "| Feature                    | **PostgreSQL JSONB**                              | **MongoDB BSON**                              |\n",
-    "|----------------------------|--------------------------------------------------|----------------------------------------------|\n",
-    "| **Integration**            | An extension to a relational database system.    | Native to MongoDB, a document database.      |\n",
-    "| **Query Language**         | Uses SQL with JSONB-specific operators/functions. | Uses MongoDB Query Language (MQL), a JSON-like query syntax. |\n",
-    "| **Storage Optimization**   | Optimized for relational data alongside JSONB.   | Fully optimized for JSON-like document storage. |\n",
-    "| **Data Type Support**      | Stores standard JSON data types (e.g., strings, numbers). | Includes additional types not in standard JSON (e.g., `Date`, `ObjectId`, `Binary`). |\n",
-    "| **Use Case**               | Best for hybrid relational/JSON use cases.       | Designed for flexible schemas, document-based databases. |\n",
-    "| **Updates**                | JSONB supports in-place updates for specific keys or paths. | BSON supports in-place updates with more native support for field-level atomic operations. |\n",
-    "| **Size Overhead**          | Slightly more compact than BSON in some cases.   | Includes metadata like type information, leading to slightly larger size. |\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
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