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     "name": "stderr",
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     "text": [
      "req         WARNING \tDEFAULT CACHE ENABLED! (947.08 MB) /Users/diegomaradona/Library/Caches/fastf1\n"
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     "name": "stdout",
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     "text": [
      "WARNING:fastf1.fastf1.req:DEFAULT CACHE ENABLED! (947.08 MB) /Users/diegomaradona/Library/Caches/fastf1\n",
      "DEFAULT CACHE ENABLED! (947.08 MB) /Users/diegomaradona/Library/Caches/fastf1\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "core           INFO \tLoading data for Spanish Grand Prix - Qualifying [v3.3.3]\n"
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     "name": "stdout",
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     "text": [
      "INFO:fastf1.fastf1.core:Loading data for Spanish Grand Prix - Qualifying [v3.3.3]\n",
      "Loading data for Spanish Grand Prix - Qualifying [v3.3.3]\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_info\n"
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      "INFO:fastf1.fastf1.req:Using cached data for driver_info\n",
      "Using cached data for driver_info\n"
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      "INFO:fastf1.fastf1.req:Using cached data for track_status_data\n",
      "Using cached data for track_status_data\n"
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     "text": [
      "req            INFO \tUsing cached data for _extended_timing_data\n"
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     "output_type": "stream",
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      "req            INFO \tUsing cached data for car_data\n"
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     "output_type": "stream",
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      "INFO:fastf1.fastf1.req:Using cached data for car_data\n",
      "Using cached data for car_data\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for position_data\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for weather_data\n"
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    {
     "name": "stdout",
     "output_type": "stream",
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      "INFO:fastf1.fastf1.req:Using cached data for weather_data\n",
      "Using cached data for weather_data\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for race_control_messages\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:fastf1.fastf1.req:Using cached data for race_control_messages\n",
      "Using cached data for race_control_messages\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "core           INFO \tFinished loading data for 20 drivers: ['44', '33', '77', '16', '31', '55', '3', '11', '4', '14', '18', '10', '5', '99', '63', '22', '7', '47', '6', '9']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:fastf1.fastf1.core:Finished loading data for 20 drivers: ['44', '33', '77', '16', '31', '55', '3', '11', '4', '14', '18', '10', '5', '99', '63', '22', '7', '47', '6', '9']\n",
      "Finished loading data for 20 drivers: ['44', '33', '77', '16', '31', '55', '3', '11', '4', '14', '18', '10', '5', '99', '63', '22', '7', '47', '6', '9']\n"
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import fastf1.plotting\n",
    "\n",
    "session = fastf1.get_session(2021, 'Spanish Grand Prix', 'Q')\n",
    "session.load()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos analisar a primeira curva no GP da Espanha e embbedar esses dados no LLM, para ver se o LLM consegue trazer informações sobre os dados"
   ]
  },
  {
   "cell_type": "code",
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             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#f2f5fa"
          },
          "geo": {
           "bgcolor": "rgb(17,17,17)",
           "lakecolor": "rgb(17,17,17)",
           "landcolor": "rgb(17,17,17)",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "#506784"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "dark"
          },
          "paper_bgcolor": "rgb(17,17,17)",
          "plot_bgcolor": "rgb(17,17,17)",
          "polar": {
           "angularaxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           },
           "bgcolor": "rgb(17,17,17)",
           "radialaxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "rgb(17,17,17)",
            "gridcolor": "#506784",
            "gridwidth": 2,
            "linecolor": "#506784",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#C8D4E3"
           },
           "yaxis": {
            "backgroundcolor": "rgb(17,17,17)",
            "gridcolor": "#506784",
            "gridwidth": 2,
            "linecolor": "#506784",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#C8D4E3"
           },
           "zaxis": {
            "backgroundcolor": "rgb(17,17,17)",
            "gridcolor": "#506784",
            "gridwidth": 2,
            "linecolor": "#506784",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#C8D4E3"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#f2f5fa"
           }
          },
          "sliderdefaults": {
           "bgcolor": "#C8D4E3",
           "bordercolor": "rgb(17,17,17)",
           "borderwidth": 1,
           "tickwidth": 0
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           },
           "bgcolor": "rgb(17,17,17)",
           "caxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "updatemenudefaults": {
           "bgcolor": "#506784",
           "borderwidth": 0
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "#283442",
           "linecolor": "#506784",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#283442",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "#283442",
           "linecolor": "#506784",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#283442",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Test"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Distance in meters"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Speed in km/h"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fastest_lap = session.laps.pick_fastest()\n",
    "car_data = fastest_lap.get_car_data().add_distance()\n",
    "\n",
    "circuit_info = session.get_circuit_info()\n",
    "\n",
    "\n",
    "fastest_lap = session.laps.pick_driver(\n",
    "    'VER').pick_fastest().get_car_data().add_distance()\n",
    "# print(fastest_lap.keys())\n",
    "fastest_lap = fastest_lap[['Distance', 'Speed']]\n",
    "fastest_lap['Driver'] = session.get_driver('VER')['BroadcastName']\n",
    "\n",
    "circuit_info = session.get_circuit_info()\n",
    "\n",
    "\n",
    "fig = px.line(fastest_lap, x=\"Distance\", y=\"Speed\", color=\"Driver\", title=\"Test\", template=\"plotly_dark\",\n",
    "              labels=dict(Speed='Speed in km/h', Distance='Distance in meters'))\n",
    "\n",
    "for _, corner in circuit_info.corners.iterrows():\n",
    "    txt = f\"{corner['Number']}{corner['Letter']}\"\n",
    "    fig.add_vline(x=corner['Distance'], line_dash=\"dash\", annotation=dict(\n",
    "        text=txt), fillcolor=\"gray\", opacity=0.2)\n",
    "fig.show()\n",
    "\n",
    "# print(fastest_lap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Distance</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Driver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>811.260000</td>\n",
       "      <td>196</td>\n",
       "      <td>M VERSTAPPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>823.541944</td>\n",
       "      <td>185</td>\n",
       "      <td>M VERSTAPPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>835.341944</td>\n",
       "      <td>177</td>\n",
       "      <td>M VERSTAPPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>846.808611</td>\n",
       "      <td>172</td>\n",
       "      <td>M VERSTAPPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>857.808611</td>\n",
       "      <td>165</td>\n",
       "      <td>M VERSTAPPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>868.875278</td>\n",
       "      <td>166</td>\n",
       "      <td>M VERSTAPPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>880.075278</td>\n",
       "      <td>168</td>\n",
       "      <td>M VERSTAPPEN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Distance  Speed        Driver\n",
       "40  811.260000    196  M VERSTAPPEN\n",
       "41  823.541944    185  M VERSTAPPEN\n",
       "42  835.341944    177  M VERSTAPPEN\n",
       "43  846.808611    172  M VERSTAPPEN\n",
       "44  857.808611    165  M VERSTAPPEN\n",
       "45  868.875278    166  M VERSTAPPEN\n",
       "46  880.075278    168  M VERSTAPPEN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastest_lap.loc[(fastest_lap['Distance'] > 800) &\n",
    "                (fastest_lap['Distance'] < 890)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "core        WARNING \tFailed to preserve data type for column 'X' while merging telemetry.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:fastf1.fastf1.core:Failed to preserve data type for column 'X' while merging telemetry.\n",
      "Failed to preserve data type for column 'X' while merging telemetry.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "core        WARNING \tFailed to preserve data type for column 'Y' while merging telemetry.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:fastf1.fastf1.core:Failed to preserve data type for column 'Y' while merging telemetry.\n",
      "Failed to preserve data type for column 'Y' while merging telemetry.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "core        WARNING \tFailed to preserve data type for column 'Z' while merging telemetry.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:fastf1.fastf1.core:Failed to preserve data type for column 'Z' while merging telemetry.\n",
      "Failed to preserve data type for column 'Z' while merging telemetry.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x675 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lap = session.laps.pick_driver(\"VER\").pick_fastest()\n",
    "telemetry = lap.telemetry\n",
    "telemetry.loc[(telemetry['Distance'] > 800) & (telemetry['Distance'] < 890)]\n",
    "# x = lap.telemetry['X']\n",
    "# y = lap.telemetry['Y']\n",
    "\n",
    "fig, ax = plt.subplots(sharex=True, sharey=True, figsize=(12, 6.75))\n",
    "\n",
    "# Adjust margins and turn of axis\n",
    "plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.12)\n",
    "ax.axis('off')\n",
    "\n",
    "\n",
    "# After this, we plot the data itself.\n",
    "# Create background track line\n",
    "ax.plot(telemetry['X'], telemetry['Y'],\n",
    "        color='black', linestyle='-', linewidth=16, zorder=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo = telemetry.loc[(telemetry['Distance'] > 800) &\n",
    "                    (telemetry['Distance'] < 890)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "VER",
         "type": "scatter",
         "x": [
          -3436,
          -3493.743485461971,
          -3521,
          -3587.6818046813873,
          -3608,
          -3683.698194465908,
          -3696,
          -3778.530471415178,
          -3783,
          -3877,
          -3881.0753786607042,
          -3981,
          -3995.0046825776035,
          -4088,
          -4112.065451169896,
          -4197
         ],
         "y": [
          -7083,
          -7135.176121264446,
          -7157,
          -7202.919570886845,
          -7215,
          -7252.8297721384115,
          -7258,
          -7284.186280849798,
          -7285,
          -7289,
          -7288.761155470017,
          -7275,
          -7271.8588176138,
          -7244,
          -7235.086561725293,
          -7200
         ]
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
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          "histogram": [
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          "mesh3d": [
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            "type": "mesh3d"
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            "type": "parcoords"
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          "pie": [
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            "automargin": true,
            "type": "pie"
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          "scatter": [
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            "fillpattern": {
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             "solidity": 0.2
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          "scatter3d": [
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            "type": "scattercarpet"
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          "scattergl": [
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            "type": "scattergl"
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          "scattermapbox": [
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            "type": "scattermapbox"
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          ],
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            "type": "scatterpolar"
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            "type": "scatterpolargl"
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          "scatterternary": [
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            "type": "scatterternary"
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          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
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             "line": {
              "color": "white"
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             "fill": {
              "color": "#C8D4E3"
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             "line": {
              "color": "white"
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            },
            "type": "table"
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          ]
         },
         "layout": {
          "annotationdefaults": {
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           "arrowhead": 0,
           "arrowwidth": 1
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          "autotypenumbers": "strict",
          "coloraxis": {
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          "colorscale": {
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          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
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          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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           "yaxis": {
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            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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           "zaxis": {
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            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "data = [\n",
    "    go.Scatter(x=foo[\"X\"], y=foo[\"Y\"],\n",
    "               name='VER'),\n",
    "\n",
    "]\n",
    "fig = go.Figure(data=data)\n",
    "# fig.show()\n",
    "\n",
    "# fig = px.line(foo, x=\"X\", y=\"Y\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "name": "VER",
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         "y": [
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          -7271.8588176138,
          -7244,
          -7235.086561725293,
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        },
        {
         "name": "HAM",
         "type": "scatter",
         "x": [
          -3433,
          -3482,
          -3522,
          -3565,
          -3614,
          -3649,
          -3706,
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         "y": [
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        }
       ],
       "layout": {
        "template": {
         "data": {
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           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
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             "line": {
              "color": "#E5ECF6",
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             },
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              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
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            },
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          ],
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             "minorgridcolor": "white",
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            "type": "carpet"
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          "xaxis": {
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   ],
   "source": [
    "ham_lap = session.laps.pick_driver(\"HAM\").pick_fastest()\n",
    "ham_telemetry = ham_lap.telemetry\n",
    "ham_telemetry_foo = ham_telemetry.loc[(ham_telemetry['Distance'] > 800) &\n",
    "                                      (ham_telemetry['Distance'] < 890)]\n",
    "\n",
    "data = [\n",
    "    go.Scatter(x=foo[\"X\"], y=foo[\"Y\"],\n",
    "               name='VER'),\n",
    "    go.Scatter(x=ham_telemetry_foo[\"X\"], y=ham_telemetry_foo[\"Y\"],\n",
    "               name='HAM'),\n",
    "\n",
    "]\n",
    "fig = go.Figure(data=data)\n",
    "# fig.show()\n",
    "\n",
    "# fig = px.line(foo, x=\"X\", y=\"Y\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "data = [\n",
    "    go.Scatter(x=foo[\"Distance\"], y=foo[\"Speed\"],\n",
    "               name='VER'),\n",
    "    go.Scatter(x=ham_telemetry_foo[\"Distance\"], y=ham_telemetry_foo[\"Speed\"],\n",
    "               name='HAM'),\n",
    "\n",
    "]\n",
    "fig = go.Figure(data=data)\n",
    "# fig.show()\n",
    "\n",
    "# fig = px.line(foo, x=\"X\", y=\"Y\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "              X            Y  Number Letter       Angle     Distance\n",
       "0  -3728.050293 -7258.672852       1         -73.417940   840.439503\n",
       "1  -4538.213867 -7090.668945       2          82.427713   925.219271\n",
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       "5  -3511.338623 -5588.651855       6         -48.179459  2364.579753\n",
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       "7  -3287.300781 -3536.369629       8        -147.354020  2637.109590\n",
       "8  -3938.748047  -911.778870       9         155.821148  2925.381432\n",
       "9    869.164917  1933.093018      10          13.736824  3499.034859\n",
       "10  -326.263885  2196.596436      11         -62.899364  3638.214026\n",
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       "12  -170.414062  3724.000000      13          89.888203  4025.864180\n",
       "13   883.026489  3076.241699      14         -89.013242  4156.471024\n",
       "14  1272.748169  3302.875244      15          87.948713  4203.509639\n",
       "15  2294.989746  2150.140137      16           1.037018  4371.906122"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit_info.corners"
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  },
  {
   "cell_type": "code",
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          "xaxis": {
           "automargin": true,
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           "linecolor": "white",
           "ticks": "",
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           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ver_sector1 = telemetry.loc[(telemetry['Distance'] < 1600)]\n",
    "ham_sector1 = ham_telemetry.loc[(ham_telemetry['Distance'] < 1600)]\n",
    "\n",
    "data = [\n",
    "    go.Scatter(x=ver_sector1[\"Distance\"], y=ver_sector1[\"Speed\"],\n",
    "               name='ver sector 1'),\n",
    "    go.Scatter(x=ham_sector1[\"Distance\"], y=ham_sector1[\"Speed\"],\n",
    "               name='ham sector 1'),\n",
    "]\n",
    "fig = go.Figure(data=data)\n",
    "# fig.show()\n",
    "\n",
    "# fig = px.line(foo, x=\"X\", y=\"Y\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ver_sector1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'SessionTime', 'DriverAhead', 'DistanceToDriverAhead', 'Time',\n",
       "       'RPM', 'Speed', 'nGear', 'Throttle', 'Brake', 'DRS', 'Source',\n",
       "       'Distance', 'RelativeDistance', 'Status', 'X', 'Y', 'Z'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ver_sector1.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hamilton Sector 1 Time: 21.798 seconds\n",
      "Verstappen Sector 1 Time: 21.759 seconds\n"
     ]
    }
   ],
   "source": [
    "print(f\"Hamilton Sector 1 Time: {\n",
    "      ham_lap['Sector1Time'].total_seconds():.3f} seconds\")\n",
    "print(f\"Verstappen Sector 1 Time: {\n",
    "      lap['Sector1Time'].total_seconds():.3f} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now using the LlamaIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25hInstalling collected packages: striprtf, dirtyjson, pypdf, nltk, networkx, greenlet, llamaindex-py-client, llama-index-legacy, llama-index-core, llama-parse, llama-index-readers-file, llama-index-llms-openai, llama-index-indices-managed-llama-cloud, llama-index-experimental, llama-index-embeddings-openai, llama-index-readers-llama-parse, llama-index-multi-modal-llms-openai, llama-index-cli, llama-index-agent-openai, llama-index-program-openai, llama-index-question-gen-openai, llama-index\n",
      "  Attempting uninstall: pypdf\n",
      "    Found existing installation: pypdf 3.17.4\n",
      "    Uninstalling pypdf-3.17.4:\n",
      "      Successfully uninstalled pypdf-3.17.4\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "embedchain 0.1.102 requires pypdf<4.0.0,>=3.11.0, but you have pypdf 4.2.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed dirtyjson-1.0.8 greenlet-3.0.3 llama-index-0.10.43 llama-index-agent-openai-0.2.7 llama-index-cli-0.1.12 llama-index-core-0.10.43 llama-index-embeddings-openai-0.1.10 llama-index-experimental-0.1.3 llama-index-indices-managed-llama-cloud-0.1.6 llama-index-legacy-0.9.48 llama-index-llms-openai-0.1.22 llama-index-multi-modal-llms-openai-0.1.6 llama-index-program-openai-0.1.6 llama-index-question-gen-openai-0.1.3 llama-index-readers-file-0.1.23 llama-index-readers-llama-parse-0.1.4 llama-parse-0.4.4 llamaindex-py-client-0.1.19 networkx-3.3 nltk-3.8.1 pypdf-4.2.0 striprtf-0.0.26\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install llama-index llama-index-experimental"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import sys\n",
    "from IPython.display import Markdown, display\n",
    "\n",
    "import pandas as pd\n",
    "from llama_index.experimental.query_engine import PandasQueryEngine\n",
    "\n",
    "\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'SessionTime', 'DriverAhead', 'DistanceToDriverAhead', 'Time',\n",
       "       'RPM', 'Speed', 'nGear', 'Throttle', 'Brake', 'DRS', 'Source',\n",
       "       'Distance', 'RelativeDistance', 'Status', 'X', 'Y', 'Z'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ver_sector1.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv()\n",
    "\n",
    "ver_query_engine = PandasQueryEngine(df=ver_sector1[[\n",
    "    'Distance', 'Speed', 'RPM', 'nGear', 'Throttle', 'Brake']], verbose=True, synthesize_response=True, use_async=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "> Pandas Instructions:\n",
      "```\n",
      "df['Speed'].mean()\n",
      "```\n",
      "> Pandas Output: 268.6223404255319\n",
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
     ]
    }
   ],
   "source": [
    "response = ver_query_engine.query(\n",
    "    \"What is the mean speed of Verstappen Sector 1?\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "268.6223404255319"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(f\"{response}\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Requirement already satisfied: annotated-types>=0.4.0 in /Users/diegomaradona/miniforge3/envs/formula-1/lib/python3.11/site-packages (from pydantic>=1.10->llamaindex-py-client<0.2.0,>=0.1.18->llama-index-core==0.10.43->llama-index) (0.7.0)\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install llama-index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting llama-index-question-gen-guidance\n",
      "  Downloading llama_index_question_gen_guidance-0.1.2-py3-none-any.whl.metadata (737 bytes)\n",
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      "Collecting llama-index-program-guidance<0.2.0,>=0.1.1 (from llama-index-question-gen-guidance)\n",
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      "Collecting guidance<0.2.0,>=0.1.10 (from llama-index-program-guidance<0.2.0,>=0.1.1->llama-index-question-gen-guidance)\n",
      "  Downloading guidance-0.1.15-cp311-cp311-macosx_11_0_arm64.whl.metadata (2.2 kB)\n",
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      "Collecting diskcache (from guidance<0.2.0,>=0.1.10->llama-index-program-guidance<0.2.0,>=0.1.1->llama-index-question-gen-guidance)\n",
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      "Collecting pydot (from pyformlang->guidance<0.2.0,>=0.1.10->llama-index-program-guidance<0.2.0,>=0.1.1->llama-index-question-gen-guidance)\n",
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      "\u001b[?25hDownloading ordered_set-4.1.0-py3-none-any.whl (7.6 kB)\n",
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      "\u001b[?25hDownloading pydot-2.0.0-py3-none-any.whl (22 kB)\n",
      "Installing collected packages: pydot, ordered-set, diskcache, pyformlang, guidance, llama-index-program-guidance, llama-index-question-gen-guidance\n",
      "Successfully installed diskcache-5.6.3 guidance-0.1.15 llama-index-program-guidance-0.1.2 llama-index-question-gen-guidance-0.1.2 ordered-set-4.1.0 pydot-2.0.0 pyformlang-1.0.10\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install llama-index-question-gen-guidance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'OpenAIChat' from 'guidance.models' (/Users/diegomaradona/miniforge3/envs/formula-1/lib/python3.11/site-packages/guidance/models/__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[28], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mquestion_gen\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mguidance\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GuidanceQuestionGenerator\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mguidance\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mllms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OpenAI \u001b[38;5;28;01mas\u001b[39;00m GuidanceOpenAI\n\u001b[1;32m      4\u001b[0m question_gen \u001b[38;5;241m=\u001b[39m GuidanceQuestionGenerator\u001b[38;5;241m.\u001b[39mfrom_defaults(\n\u001b[1;32m      5\u001b[0m     guidance_llm\u001b[38;5;241m=\u001b[39mGuidanceOpenAI(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext-davinci-003\u001b[39m\u001b[38;5;124m\"\u001b[39m), verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m      6\u001b[0m )\n",
      "File \u001b[0;32m~/miniforge3/envs/formula-1/lib/python3.11/site-packages/llama_index/question_gen/guidance/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mquestion_gen\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mguidance\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GuidanceQuestionGenerator\n\u001b[1;32m      3\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGuidanceQuestionGenerator\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[0;32m~/miniforge3/envs/formula-1/lib/python3.11/site-packages/llama_index/question_gen/guidance/base.py:16\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mschema\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m QueryBundle\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtools\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ToolMetadata\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprogram\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mguidance\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GuidancePydanticProgram\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m TYPE_CHECKING:\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mguidance\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Model \u001b[38;5;28;01mas\u001b[39;00m GuidanceLLM\n",
      "File \u001b[0;32m~/miniforge3/envs/formula-1/lib/python3.11/site-packages/llama_index/program/guidance/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprogram\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mguidance\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GuidancePydanticProgram\n\u001b[1;32m      3\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGuidancePydanticProgram\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[0;32m~/miniforge3/envs/formula-1/lib/python3.11/site-packages/llama_index/program/guidance/base.py:13\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mguidance\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m assistant, gen, user\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mguidance\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Model \u001b[38;5;28;01mas\u001b[39;00m GuidanceLLM\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mguidance\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OpenAIChat\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mGuidancePydanticProgram\u001b[39;00m(BaseLLMFunctionProgram[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGuidanceLLM\u001b[39m\u001b[38;5;124m\"\u001b[39m]):\n\u001b[1;32m     17\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;124;03m    A guidance-based function that returns a pydantic model.\u001b[39;00m\n\u001b[1;32m     19\u001b[0m \n\u001b[1;32m     20\u001b[0m \u001b[38;5;124;03m    Note: this interface is not yet stable.\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'OpenAIChat' from 'guidance.models' (/Users/diegomaradona/miniforge3/envs/formula-1/lib/python3.11/site-packages/guidance/models/__init__.py)"
     ]
    }
   ],
   "source": [
    "# from llama_index.question_gen.guidance import GuidanceQuestionGenerator\n",
    "# from guidance.llms import OpenAI as GuidanceOpenAI\n",
    "\n",
    "# question_gen = GuidanceQuestionGenerator.from_defaults(\n",
    "#     guidance_llm=GuidanceOpenAI(\"text-davinci-003\"), verbose=False\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.tools import QueryEngineTool, ToolMetadata\n",
    "from llama_index.core.query_engine import SubQuestionQueryEngine\n",
    "\n",
    "import nest_asyncio\n",
    "nest_asyncio.apply()\n",
    "\n",
    "ham_query_engine = PandasQueryEngine(df=ham_sector1[[\n",
    "    'Distance', 'Speed', 'RPM', 'nGear', 'Throttle', 'Brake']], verbose=True, synthesize_response=True, use_async=True)\n",
    "\n",
    "query_engine_tools = [\n",
    "    QueryEngineTool(\n",
    "        query_engine=ver_query_engine,\n",
    "        metadata=ToolMetadata(\n",
    "            name=\"ver_sector1_telemetry\",\n",
    "            description=(\n",
    "                \"Provides telemetry data about Verstappen fastest lap on Sector 1 of Spanish GP\"\n",
    "            ),\n",
    "        ),\n",
    "    ),\n",
    "    QueryEngineTool(\n",
    "        query_engine=ham_query_engine,\n",
    "        metadata=ToolMetadata(\n",
    "            name=\"ham_sector1_telemetry\",\n",
    "            description=(\n",
    "                \"Provides telemetry data about Hamilton fastest lap on Sector 1 of Spanish GP\"\n",
    "            ),\n",
    "        ),\n",
    "    ),\n",
    "]\n",
    "\n",
    "s_engine = SubQuestionQueryEngine.from_defaults(\n",
    "    # question_gen=question_gen,  # use guidance based question_gen defined above\n",
    "    query_engine_tools=query_engine_tools,\n",
    "    use_async=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "Generated 2 sub questions.\n",
      "\u001b[1;3;38;2;237;90;200m[ver_sector1_telemetry] Q: What was Verstappen's speed on Sector 1 of the Spanish GP?\n",
      "\u001b[0mINFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "> Pandas Instructions:\n",
      "```\n",
      "df.loc[2, 'Speed']\n",
      "```\n",
      "> Pandas Output: 289\n",
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "\u001b[1;3;38;2;237;90;200m[ver_sector1_telemetry] A: Verstappen's speed on Sector 1 of the Spanish GP was 289 km/h.\n",
      "\u001b[0m\u001b[1;3;38;2;90;149;237m[ham_sector1_telemetry] Q: What was Hamilton's speed on Sector 1 of the Spanish GP?\n",
      "\u001b[0mINFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "> Pandas Instructions:\n",
      "```\n",
      "df.loc[2, 'Speed']\n",
      "```\n",
      "> Pandas Output: 289\n",
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "\u001b[1;3;38;2;90;149;237m[ham_sector1_telemetry] A: Hamilton's speed on Sector 1 of the Spanish GP was 289 km/h.\n",
      "\u001b[0mINFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
     ]
    }
   ],
   "source": [
    "response = s_engine.query(\n",
    "    \"Compare and contrast the race on Sector 1 from Verstappen and Hamilton knowing that Verstappen was faster than Hamilton\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Verstappen had a faster speed than Hamilton on Sector 1 of the Spanish GP."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(f\"{response}\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "Generated 2 sub questions.\n",
      "\u001b[1;3;38;2;237;90;200m[ver_sector1_telemetry] Q: What was Verstappen's speed in Sector 1 of the Spanish GP?\n",
      "\u001b[0mINFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "> Pandas Instructions:\n",
      "```\n",
      "df.loc[2, 'Speed']\n",
      "```\n",
      "> Pandas Output: 289\n",
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "\u001b[1;3;38;2;237;90;200m[ver_sector1_telemetry] A: Verstappen's speed in Sector 1 of the Spanish GP was 289 km/h.\n",
      "\u001b[0m\u001b[1;3;38;2;90;149;237m[ham_sector1_telemetry] Q: What was Hamilton's speed in Sector 1 of the Spanish GP?\n",
      "\u001b[0mINFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "> Pandas Instructions:\n",
      "```\n",
      "df.loc[df['Sector'] == 1, 'Speed'].mean()\n",
      "```\n",
      "> Pandas Output: There was an error running the output as Python code. Error message: 'Sector'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/Users/diegomaradona/miniforge3/envs/formula-1/lib/python3.11/site-packages/llama_index/core/async_utils.py\", line 31, in asyncio_run\n",
      "    raise RuntimeError(\n",
      "RuntimeError: Nested async detected. Use async functions where possible (`aquery`, `aretrieve`, `arun`, etc.). Otherwise, use `import nest_asyncio; nest_asyncio.apply()` to enable nested async or use in a jupyter notebook.\n",
      "\n",
      "If you are experiencing while using async functions and not in a notebook, please raise an issue on github, as it indicates a bad design pattern.\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/diegomaradona/miniforge3/envs/formula-1/lib/python3.11/site-packages/pandas/core/indexes/base.py\", line 3805, in get_loc\n",
      "    return self._engine.get_loc(casted_key)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"index.pyx\", line 167, in pandas._libs.index.IndexEngine.get_loc\n",
      "  File \"index.pyx\", line 196, in pandas._libs.index.IndexEngine.get_loc\n",
      "  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7081, in pandas._libs.hashtable.PyObjectHashTable.get_item\n",
      "  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7089, in pandas._libs.hashtable.PyObjectHashTable.get_item\n",
      "KeyError: 'Sector'\n",
      "\n",
      "The above exception was the direct cause of the following exception:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/diegomaradona/miniforge3/envs/formula-1/lib/python3.11/site-packages/llama_index/experimental/query_engine/pandas/output_parser.py\", line 54, in default_output_processor\n",
      "    output_str = str(safe_eval(module_end_str, global_vars, local_vars))\n",
      "                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/diegomaradona/miniforge3/envs/formula-1/lib/python3.11/site-packages/llama_index/experimental/exec_utils.py\", line 159, in safe_eval\n",
      "    return eval(__source, _get_restricted_globals(__globals), __locals)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"<string>\", line 1, in <module>\n",
      "  File \"/Users/diegomaradona/miniforge3/envs/formula-1/lib/python3.11/site-packages/pandas/core/frame.py\", line 4102, in __getitem__\n",
      "    indexer = self.columns.get_loc(key)\n",
      "              ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/diegomaradona/miniforge3/envs/formula-1/lib/python3.11/site-packages/pandas/core/indexes/base.py\", line 3812, in get_loc\n",
      "    raise KeyError(key) from err\n",
      "KeyError: 'Sector'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "\u001b[1;3;38;2;90;149;237m[ham_sector1_telemetry] A: There was an error running the output as Python code. It seems there was an issue with the 'Sector' column in the dataset.\n",
      "\u001b[0mINFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Verstappen finished 2 centesimae ahead of Hamilton due to his faster speed in Sector 1 of the Spanish GP, which allowed him to gain a slight advantage over Hamilton during that part of the race."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "response = s_engine.query(\n",
    "    \"Explain why Verstappen finished 2 centesimae ahead of Hamilton\"\n",
    ")\n",
    "\n",
    "display(Markdown(f\"{response}\"))"
   ]
  }
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