Table Parsing From Document

Extract a table from a document and return it in a structured format (e.g., HTML).

You can query a model directly and test the results returned when using different parameter values with the Cloud console, or by calling the Vertex AI API directly.

Prompt Freeform

salary_table.pdf

What is the html code of the table in this document?

Response

<table>
<tbody>
<tr>
<td>Grade</td>
<td>Step 1</td>
<td>Step 2</td>
<td>Step 3</td>
<td>Step 4</td>
<td>Step 5</td>
<td>Step 6</td>
<td>Step 7</td>
<td>Step 8</td>
<td>Step 9</td>
<td>Step 10</td>
<td>WITHIN
GRADE
AMOUNTS</td>
</tr>
<tr>
<td>1</td>
<td>$ 20,999</td>
<td>$ 21,704</td>
<td>$ 22,401</td>
<td>$ 23,097</td>
<td>$ 23,794</td>
<td>$ 24,202</td>
<td>$ 24,893</td>
<td>$ 25,589</td>
<td>$ 25,617</td>
<td>$ 26,273</td>
<td>VARIES</td>
</tr>
<tr>
<td>2</td>
<td>23,612</td>
<td>24,174</td>
<td>24,956</td>
<td>25,617</td>
<td>25,906</td>
<td>26,668</td>
<td>27,430</td>
<td>28,192</td>
<td>28,954</td>
<td>29,716</td>
<td>VARIES</td>
</tr>
<tr>
<td>3</td>
<td>25,764</td>
<td>26,623</td>
<td>27,482</td>
<td>28,341</td>
<td>29,200</td>
<td>30,059</td>
<td>30,918</td>
<td>31,777</td>
<td>32,636</td>
<td>33,495</td>
<td>859</td>
</tr>
<tr>
<td>4</td>
<td>28,921</td>
<td>29,885</td>
<td>30,849</td>
<td>31,813</td>
<td>32,777</td>
<td>33,741</td>
<td>34,705</td>
<td>35,669</td>
<td>36,633</td>
<td>37,597</td>
<td>964</td>
</tr>
<tr>
<td>5</td>
<td>32,357</td>
<td>33,436</td>
<td>34,515</td>
<td>35,594</td>
<td>36,673</td>
<td>37,752</td>
<td>38,831</td>
<td>39,910</td>
<td>40,989</td>
<td>42,068</td>
<td>1,079</td>
</tr>
<tr>
<td>6</td>
<td>36,070</td>
<td>37,272</td>
<td>38,474</td>
<td>39,676</td>
<td>40,878</td>
<td>42,080</td>
<td>43,282</td>
<td>44,484</td>
<td>45,686</td>
<td>46,888</td>
<td>1,202</td>
</tr>
<tr>
<td>7</td>
<td>40,082</td>
<td>41,418</td>
<td>42,754</td>
<td>44,090</td>
<td>45,426</td>
<td>46,762</td>
<td>48,098</td>
<td>49,434</td>
<td>50,770</td>
<td>52,106</td>
<td>1,336</td>
</tr>
<tr>
<td>8</td>
<td>44,389</td>
<td>45,869</td>
<td>47,349</td>
<td>48,829</td>
<td>50,309</td>
<td>51,789</td>
<td>53,269</td>
<td>54,749</td>
<td>56,229</td>
<td>57,709</td>
<td>1,480</td>
</tr>
<tr>
<td>9</td>
<td>49,028</td>
<td>50,662</td>
<td>52,296</td>
<td>53,930</td>
<td>55,564</td>
<td>57,198</td>
<td>58,832</td>
<td>60,466</td>
<td>62,100</td>
<td>63,734</td>
<td>1,634</td>
</tr>
<tr>
<td>10</td>
<td>53,990</td>
<td>55,790</td>
<td>57,590</td>
<td>59,390</td>
<td>61,190</td>
<td>62,990</td>
<td>64,790</td>
<td>66,590</td>
<td>68,390</td>
<td>70,190</td>
<td>1,800</td>
</tr>
<tr>
<td>11</td>
<td>59,319</td>
<td>61,296</td>
<td>63,273</td>
<td>65,250</td>
<td>67,227</td>
<td>69,204</td>
<td>71,181</td>
<td>73,158</td>
<td>75,135</td>
<td>77,112</td>
<td>1,977</td>
</tr>
<tr>
<td>12</td>
<td>71,099</td>
<td>73,469</td>
<td>75,839</td>
<td>78,209</td>
<td>80,579</td>
<td>82,949</td>
<td>85,319</td>
<td>87,689</td>
<td>90,059</td>
<td>92,429</td>
<td>2,370</td>
</tr>
<tr>
<td>13</td>
<td>84,546</td>
<td>87,364</td>
<td>90,182</td>
<td>93,000</td>
<td>95,818</td>
<td>98,636</td>
<td>101,454</td>
<td>104,272</td>
<td>107,090</td>
<td>109,908</td>
<td>2,818</td>
</tr>
<tr>
<td>14</td>
<td>99,908</td>
<td>103,238</td>
<td>106,568</td>
<td>109,898</td>
<td>113,228</td>
<td>116,558</td>
<td>119,888</td>
<td>123,218</td>
<td>126,548</td>
<td>129,878</td>
<td>3,330</td>
</tr>
<tr>
<td>15</td>
<td>117,518</td>
<td>121,435</td>
<td>125,352</td>
<td>129,269</td>
<td>133,186</td>
<td>137,103</td>
<td>141,020</td>
<td>144,937</td>
<td>148,854</td>
<td>152,771</td>
<td>3,917</td>
</tr>
</tbody>
</table>

Citation Sources

  1. https://en.wikipedia.org/wiki/GeneralSchedule(US_civil_service_pay_scale)
Model: gemini-1.5-flash-001
Temperature: 1
Max output tokens: 8192
TopK: 40
TopP: 0.95