haiku.rag/tests/cassettes/test_analysis/TestClientAnalysisIntegration.test_analyze_search_and_extract.yaml

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        Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.
        Caption, Count = 22524. Caption, % of Total.Train = 2.04. Caption, % of Total.Test = 1.77. Caption, % of Total.Val = 2.32. Caption, triple inter-annotator mAP @0.5-0.95 (%).All = 84-89. Caption, triple inter-annotator mAP @0.5-0.95 (%).Fin = 40-61. Caption, triple inter-annotator mAP @0.5-0.95 (%).Man = 86-92. Caption, triple inter-annotator mAP @0.5-0.95 (%).Sci = 94-99. Caption, triple
      - |2-

         inter-annotator mAP @0.5-0.95 (%).Law = 95-99. Caption, triple inter-annotator mAP @0.5-0.95 (%).Pat = 69-78. Caption, triple inter-annotator mAP @0.5-0.95 (%).Ten = n/a. Footnote, Count = 6318. Footnote, % of Total.Train = 0.60. Footnote, % of Total.Test = 0.31. Footnote, % of Total.Val = 0.58. Footnote, triple inter-annotator mAP @0.5-0.95 (%).All = 83-91. Footnote, triple inter-annotator mAP @0.5-0.95 (%).Fin = n/a. Footnote, triple inter-annotator mAP @0.5-0.95 (%).Man = 100. Footnote, triple inter-annotator mAP @0.5-0.95 (%).Sci = 62-88. Footnote, triple
      - |2-

         inter-annotator mAP @0.5-0.95 (%).Law = 85-94. Footnote, triple inter-annotator mAP @0.5-0.95 (%).Pat = n/a. Footnote, triple inter-annotator mAP @0.5-0.95 (%).Ten = 82-97. Formula, Count = 25027. Formula, % of Total.Train = 2.25. Formula, % of Total.Test = 1.90. Formula, % of Total.Val = 2.96. Formula, triple inter-annotator mAP @0.5-0.95 (%).All = 83-85. Formula, triple inter-annotator mAP @0.5-0.95 (%).Fin = n/a. Formula, triple inter-annotator mAP @0.5-0.95 (%).Man = n/a. Formula, triple inter-annotator mAP @0.5-0.95 (%).Sci = 84-87. Formula, triple inter-annotator mAP
      - |2-

         @0.5-0.95 (%).Law = 86-96. Formula, triple inter-annotator mAP @0.5-0.95 (%).Pat = n/a. Formula, triple inter-annotator mAP @0.5-0.95 (%).Ten = n/a. List-item, Count = 185660. List-item, % of Total.Train = 17.19. List-item, % of Total.Test = 13.34. List-item, % of Total.Val = 15.82. List-item, triple inter-annotator mAP @0.5-0.95 (%).All = 87-88. List-item, triple inter-annotator mAP @0.5-0.95 (%).Fin = 74-83. List-item, triple inter-annotator mAP @0.5-0.95 (%).Man = 90-92. List-item, triple inter-annotator mAP @0.5-0.95 (%).Sci = 97-97. List-item, triple
      - |2-

         inter-annotator mAP @0.5-0.95 (%).Law = 81-85. List-item, triple inter-annotator mAP @0.5-0.95 (%).Pat = 75-88. List-item, triple inter-annotator mAP @0.5-0.95 (%).Ten = 93-95. Page-footer, Count = 70878. Page-footer, % of Total.Train = 6.51. Page-footer, % of Total.Test = 5.58. Page-footer, % of Total.Val = 6.00. Page-footer, triple inter-annotator mAP @0.5-0.95 (%).All = 93-94. Page-footer, triple inter-annotator mAP @0.5-0.95 (%).Fin = 88-90. Page-footer, triple inter-annotator mAP @0.5-0.95 (%).Man = 95-96. Page-footer, triple inter-annotator mAP @0.5-0.95 (%).Sci =
      - |2-

         100. Page-footer, triple inter-annotator mAP @0.5-0.95 (%).Law = 92-97. Page-footer, triple inter-annotator mAP @0.5-0.95 (%).Pat = 100. Page-footer, triple inter-annotator mAP @0.5-0.95 (%).Ten = 96-98. Page-header, Count = 58022. Page-header, % of Total.Train = 5.10. Page-header, % of Total.Test = 6.70. Page-header, % of Total.Val = 5.06. Page-header, triple inter-annotator mAP @0.5-0.95 (%).All = 85-89. Page-header, triple inter-annotator mAP @0.5-0.95 (%).Fin = 66-76. Page-header, triple inter-annotator mAP @0.5-0.95 (%).Man = 90-94. Page-header, triple inter-annotator mAP
      - |2-

         @0.5-0.95 (%).Sci = 98-100. Page-header, triple inter-annotator mAP @0.5-0.95 (%).Law = 91-92. Page-header, triple inter-annotator mAP @0.5-0.95 (%).Pat = 97-99. Page-header, triple inter-annotator mAP @0.5-0.95 (%).Ten = 81-86. Picture, Count = 45976. Picture, % of Total.Train = 4.21. Picture, % of Total.Test = 2.78. Picture, % of Total.Val = 5.31. Picture, triple inter-annotator mAP @0.5-0.95 (%).All = 69-71. Picture, triple inter-annotator mAP @0.5-0.95 (%).Fin = 56-59. Picture, triple inter-annotator mAP @0.5-0.95 (%).Man = 82-86. Picture, triple
      - |2-

         inter-annotator mAP @0.5-0.95 (%).Sci = 69-82. Picture, triple inter-annotator mAP @0.5-0.95 (%).Law = 80-95. Picture, triple inter-annotator mAP @0.5-0.95 (%).Pat = 66-71. Picture, triple inter-annotator mAP @0.5-0.95 (%).Ten = 59-76. Section-header, Count = 142884. Section-header, % of Total.Train = 12.60. Section-header, % of Total.Test = 15.77. Section-header, % of Total.Val = 12.85. Section-header, triple inter-annotator mAP @0.5-0.95 (%).All = 83-84. Section-header, triple inter-annotator mAP @0.5-0.95 (%).Fin = 76-81. Section-header, triple inter-annotator mAP @0.5-0.95 (%).Man =
      - |2-

         90-92. Section-header, triple inter-annotator mAP @0.5-0.95 (%).Sci = 94-95. Section-header, triple inter-annotator mAP @0.5-0.95 (%).Law = 87-94. Section-header, triple inter-annotator mAP @0.5-0.95 (%).Pat = 69-73. Section-header, triple inter-annotator mAP @0.5-0.95 (%).Ten = 78-86. Table, Count = 34733. Table, % of Total.Train = 3.20. Table, % of Total.Test = 2.27. Table, % of Total.Val = 3.60. Table, triple inter-annotator mAP @0.5-0.95 (%).All = 77-81. Table, triple inter-annotator mAP @0.5-0.95 (%).Fin = 75-80. Table, triple inter-annotator mAP @0.5-0.95
      - |2-

         (%).Man = 83-86. Table, triple inter-annotator mAP @0.5-0.95 (%).Sci = 98-99. Table, triple inter-annotator mAP @0.5-0.95 (%).Law = 58-80. Table, triple inter-annotator mAP @0.5-0.95 (%).Pat = 79-84. Table, triple inter-annotator mAP @0.5-0.95 (%).Ten = 70-85. Text, Count = 510377. Text, % of Total.Train = 45.82. Text, % of Total.Test = 49.28. Text, % of Total.Val = 45.00. Text, triple inter-annotator mAP @0.5-0.95 (%).All = 84-86. Text, triple inter-annotator mAP @0.5-0.95 (%).Fin = 81-86. Text, triple inter-annotator mAP
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         @0.5-0.95 (%).Man = 88-93. Text, triple inter-annotator mAP @0.5-0.95 (%).Sci = 89-93. Text, triple inter-annotator mAP @0.5-0.95 (%).Law = 87-92. Text, triple inter-annotator mAP @0.5-0.95 (%).Pat = 71-79. Text, triple inter-annotator mAP @0.5-0.95 (%).Ten = 87-95. Title, Count = 5071. Title, % of Total.Train = 0.47. Title, % of Total.Test = 0.30. Title, % of Total.Val = 0.50. Title, triple inter-annotator mAP @0.5-0.95 (%).All = 60-72. Title, triple inter-annotator mAP @0.5-0.95 (%).Fin = 24-63. Title, triple inter-annotator mAP
      - |2-

         @0.5-0.95 (%).Man = 50-63. Title, triple inter-annotator mAP @0.5-0.95 (%).Sci = 94-100. Title, triple inter-annotator mAP @0.5-0.95 (%).Law = 82-96. Title, triple inter-annotator mAP @0.5-0.95 (%).Pat = 68-79. Title, triple inter-annotator mAP @0.5-0.95 (%).Ten = 24-56. Total, Count = 1107470. Total, % of Total.Train = 941123. Total, % of Total.Test = 99816. Total, % of Total.Val = 66531. Total, triple inter-annotator mAP @0.5-0.95 (%).All = 82-83. Total, triple inter-annotator mAP @0.5-0.95 (%).Fin = 71-74. Total, triple
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         inter-annotator mAP @0.5-0.95 (%).Man = 79-81. Total, triple inter-annotator mAP @0.5-0.95 (%).Sci = 89-94. Total, triple inter-annotator mAP @0.5-0.95 (%).Law = 86-91. Total, triple inter-annotator mAP @0.5-0.95 (%).Pat = 71-76. Total, triple inter-annotator mAP @0.5-0.95 (%).Ten = 68-85
        Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
        we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.
      - |-
        Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.
        Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.        
      - |-
        Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on
        3 https://arxiv.org/        
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wzxagss8x8MWu4cW0LzCPTE9UUIgPQ+rXjx1MQY87ayhPOyErzyKkBI8+k9hO8ISVL3A+JE80OHWvFMTarzVf466vbt1vGB4+7zYpFe8Cn8APC+OEjws//A8/SeJPPvuUTxYDkW8guPiPEjaAbqUVsg8ydviPF4Wp7xJYoq8dvIVPHgsXTuCL5q8ZSrvuvIII7yS2eG7I6WavNJspLwoGqq74TWBvNKBQzxOAnG8+zLLvG9kBDvmtzM83qLKu1oJs7z+Sv47aTmFPKE3YzyYb2w8VkEJvHI+Prrzyh08KBDrOwvhxzxX/w498/nZPLuqBDwTBOC7cOmOOxhmU72PAb88u9srObkxYrxP1KO8g6R1vAg8IDzrIWa7FZDbPKsr37zoRAe9N6myvM4ihDzlB4+7W8w4vGNDsLx+oIA8ingGPaJtO7y5YCc9k5J/vLVSXTvLc0C7USaQPFF0yDu/qok7JGiyOuOuDzxQnRU8wU05PKjc0DobULc8RGSEvbePyLvbK5Y815ryvLrhoDzOMvW7E7BrPHfxizo5YOa7iN45PNj2BjzIIrG7p4IjvOoaCD1wK2c87FkMvG9iHLyUkgC87g5tvGx/IbxvlJY8eQU6PTD7CDxxaA29rkuaumqcpbuXg708RXh3PHdZEjyLmZ282RpiuvKbY7ys7ny6B9HfvLoMnzstLQk7ac2aPMkq+LsIn2S7IgrwPCsSpLxehKO8rD2PvPXgjrtEBJC8eowmvS9K4rsBqSg6p6e0uwXAr7uaSsC7GWliPMFMwTrk7bY88P2cPNXTqLu7HGm8lL7RPEx9z7sd+9c6vXACPaLYzLyVc0E8oUHXvCUt1bxQvAs85oTQvJfEE7ypPo875kUlvLBAy7ynrfs5XTsPvV+/aLwMmxI9fY93PGxMDbsu6u08+Vs4vRDHnjuUQgQ7IMaJO5gENzztrYm8FVWZvGxYP73mTf27v/AMva9+0jx8ars89ifuvJzS/zx6Srk8q4tuue+VTTzo3pQ8w5YMvKbnBT3sqvS8a42Gu3eR+bpifJm7FMvwu2Qtijx4LYW8rh4+u9uJmjyXAWE7bmpzvCMrd7y1AK08LJ35vOeTvzx74qg6KkY6u2RXMD3yaFi7nl8NPXf1xry9Cis6S8/kuxNaJ7w1dYs8CTAKvb/z3rxOiko8PHObPD9eATwN/d+7bfKZPAW3abql/bW85Pelu0pME7zZbAA9InapvFzlVzxZLmY8fB5WPKCy8ry/+6M8CcEiPGHBZ7y44Iw8zd9NO5M+v7wfirS8eg4IvJzuCb3Az4e62pE/PedbIb3dIRC9DolEu3DohDp9pYs8h8KAvAA8tjxXgqk8pKgvPIU+YLtsjKy8qA1Hu0RSOrwjv+O7qkrIOw3lk72TDHG7LiWCO2Te1bwLWVY7S4/3vED/Frwdn8o7j7xdvNngYDyxEUQ8boIzPfYcoDxk2LM81UWfvNhedzlGCJU8FgYLvDLLe7lv6hu8MwVbvMd6bLz5Z1u8cm1svBx55Ds61Ia8AlYzO57v+7yjUR48GgoAPVRivjyuyHm7XIwrvGtftzxr5Yw791q2O8TNGjxuxZ47n5aBvNgaRryVk7i540nmumli2zt4AKM83ZRzPBg7V7ywnhk9A1CNvCpz+TvPb2Y83he5vOzlCLm9F2q9TyNuPC1hvLy0RKM8uz5duwgrEDxPkZg72gOjvDXP9Lv1JSk9ET/zvH1R8zyfWC29Mma2uwvzJjoZ7488LCckPUi/irx+po68GDuDPD3xQ7v1Jx69a9bGPBntDD0tQvg4omNAvLA0EzyFvZC6vn4OvLTb5rqwQW88nSDqvOyIDryRK1S8SEMAOim5tryfS9S88FsuPJV9ibwkrzq9NuYFvBT/DTsTTQa9zPQzvNnYSLyc77o8qh1fOnLlAbx+zxw7GWkIvOe9Mbyc3bc8PItuvCQmfzz66u478EPIvN4erjx8GVe8PsqSPOFyZrxCBgU8E7uiu7p9nTx3wUw8SHt4O2BlL72/ijS8xAqKuq43C73vewO8cffdvN5lPzsaXh28IHmIuzwYzbut1OW7wOkXvHjYuDtyIpA7urXJvGFVgzyuRBw8nEYgO1BzmTwbvPq8d46JPHPh8Ts6MMG7iKI3Pfv7ybyLog69cTVDvcxwzLx7wX68P7EBvcv6nDy4S0y9IrAJvKijJ7wzX1W8z5BBPWtjPTtbTnQ8YxmpPFiovzz80Xk6JmM1O9mZbbw5zvC8j/cuPIi8VDwsH2a8xLQEPXnYU7x9J8c8XljFOuRAqjxBoAA9hH0VvAugMDvHg+Y8S1uyvIX6gLpNC5C8sngAvI3G/bsvUdq8iampPIKktrzMJYA6BXCyPLSmtDwKShY8EUF7Om8UFD0cykc9+rozvfDNGz3r5Wu8q8zLO+jZ5bsQ59e8gf7DvCWboTwM68M8gjr+u56QhzxpFa27gJlXvL0Prbl8rHa8ag5tulYqBL1kkp66Hi25OivKD7zWacc8/gpdPKGzA70dyjm75ZvauzyqBz3RI568FN3Ju8jmwDsX1BS9a/63PGlvCryQIKc8N8OEvPoyFjya6X291soUvf9hI7xr7Sy6Y0iBPAPNL7x396S8F2mkOxN2NTynRU47Roj3vPsyULxT2/27+ynJuztasjxhzVA7b3eKPK5krTuIBIA82EG3Oyq4pbymGqk8hGKePN6eC7zBTaq8KXweO5i5SDqpzXA6KWKBvEk7hTuc4aU76YqXPBTggjs8mKQ74YldOzPqkDwyA6q8+bLpPD78GryJ48s86PjvPGzczrokIH08+E7IPGYOFjyQne68IqSXvFeofLxNwE683m0/vOQ9rDyOvKK7krgfPBxoEb1//KE8/7jju1fMfjuqnQQ874KwvJoXJjy0akM72gT1PN7iJjygysE662xGPPzWgru/EEi7HvDqPJvMW7vX3Ki6oD6SuyJmhTydake8Z9vpPKx0ILyRGoG8tsvGvIn+8LwVHSC9SRzPO/iay7xZRkG9pOFPu9pQiDxgKnm8daneu7LwZDyudjA84C6VPPja27w3qH+7Q5CBPMqiWTux0c285wbeuylN8jvmKME8kgwCvc0ys7xoT767q78rvd/K3junQim9p9jPO8Clk7qGSqA8EINevDmQ5DzLbAq7uXuhPClFMzzBcXY8t13LuufZwrrBgJo8IQ9TvPEdBryTwTg9MlxsvP3kuTyAUwC8NWs2PAA9UruitIc7FrbBvPyVJT0PmmI8hPljvP/7Ezwv5/w6ZBIHvWzeBzx0ibC6LsnuvBxLPry39kI8F5CwPJE7gzwp2Rq8ZXm9O758aj1zCIq8vI6JO0jcSLurF7E8N1qju9GvxTxlrje73XPKPCfbUbwFZ7Q89z0xuotmaDzqfqI7dwtZPJe74Dw/ZAy8pIwGPCKOLLsXygQ9n1KlO0aweLxpDoq8mKp5OwTrRDxi1Mc8guoiPSO4yLzhjic8NIicPP5cYbv1T2M7baabO5fkAz3yC7G8e/nmu8Fk97onXCw8yf/0PLtTsby+0628K1zMvJAREj04bYy8mPlRPHe6ibykl3G8uZHZu2XfWjybKe27X0lTvFT6sbvK2jE9nb3qvIXqxbx0cI08TjvcPFXzGz1uU4g8IBfCPNZmFj1fRgM9nCmnOz+RFbsvO068VsMAvdNXNLzN+Rs8wW1LOyL88zxTegI8s7pOvFGgxzvOJPy7iEWivAec7rs+tpk8mNmsPLWsbrzLFxM8quEKu3o0xDwWcOe8e8oMvHfcrrvGbyY7cmGWPLtFwjx/5/68UP7FPAcz4bv12y89gQvDPJKwc7svKCY8b2M+OpuHQLwS4/o70K1EPOJHjzxRF7i8vjYAO2M0BT28OLs7q+lWPE/bObuNdA279WOpvMduzTtl35i8Lj8kvcgLgjyGDTi68M4XPajsBToPhhS8pZOUvOFgcLyc+bK8sLudOVSvm7zNdFE8YLggPTGlt7woFda8kZAZPJiSFDwHtSm6BqZTPDriFj2bh5k8uooWPFGWpjy77AW8cfcKPZhFdTs52hm9uRsrO3Sh8zzTx268hCpOPFLz27w6dga9XTOaO2HcjLz5wdi8731KvL3YdrtUoKi8CiN7O2+YfTyiNia9HddFvEv/LLy3VEo9v1wFvfwnlzwHDFU82sKnvBISijz1voA8SG46O481BDyq2OG7LL13Oec+BT0dW0a71L7YvOw/iDsBfsK75N3ZPCR1ors28aI8Vm8EPJjHfryzXCM8bP++vGl7lbxcN5W6Hm2EvK3SqbxIzYa8ISMRPCrK4juCsl68AgkUvFSuCTwMSCc9hM1qu7x5DDy/RpU8kzmQupiIFbwWqVi8jKIsvCQ/cLxXpQQ8l/fIu6FngrxGNla8/libO22MqbynCdE8atWlvJ6TJL0bPTK91u2IvK62krtCyy69/xriukI95Dveh7e8UY5zPTQbjbyyNXw8lOEsvD7PIDzND7e8liUrveL1jLwhEEI8OQvvvEf/IDtTye48C5UfOzK4yjqDeja89A2GvNBbnrzTNuO8oyeUuwVLkTujobi8bhwMvVONcjymKDS9pUzsuwQxhrzKK6Q7d93tu8HvorvJA6a3cdLUvL/0GbzexxM8GULVvNuz8rxeC1O88oWBvESdjjuwMGo7pmu6PGgwE7xYtm48yV4gPFBUar2T/KO7nRp8O+ZtsjyCcUS8sRPkvDAh8rvEPjQ8kCt6vIwjOT2TWSO7iCD3O1a4+rtRzhm94re+u9+LP7wxR+683HobvWWA/buohMu7F2x1PP7eFrw2iyc9I9NHPCxpqbxzzji8+QoCPGA5ADx/iFG7kW8OvEWxJLy6Hps7jJbIvKJQHrz9hh088rWOvNvCpTzCWbU6fOyYO2sihjwvNFo7PIMoPCzhCDww3f68d2/AOh1v8bxhBDS8HZ79PPhkyTyZ7c+7CX6lPFWW67sKdb67GAOqvONXk7xxzqK66sK/u8oTA72UZ488FWXvPF59QD1lDYo79V1FPAM/tjzdrJQ8aLcGPZhZzTtBS3E8FZ7TPP7fTDzSYYE8B5kGPeGZZTx67PG7NTksO47WdrwioRg9a81ku9MKvLyoyio786/SOmSI3Dy+TWs8EtJvOEE1eLxtmVC9WtJAvXsKtzxJUiu8zRGMO8kXEbsmKYc78B6eu+SoJDyUZ6A8mPOsvLduKrsLO5Q8zEgFu65Uo7q2hsc7vSNNPNfT27zAvyu8hKMAvICQkTxo18Q6ZiXWPJsLRzxiw0u7a8JovAoZzDwicom4e0jGvDjRVjzu5J68H9KpvO68hjztcZm8aJvFOZG+XrwYS/U8eF3UPCHYqLzkkUM7PHoqPW6qZzzRrAi8WuDIu/L8Ib0kZTe8lPpYuyPINzzENoc7I44HvIuUAjzU1bu7HwP/PH8Fqrzuv2689+qCvF1Qobz4LmC81yq0PDBmEjyIMJs8BQaHvK/W5buKg+q8eaWcPBi5WjxAZRi9ux7nvDX0c7tEMnw7tb65OlkrBzzpDvy7kh/0OaZlgrwlWxU8arGDO20S4jtaFSa9w5uOPJCL+jri5NM8sZeOO15fIDxRL7o6hQ44PXK65rxGIdM7q94ivEe1WDyIzFg6598mvbxb8LtDYPg8zAaUPOUG6jyT2F48+GE1vJOyFLzAfwq8G98BvRJOD7tWxS28E55uOxaGLD2hnTS8VqqxugyDSbxk0Ry80BnPvDzKGb00ZLU70cxlvLpGtLw+uYu7r0Q+OxfsmDyBfkq8cKaYO4JFTLwNj927BcGtOpXSHz1to5K8gX2svAKTXruj/5m5eVAlvEKUPTzkV0W8wtyfvKaRYr0Zm/E8vfCXPLaJCj3uC7G8/p76u+sIzTtIsbG8x9YqPRJpPjwK+1480zyWuhKNojzW01i7ZSVsvOChm7wNV6q8StM8PAuDgLxq//Y8pMcSvY+P6ruis9+8wTTOvJ0phTxnBtA7zUC+OxVQlbuRaqi8mfAYPbnOnjvUxEm8YGvRO4cSHrzeQty8UGyMPOn07Ty9tqc8ROd5vAwu7bobjD482iMSPA3FNTxNzGU8K8l8vIb3+jmZDp+7OjeDPEuTp7xGDo+8Y9/kvGr4Qjw86JA8X5eWvPBYSLvubnS8yyCbvJlVF727Ta28W2VjvPYb07vm6zo80w4PPSq/FDzf+7y8tNHIvOIjrTxmXvC7yncGvYdNU7yaOxE9dN3uPJ79+bxaJ6u5Fp6QPGs4AL0pDjq8XWSjO8iVUryOmqu8V4vIu6Aa67zF1iC74zNBPEVvAz3yyUe9mlHDPN+VdLxoEp081DerO+HiSrvklEE7bKIUPFeqFryDANM7rHSiO2v4krwrQz68yjX4uxU2frxIuJM8HQAqO9tgiTtpRKG8XxFaPYIqozyIsN670rn+PDwPIzzOlg28qACVPCMYDzux/ws8VLSsPJFUDTst46M83qC6PLd4zzvB+Uq85XQbvOfIwzu8syM87c9PvA/qj7t4+Aw96lSpvJ7wp7x4Tws82tEBPYb6h7y/xHG8hWOKvPBxDz1E00U8XVZAvQdyI70XE6+8VgjeO92AijyMsce86aRkvHctAbweX9g8oOn0uzWsHT2CDL28lb8XPPOmHjy38Ky8qwn0u1l2try4RL48D3y+vGRZCr3RhX47+WOZu6SOQzySlbs8T5cdOyps7DzoQV884pI8vSUE/Dz4ukC8725YO0pmLL0I9mM6R61JvD8pi7zUOgM8tnvovL9BGTxf04S6eyYFvNNcKz0rnz283oPRPGIuDLsAQ8W8Yf2Uu7r2bbzzY6k8OitVvPmZQDuhHLg8OxOnvMwsj7zMpyk8795IPNnQTDynFqS7qPGZvBAb1Dz5Ufg7IgUsvItk0Txg+7I8W23GvKl3WzuKx7g6tZCgPNiY0zzjSzA9RHNGvHTutrwQrac7OFiqvEqy2jy7JAa8B1GBPDKxD73HkbC8PYvju5xdCTwnRQK9oW71utlUCDx2CIK8XvTWu8JzC71pte25XNMePHA/3TvV4DK8/3TOu6U+lTxflXg8hMP1O5aZ8TwHgTS8n2/Cu/neyDwnkY68BauFPOaBljwgBxQ8yXsru/IDL7xva1M8kz4evLsArbtuz4G8RbkcPTEoqbtn7pW7RJfqOl8h7rwO+Gw86Kv2PMjZMbz07f87dSyyOyrKWzzdvl+8MIR9vIj62rwImYw8SdLHPBxAuDoJG/k8cOs7vMgdlrz+SVO8NXLKuzX7lTtb0fW8rW7dO/Qwijwtft47VgPavIdxOr0Lx7+8lK5bPVCTFj2Ou5m8pYJkPNxEaTxPx+g8fC+KPBXDj7xV3Qk8XbatvIc2Q7x/KhW8i2C4PDtLnTy7u+g6mxPFPAixizw/7ac8wa4Wu9g0JrzESEi81AwEvCaW4rugCmA8cFqUPEpVjzuDVO28XFYxvBP7KTw5mYW76x+BOxdpyLrc5hS8q8RZvPqYyTzstGu8qcCjPK6G9rqtI4+5yqJdumCeOr0X1Bq8B5rEvDjZCL0258a80Al0O2jQ/rr+EB68K3k9PGpYs7vJSNe8a/JePE4QhjxwfYs6f5MEvGWV87nkCJC8PisePVgpHj3+Bou8A0XjPOGw2LtQqCi8GmwvO+GCZrw2PJE8uZbUPBrYrDvpEBO9i+shPTJQrbwG4wY84r7+PMakNbx5vx89MfjKPOSdkTy71GI7BO95PK2XRDvIRga8hnvgPP+lyzwfWpi8Gvyuu3LeVzx9Cd088UKZO1UevryFL3A6r8HzPKvLAzxU0dE8aXO3Ok7VqLvX2RK7u1vJPIzbCzxhRsC7tQkcPLt76buWh5S8Wbscu75jZzx4/Yg7AW4LvJnFGrsh8qg7/On8vAosBjwznSq8sHusuz7g9jqqLkk8DUeOOm3bZbw55bI88dPwvHIs2zsCNRs9OCOHvJUchrzPqpQ80D3pO1peLDxS0oC8LuCMO57Rzrt2COA833qkvE/0A7w3hgW9aQ6aPKFcTjxznno8okY/PaDaoLyTcyI6DWO5vEgyGzxxxUm8xzSJPCHe9rwRq948qvQEvV0PrbsSeqK8K1S0PN8fwjspW7S8gW6SPA7Ul7yYCl48qaP8u1srPzwCI9k8ZMfnO94smTsf7Mc88G4bO+oXrLxe6xU8dC1qPLZqMbybupW8ngB4PNz7WjwgSsY8ItQOvYU6m7zbTzi9cKJpvNuvXTxiinW82v5yvQoavTyXLUQ77+c9PcEaALyE75C85Z+9O7Mpkrz3O6m8YAosvArFxrrCeRO9y2C8vMYuH72gzTQ9YPbgOhZ7trh30gm8ubW6vNeE4zwabAI8y2eRPBPkjzvh6Bm7UayuvJQsu7xjsyi9sruDO/OxpbsQyIS8Ob0PPKh6ezzYbEc8qfT6PPTHKDzyMtG7dd4nvVYXZzyOPjo8OmbHvHtawrxOy428vz3bvD/hVLyBHkE8uMlTvKQinTyKMdM7F4HTvAV65jteFew8BTZFPES80zvx3OQ8SXtpvCpaLbxHK1E8p8CavFYr/jl3E4e8jJGgvIbsprzKX4m8NSOeuwKTkjze4FU8falFvNvXv7zsPcW6wtyDvDmuPDzAZOu8GHVCu1Fw2jusos4825SfPMZtYDzdDis8dm4LvNZIl7wRaFs7TvmwPA==
        index: 0
        object: embedding
      - embedding: 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
        index: 1
        object: embedding
      - embedding: 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
        index: 2
        object: embedding
      - embedding: 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
        index: 3
        object: embedding
      - embedding: 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
        index: 4
        object: embedding
      - embedding: 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
        index: 5
        object: embedding
      - embedding: Rh27ucGhrboblO88OqqQPFcKrrqSVns9hwlePbN5ljvV1CA8HGVzPOkKLT1cQmY9AdAgOzzNVL32Cm69QbmGvawf0joKbAS9SZl3O/QhTLmTPYe6dG/SPN+BqzvLoYI7VRs3uxYP1bxzZ568lR5mvMP/hTyJvlc83xc7PKvqx7zmVBU8fxMCPFrhyjnpY568d4FSvAlWV7qea3272jwXvXXBWLycg/O89EJvPM1DvTwvZlG6F3M7vD1MxztSL2i8XdlyvK+eGLwCcbs7H3tFPN23W70WoZe81lcSPS1jxbyumgQ9s1qpu2inWLvqHyK8X2VPPBitdbzTVQ87xOmZuq1XkbteJAC9kOIHPJ6cUrxIRgo8XWEnu4fIkTwJ1fS8Se7Ku5SbEzzR8b48/GervBuKbLz028m63WCOO4S/3DtQbrG8eJ0sPJLI9bvRj8g8NB6CPGQi2Lt+/hQ9wc/5OxzJ2rxKBT28Y1SePGW8xDupnD286qa6PJSUkbvY5rs7lHykO8z0KbzUguC6RS2hO7TherxtWOe82BYcPSLNQbwYAg09gCn6u/BOs7sIYzi7ayTGOwKAADt+Dei7fBSePEqCz7zdQBE94RSaPPLAGDvH8/g8FHgDPRVUyDtYIkA8Uh18vGvYjzxYBlm8wy+ouzTM9DytFku94VGdvMrvnrvjq5Q8rUybO8AGpzxjNge9CsGNPJnPj7x6cRK93XiBPHqMmrtzSrc77LXMvAY8ojx7wA281Y9vvBkpD7xwKHc7aEOBvKAnWr3ofBs87AxiPPzCJLtmOMk5qM2+O8zR47x7kpc7es6RPHULHjxNltI8lxNlu+6q0DxDNU48KzZAPGWdBLwuGE25AH+KvNTMjDw1N8s7VkQ3PF0Ozrtm/Rs8clAivMolibwnQWM8Ulvtu+9mbLycL9i7MkZovGGiervHgv68pI46O9GFfLyXlCE7mxTyO+1NRT2/Ti89YHomPPuYgzyvHCa8rDJYuwwci7wr8AA8T4iXOrs5gDtRW8G22HKAu10d7zzuWfe6JxyLvHYXkLxneI87qq4SPJhw6zxzISC8V0rBO6ViJ7wTQVG8T7WvvE1+MDxLklI8VSUEvH66O7uxl8y7j2fmPLQFS7saohU8vSIXPN49SLtL/YA8r/jmvMNWk7uyoY88NT6IugPj2Lrh4LS74Q5pvIbJHbu0r4W8TB+xu1Hhtjt3eUq8yUFevHG6CLxk//Y8BYMoPUZt0TrrJF08b/1XPNIx0bxfb2C6SmlqPABd3Dz/WSq9X2JbvLfn7rw9TY+8h5C6O/aJv7xZw468rNHKO6Lk57zzv566HMeXvAG2Bbw6fCo8Q1KlPDJ+qbzC5uW8yXeau6KwD7yq1AK9YT9qvOPMijkr53I7D6YVvYITcLzs+ii8KhmtuStKrjznAZQ881I+vSPVWTuuyZW8oNVOPZQ7lLyMJwM85vPqOxBJqDwNH6y8hgXBu+KUCrsfAp47/xkIvF6G5rqbYa48HQHvvBDkN7prGr68zeTDPDteLz2+L6O8XcPXvD/CaborBjY8l5quPHI+H7wCeR48CURtvC7Dtjw3Z088BHFTO1n1wru57i26ybEwvKEQczl5KlI7jlAvPRA+mTpk2Ao9xAUPPKDh6zpLIAs7tLOovBHzsDjs1Hk7VEk0O+brBDqIj4c8zrQ0vAgmgrsPW5Q7DExPvPU94LzDpg88Y44/vbZ7p7y1uom8QIgHvDueOjxmuus8mx6JPF9H7Toc7DE8tIM8PNjYwTvDq4u9BGevulLZJToke6a8ww2JuxN1yjyGXik7E94AO+I6+bxB+oo8kq3MO5MYCr2pz5i8OrTZO+AXYzvEJDY8g0d8O7DkHbmugTa9HnUIvWGSSLx1kyS8/HOxPNQnBbtszYQ8iekrvElOGzxeyiq9P1pjvJUaWLsyris8z+IAPOrsDL3uDQC9h+0OvOlupTyLkpo7qDfuvHHvE7wnJ8k7mqwlPVudAL12XpG8CMY3vAAI1TyMrsI5w0ohvLNAAj3voY08fbfgPFObtrzzn1q7R2GevDlAN7s5Ud+6wc4evM4eqTtOZTm8+hGhPILiELvAUJu6P5QXOu/2B71m26U8UX12u1fMzzuLyJw93dYqvYhAEr18que8/voUvd5wF7yjEgQ9lsXCvNg6w7yFFzo8R08FvOvgVDoust482cGmvDeOxzsQ+268n3+DvUSuiLwlRIU8Z4ygvDZxsLs/NbW6Sd4ovTYJRLzs4AI9Bo68O039brwxJxI9/ViHPBjOwDyo9TK8WOx9vSqBJTyJPyQ9rayWPFehtDxywrG6jWqEO5593Lwlayi8Nn1YPMeTA7z8ZEs8Un4BOvuySrzD79Q89bvXuu66hjr//i271UkMOoldm7si1VC7Up5lPE/ii7ysW9g7vvbAOwYYEbw+MTc87XPYuyFchzsDh+a8QZryux56Ur2lkd48m3NxPGsj7LwQ67q8MPouvMZskrnMJ5W8b43Xuw5Wnjzvdsu7O8GLvEeIKj3q1D28hIoFvCW5urtbwxu9CWAhPDaqH7vQwaM7mhcQvMyyerxHdLk8aIpnPIwwZzydXac8ZH6tPPi6CDwAysu8I1t3vNBOozx9gFC7VoYFvZD9RLvNTiW84eC3PLgCIj2nHYk8tvecPDC11TuWL668kwKnvKy/fbw4e8K7GkmmOze/Gjy2Ir88minlvKQ9PbvPpww8SH6JPEyQjDx3sRe678YsvOdfqjwdzZc7NHlzvLc5cbz8EIW7WQTGO4ZigLwYpum8BMJVvDsHGbwhciY8ZL2PPOFlKTvtIYy7q4sUvMA1wTtaqDs7pVaTOsp8Ejy8NKS8MJFTvJjzNDwPWIG87aeAPCUrwTw1xji6qWWau7lKMLzbKAG7kJikOTN31TxCplI82bMCvJcOGT2Xaji9OCA0PV/za7mB56e4GLDQO+pP2byoWai7PlBPPCVARjvYNpk8xwKLPKvWBbmZsoG9v/AWPUc3CD2NWKI8MbWzPJSRGryMH0s8ccGLu/zlN71/mSa8mJugOzJeM7zLGVM7+DFPvJq+wTxN3SA9lINku3laH7wJd128tCU0PGxaDjwfwMU779QFPLtblLyPVm+8FVZUO9PW2LxOg1C82EmIPHVHSjymZry7bcWevDzvnDwogqS8Ub5euq7MArxZr7s87G/svMvhQ70LcFK8ZGOHPHU0zLyic4+8Mo8JPaypOLyQa6u8z+3uPA0Azjxy+Ow8kQakPIS73DuUDcw74Qx+O2nA5jtLUqe8eL6DvB/CxLylOSq84szIvPcaIrwnNzc7Zs5OPCe2Gr1P8o47InT5uuWharyTDgg8H13RvPs55bwCF9m7i5q5O07MAD2Cv3C4gZu1O78V9LyjfO+8/ymgvJc5Dz18d2a6oL4su+1QizzFO6Q72K7hO8RhubymvCc996l9O6Ntt7xF3cu8W4MwPE/jiTwOQrw78BeIPL1cPjt1iwo9lV1JvCMk7byHlBE8GHmHPO3esjukVz48EFOTO5XpNb0v0IE6oIWFO4KzTrzsN7q7JZGXvKLyNzxdI+M8RSKQvNsT6bvTKAk79utnPOxx+joM40g8PqyPOygTcbyhlhy8yz7guipGW7y7fHG8Yow9PAXkvzwh2fO8MV+GPKYA0rwVgVm8vZ19OiWTDjxrM/U8dQdNPDOXkLucvJQ8U2kBPfRaMLxU0bU7uco7PElADb2dTyO96F/evCUyY7xKUj68QJkXvKTEDr3XNKC6XrLPu7KdYDx92sS8rlcQPHHFCjsN44286LcivVutgLwzuiY9uCT/vB7fpLydVCG9Ex1lPCdhCTzRnCM8+qxjvNY2ED1fI/27JLGPO+aOsDtS7d88Zg+svPdzbjwjhrk8nDwHPb5+4TqNIA874OCNO9qMCz2RTq+8InD1O2OuirpaZVq8kH0YveOSHbszlZI8/TQAvferUzuFa7s8Nk6dPEnIozxjYQm9N+fLu74SOD1Mzqk7SHYBPSvm3rzlaw89RNC1u1NMB70Rd9C7hXXyu3ucszsIMYg8aqYGPTiJnLyil4A87XkMvUYyk7ipVr68bKaRu0gQND18/xO8cxChvPiHLDyxEGO8tj8DvAFjhzxRR4M8/X9hPa6a8roLIP689bfEvOTWyjytl5i8oKiUvKlCCrvSp4i7WvNcue/xk7xPPpe7ldrDvIa/0bzgCQw9DG+uO2jGfDq+caA8+6mQvLx5fDodrsA8LC+5vDzdljzf5py8vK8hORT+5js79Tc8lJhuvJyUsjxE/Ig8uxk1vBpBWbzFFgs8n/DuPL4zVrw/k/48i5ppvAH1vrodjr47+qfzPPWsmbw/vwC9HLyOPATNyDt8epM7eJTUO8vQ7jt/ECQ8idOtvIBie7wuQby76nivPCXxxDz2UTk9+LjmPCImPj1PdmE7tRXfPBzXYbqplNA8sicZPAnBPDzNUK08nZXIvKVrrjymL7i8wS0KvT1t1byEDhQ9yOTeu1iB4ztpNeC7RH6XOwnyG7ze0Ic8TnSnvOs8Bz1Cp4M9QW7vuxuRobwv+tA71+rXOscuNT3rfrQ6Ym3XPEheZLzdj4o8VnMou7LHmDxUqXS972KyPDoWQTxTo5S7TsR8u59ir7zhMhK9eALjPGVPDTygrts8U/Xyu7aYCz2mw2Y7LkHfvMV9MD1cpvm8fUahvCxj8jysb+c7/y5jvAwnEzwMFpc8J1bUOz5d9DkQEFA82t4UvYzmtbhNYpC83zq5PPL3Rzz8hpC74nL6O5QcAzzUjhi8l83ZvPg5Cj2ubYC8Wcn/uz0jkzzVxYi8DoV4O3Arrjw9Dzu7YZCtvL4Gp7vq/sg8tEC4vC0sKL3zAVW82L3OO2bybzz82ya92ok2vMk0KLy+JIQ8BhzTu+mtSjxaq0k8EMBNu9sPFbyOUau8X0wYvEKlkrxvbM68gtktvIJlOL07Wpa7JXu4PIBQ3zxmBxu90XBrOzfO47lnhF68GbRUvJMODTy6Bxo9S8WFPDeEDLzujxQ9rvmgPAMf5DzrkhQ8xC8qPASxCz1GUps6qbXMPDc+jbzZk0k86teUvDjXGr3WAsW8ET3SvEHifL0pd9K8SSZHPMlFB7wBqCU885DAO23T/TxaSGS7RotwPNS4tTsoYP27VAPEOm+3/LzVuBg8yIpjvIslmTwGXr+8pH+KOCsYz7zFIxE8lLHVvNguFLp/Wt068WsPvAgCITw3G328+xVvvKlVwrx3Xtu8l48fuwFDnLw5hHE6LK5CPMZPbjwZg+M6H5RPvF1yVTtW24M81ZX+PMvx6DtOBxY95bNqPML7hjxxSYW8SH/oulF6/LzEQa08uwIHO54YSrw+vTc8bPz0vIkbETzQzC+8IF7dPI19azshh7O8/3zYvAbCbzxn9wE7a0qXvNPM7rwZxYs8IBIfPMvRQrxGlyo9smpQvDb6BrxRJcW5LZAGPEKdUbpJhuA7ZsjMu/MlgzzG5II8TR+fOxx1Bzze1gc83QjVvPxsELz8VyE9Qf6Eu9xH1jwaAAk816kVPXFr3ruBFzG7n7SROgW9ZzwpuyS8XySCvNRkUjx7GSY8V0oFvBIV3roKja87c9U5PNPG9bq/Mfg88HwjPebiNjxsAta8g5mOvB/idTz4Z2I84wA6PJx9MLxUdJo7vSpNvOOzGb1K9iW8q2mlO9O8b7rRtAm8XZecPA+RBD3JMgW93hzZPOXebrxpeRW90huNvHJE6Ly+2Iu8PMwFvRM6iLwytq44VhvPvFSvCTsVa6q76EBdPECd6zvF0aa7QfYgPE/Um7uEoLw7NnJaPCK7GLzaHDk6MT48PO+94bwUMhc9fvcJvckWF73rHEU8ExS7Ow9N3rwCLLK8R0tavL3mobxF7228SYGQvAPwW7z58ug89hKqPEGlOrwCYMk8+NiGvLU8CryDocQ7QmKiPF2BnDt2eOu89dnTvCh/sbxMTdW6S1n0vG+HrTzDhxg7SGHLvEvX6jxmwiU9C0vDO+nYLTxH5bM8u0RvPEOAsDsnHbW8OuvMPEMAt7zU4Tq8ujvhOwtlr7tG+247Wj2cPBFZpzwXgEg7A/oRvEOkjDsIBl48gfiQvCUPcTwACk87fjB8vAbQET3YAiM8bG/rPCBAlbzDCJG8FX34vCBLgjs+jV88GBPzu1I6QLxTDlg6psDUO6C3oDz07ey7nbRnPK1GFzsCNhM8KjFIvABPM7zh14E8hv5bvKfYBTzjEP48JJTEOvk7f7wtgbQ8IDCyO0sibLxKaAA9uZmBPB9mWrz9C7+8NCtAvEY+gLwji8I7jKIWPaz1BL2YlBO9BsG/vK1pyTutkpw72gDGvE3h0DpSbdE8yWQiPGhwwrq/r8e8wF6GvO9xvbwT0ge8dvijvHyLL72+sS28cU21O1Wbh7wka9E65xPavPSsgDtn6YU8fUCdvHPYYzwfF9067OX5PJ4aKD3Y3RI9THxUvD4elDuNNPc7at7KvINqAz17l8U7RMAMvOtG17sOXca7aTMbPOhWiTqi0Mi7SrOCvDqGjLy5rkY8kAKgPBNOATsBvDw8Z58IvIb4ejy0cbI8Z4ELPdoDn7y++SQ8DQt0uwdOz7x7VJW7oiMEvJZ9BL04K6g89pQDPLqzcDwoZTs8QO4tvIiCG7prGzg79lMdvdVTpryVSUq9DjeSPElKuTpGnwO8fFBkPEkNKLyGHRA9vJ0AvbWEo7sDUTQ9/wMgvQTZ0TyYAju9Teo+vO/67DqL0bk7FRYcPbgGMLvsnHu8EsX8PDcTVzo2izQ6IuyzPOsuvTxCOem8hQ/eu/eZN7x447e8fSyfuxIq3DxbUVY8z2L0O0yUHDzpDpY58Fv5OkZYTrxATgy8uiYVPDEwlrx73ua8f/OJOxYr+jsNkfG847DivBtg9bvHZ7Q7b0CwOwm2RjzYqDa82JmfvPl1mrum6LU8FiKWPFsSmDvg8Lo8QksivWomaDw+4qi8g9mhPPbulbycimO8n2Lwul2WvjzKrjU7IpCHPMrQuLzQUAk9vU88uzp1qrxxMhS6emLAvEFo3zs5cXY7EWpQvKJilrw0Toa8oVyBvELB0bwdH5U8aOsovWcajzxp0OA8osacPFeWoDwASs68rqtZPEzFczwGzpW8rkkgPdMBc73A5nq8tt1CvUNaors6hZi7UP4PvAngAD15Deq86QwnukBMpDvSmQy9hyOwPDoCmbusNDo8SxISPREp0DstmyC8pKpyPLs8cLxtKiG9c0lwOndJvLrJDY68WMsUPUBPA7yfYnE89SDau2/+qTxs/Sq6MD3QO+9ZKjwpbJ68g+oNPInxX7vCn307Z4RjPDJ2zrlzI828Y3QfPGo5lLvwbWc8CaLBPLP/nzoSkxG7BDMtu4Cu9Duaruw88CobvaqtpzzIhRW9MaU9PBpLbjq+ovu8JNrYvFu1azzcaKM84hKTO9vjDz1wFDe6Or4nvTCqITq1Agw8R7JROyEnS7uYQWo8CEk7O8NUzLvCyJY8XwwBPBTiz7wL3lm8Rd3EOtub3Dz4do28Q1DJvNErHjzlIQK9xIgAPGwqnzjFa6w8lPIfvJP73zucgiG9oXrjvAk1yrvjAzy5VdmJO/UgNLsoZOG8ZwUiPJQJ3js2Mwm87DLkvPqZFb2bgam7gENIu0bgAT0fUhw8HRZRuvsgujzNzQI9bNdlO+ehtrwJgH48YVd2PPxxYTzH/2C82FD5PKU6brtCWJm8MyZ2PLLs8rpHrKq8IR/kumJNSLrCCtQ8j4UFu4JILT3OlVu8YutKPdpMozx0yTY7NrQLPVMUIDiye+Y8AuggPGC4l7vYxOe82GoRu1o6D7xEiEO8j4JePCtpBzwh/au7s3VrOlXsoby/ZXU8orU/N2+3Dbt9Nm886DXSvP6/OzyyPq48TvPaPHcQ8zy5+wG9VooVvLdJBrx3xCS89gQnPeFcUjq3vfq8PvyxvEiTiDxjWqG8AOvePKwCPTyv0sm81/rSvDzmGDzSzQS9riCLPD5Bt7yHjhe98GuHvPzBzjz8bQ+8GKhJO1E0tzj68Iw8Q9mAPNYTrbq2+Zu7vem+PD53WrzshQO965dvurdH5Dz6X9o6DWXhvABW2js3SYS8RHEZvfAOazwAcAq9yAAPPTQDo7ypLmQ80EegvE5X9TwK05w87W1DPJoHhjxVzY46zt5AOZaqMTpWt3Y8SPlZt71eELt52sI8AMNNPKOgBT1rvfu4y2Gzu3yigDzyqmI8/2kzvFtvzzwM1iG8m+pivOaeDDzEF/A8/0/pvO9eOTv8/Xu8zmohvWDjnTuPofg8oHiMPE784zsB/Iy8rtmtPLzuQz1dMHK8AEuvuv7SkDzLJ/Q7dExnuwYJvDt52Ae8lCrCPG3OX7zCoI08D/1lPKKltTzB9RG5F1FnO0Ya3zwdIXm8TNWxuzMznTwu6Lc8d6VYvAI97rsMfrO8H/aCuyPbO7zpxby7D2UKPQsoVbuj5GQ7bUwBPVZRwDyB/56709HiO9BjBT0tLpq8MOGsvFMpRbzrWOw7K/XCu4jVMLwiWvK8mICgvOl8+TxWch+9wMQhO9Lt77vbPSe8zNArvM5mJTsBLGC8q9YJO5wpALyZgDo973BpO3+7y7xRUhO7LGOjPG6ugjz+1bM8pBaqO58/cj2hqRo9SuuTvLyzk7uG+oy7nHy2vCP0qLysFbe83JtPu1wE2zzZngu8LxFMvAXWV7vHL/u8ji3BvK+EVrwWUe88Q5HbPAXdD7ujukI8veQRO1GNVTs/0CG9hAM7vBvqZLxL2em6xBnEPIEgBj2qpNq8AKEMPUbfJLx0W0489XuNPKdCajvi8gQ9JASYu+eP9rwtBoA6cxJPO7mynDsBLhi9YycIPCZgqTw7CKO8an+gPO+vrbuf/a46kwbgvJHbpjyJTAw853uvvLO4ijyZeza8TLzxPPFjKzzmlIi8OQLSutlVq7vKVkO8XzOxu1OF87yM4U68oy+4PI8d4bwDl+y8utSEPDnZvzwmfLg73nDXPBZgRj2GaWC7BuCbOrFJFD2pVHI8LblbPOHgtTtoH8K8PqAwO0J4HD0VmSi9Vo7UO4jJpLxupSm8N9UUPGUKfbxmXNu87uifvNogobxnwEq8/5Z/O43/UTzeUB29dTPPPAT/ULyYItQ8/FQnvZBVY7w7F6M8i38/O0hDpzy5Q1I8aPKSOrmyiTypBT67J9lbO7A2RjwAFh68XL5svHXDJbyubs+54k4FPSL1KzxTPpO7alaIPOsiO7xaDbc8v0KIvA24xrzv5Da86lqzvKZ0nzz3ti28yAFTPBdaXrwykkG7VtmRuy/kSjt5Eho97CRavLUVGDwrU2E8QSlcu2+U5DwofRq8MyMdPN1517wNrom7gvwEvPijjrw218K8YA1NPC2Ws7vxMpc8ykmGO+hT2by+iA298PmUPI9ycLviEOu8BRMXPJv7/Tx6drw7vQgLPWhPNrwfWDu8Js3Gu+jn2Ty+Zqq8GZvrvBBVqbwRXRW7JoTmu89MGDyPqJ08+cyGuzaNi7vhJZa8b/qSO7ebg7x6R6274ek+PIP5HzofKZu82Vx0vDY2kbwjHBy9sMdPO22KmrzITcw8A7/kOxurKrzQZxK843eTvH0UOTxGDNc7Zh7SvCMWgLwXnlO8VCT6vPqR7rldgMo7nAmqPOjP7Tv68BE8gh2KPPiHI735yLE82M3AuiYE7DtBmrS8ItIgvZ3pgbytj8m5SIqpvC3GUD3geCI8rCfOO49Vt7vIIiK9FKeuvGJ7kTuGLQe9TxCFvIG5vjt5xbA5fzGyu/LbW7mpdhM9GG8zPOgQdbw37Eq7MEoDvFgYjzyul0c83Ya7vJ0g2Ly0x4O8XVy1vMZq9LtNYVA8Z1UAvbIG8DysLSQ8uouCvO/uHrvLO3470BojvCTfkjz/1A+9vVQqu5rMyrwzIKi8jlIcPUlT4zw12Jc7/PRcvNkXmbzFDQK9hAecvLwmEDzx+xO8S9ArPMYHtLz+Q5O7T5NnPGSRbj2NbPc8Lg21O9iWHDyVLRk9/DUzPWQ4gDyujtY8bTHKuJ2igrwlcis7Py4NPbBmoTuPj+K8864fPOpfP7yMcY88FBgFPLTVELzu+hW7rtwJPI13mDyzpC+8T4QQPPnBBLwDGse8pmMqvZ/tQD33CRc8se/vPF6QnLygNGy7S5qIvDlJtTxePF48CSRcPEHlZTt5FQE8IdrJu9pk+jymtuU7cc1IPLovnbzveLI5VKpxO/DLRDv0lAg9T2ycPJynwzzWXoq8de5ZvL9YzDzuPdG7ZDZ6vJkHv7q/3GG8JYEzvK8bIzug8Om7eA6YOdPmFzyM1QA9UR64OX3Tmrxhao+8ZSddPfJyOLxIlLo6asd1u2jWA71YVeM7zXQhvKjbdLwlgGG7OshaPMjnhrwuS0G8mVDRO5P0P7xVqWS8+c9XvMaHmbwkkBU7AWb1PKlcrzzbcQe7xL8rvV++Ubzgd82879c7PWsNsDvdOFy9tE+YvA/hlby8f7A8c7tnuxKdzLuqbpU7+ZvpuxENaLxMFow7AQzBO22ehTzWQFS9pthNu24FY7wlKGQ8J1uRO9TMvzvu57y8RTqbPGuJKb2twxo94JHfPNBGOzv0vwc74JYmvVx7oroZlIo8HFvbOXxZez3yeB09jwaRvLmXSby49yy8f5wxu3c1u7ueV4U7qChDvDY43zzT85g8mDDpO51o7LxNpc28X/+DvARlDDy4EHI8bAaLuh4N7LxCXfa76VHJO3ZjiDzY/Qa92xKOvGZe0rwROQO9EHHKO2RTMDznC/O8rCvNvMpuoTzaxMY8TWFLPAWhkTtloE680VCTvEm5jLy+F285DEkNOw/qibyimUO8jrWAvPENobzDUg29TuEVPfR1gTxUHd087afhO+qkgTuTBpG8J/pCO2rIjLtv6fo7H+eWPGgtmby3z/g87YSXvMYlLjodOIG8vlJWvMS9pzxs86e45YCiPOLdNbz4KEe86ogBPf2vZjywNry6fmSmvL5UyDvwt3a8X6XQPPui8brONFK7JhSIuwuHiTiQKGE8Layou04tMzyyopo8/zKyu0DbL7xVcR69FBaGPNWKo7zhFdy8zFMgvGOzj7wCXgK9YmYGvam7OTxD6Z28DBsCva7DAr1JTN+8M+ubvMVSizy0hVY8g0q3PJVyqzxI78O70JRtvf36GTyyqyy70PpNu2RB5bu5AoY87+DkPCpCmLyenPy7SYfmPB4VLbzySbq86gYAPCvh2LufDka8hQ7xvDjwJb2pSE08VIvLOxeh4Dz/Rky9dmKwPBfJkjygY0Y9sHoDu5JmuDpIRqY8ykqrO+nc0jqgZUO7eZCqvIuJR7t1j767cvOkO/yqlDsAf367cW0lvMHCgrtkHaC8G1chPdVQbLvyxcc7Zt3vO+NkYTvQOKq8gbvXPBHrBD1un9W6jHr4PJOXrjwdxxs8GeYYPTY/fbye8wE7tvs1PVZuQLyt0qs8MI8SvIezEb1FdFA84K3GvG4l0zoIgDe89SVjPBV00LuRV+a8/gVePKTiYzwemkQ87QY1vPyrjrzYt7i8z9YRu1MbmTzx4lq8kmHQuzlau7hTOmc8QuaIvKv0nzz4j1W8vnvLPFvdaLwoILq8xKGRuvmHf7ttgOs681/Vu3ls+LuioiM8N3Jau7ZCuDzAbI48x2G0PMNQUjtbvbW7uppBvekXbDwitDM86fzpPMzd4Ly+N7A6SPwOvF98h7yamYY8Nxzwu7m56TuhpKI8YDjTu7p0GDsT0DO9UeWaO7CmLbuX4qi8MXgpPGzk0Lw/5Wk8RSRjvBEsHrwD1OE8kQ0dvdx9Wzuo6SY7tugsPE4dfDtyRz06Q54ku04SuTzoPXg8JsTgPMA+qzwjM7c8NqcJvECcsztl2D680osOPe1VdbvRvHE9vR/lvHWkl7wx9Qk8eXsGvPULDT1qRJ68ZffBvPgPF720JGe8xtBKvEj4eTyP5g881a3SvHcKYjxaYjg8s2AmO1VmdbxDCpQ8/qb1O5gWaDxyth28EWDUvPWtFj2N18U8oyODOwg+rDwKSpM8uDIVPMWyHT1Dobi8UQrUO1b7qjsw/7a8vYtIu3NlGbwk/IC82wJbPKde9bqaeXw8z+/zPIKmHrxtFdK6rToaPBSFAb18tUE7xHgjPUUIVDq/a+e4qvP/u2bl17vv5Ea88/uMur7jYzsKwS48pm6TPNodgDyLawA9ry2du38sizozi2S8dPFJPIQ5p7z+3AW9k+XyPEAO6jwKDOY7fHeFvBtPl7zCGY+88uMFPSAMqDw00+u8X21YvIu7GLxZfMw8ZJ1rvPR/ALySmaM8kOYIvYXynbwxfD+7ap1FPO8n2jyLj846Mjz+vIcC3TzzGiE8hVpQPLX6/jsUR6O7tvQDOyKxfLwgkn28gd/yPDN0JrxoJsS7Zj+evLQ0QLy2KzA8ec1HO84MvDsxft+6Q3aOO+U/Yzz53bG7JsjTPE51hLyezSm87RYeOxtXAb3ZDfM7bWGDvBWrCrz4gq+8tpcJO0cCjzsL8me8rjAhOzUBLboEfVS93tqDPGKQ6zu8hvo7cWkDvKGm7DsHgmO8VOQsPK4//DxTCI87IKolu2VKcrxQC1e8gAG9u8hvSLxSMma7llhFPD3R/rqHzkC9cf05PQb3Eb3tAgO9cmjwPB24Trx3siA9nnrbPCQBCz3Q2UI8F4nXu9J57zsomNe7/NzcO07LvDtqoD+9bl6vOfUf3jwazD08iqvrPGgqUbylyE+6I3cNPfhrqjtJkn47eN6LOsNoQbsgPcs6erfxO8kSTLreHM87H7M/vGmSkrx1Jda8rQnEvOxouTzVaMg8lmQ+Ow8ILjwKOCg8O8yDu8p3jLzeBNS804ApvEk2pjtxIJA8lBlwvOd46bycHVY8TcZkvPp05TyeUvA8C/bhu6daGb1CTn47zSIpPN9bsLw+GwW9v8wBPAzFKjsiLsE897cevEC1NjzdtJW7qNuIuxPmPDzRtAS7wMAQPemqi7yygXQ88iE8PDFRMTxbHNO8K2FYO6sHFL3yvwc91aoevHlQujxLOxM81DG8u1PfqLtm/ri8LfccuyO+7byUkiE8hpjouv9U8DxBno48NDsTPOguADs9chc8+lJYO/BXnbxO2wI8JjyaO8Spwbyr3LW7w6c4POdZKz1Jpk26aVJlvE4WiLuqoIC8QpQEO0ZJtDwUVWm6NPWzvH3ujzwRGQg8snEyPY/fxLtXH8e80ehRvAsaz7tja6c7CjXPu4P4t7xEkba8LVoxvLUmvry6bgU9DjoKvK16yzuj2qS8j86huz2yizx9AgO8U7xgPGF8ET1rzHe8f5SQvCwrm7t4gPO87u3EvKICCLz5rkU7r/xRvL+CtrxTdM87fzjwPDRGqjzddYu8JAUFvcjpXzypNaA8AhwHvMVeQLx51P+8W7WCvLaZQzzt6CW7hCtwvNPxuTyBVou7n0CfvMc4RbwgJwQ92JO9vAx1lzvDo6Y8gLmvvNk6XrsZ2s87D8EHvJCJubujgP27MZ7Iu/zR1Lv1Qe68rhoPu2ytFDzSE4q8TZYivKY0EDyAbpu8gi7uOjkvdTvf7o284QPVupbxt7tTUAE8uxedPID3yjzv3yE8DnvFu+8tdbukWdY8MWZIPA==
        index: 6
        object: embedding
      - embedding: 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
        index: 7
        object: embedding
      - embedding: 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
        index: 8
        object: embedding
      - embedding: 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
        index: 9
        object: embedding
      - embedding: 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
        index: 10
        object: embedding
      - embedding: 602NuWggczwj8CI9+LakPF2HgboWJaA9G0xaPQ6IgTyEUU48lJ3Su0jOUz3JKTA9jfTkOuHTL71XhyG9EqKZvV1UFD3Vk5I7E8KGO0dHjLrShuW7u3MWPdl8dDzR9PI8FUFuO2j+wLwHIK68qxVXOvgNdjzLpyo7xHk7PAdpxbxc5n88gAoyPBZLMDovXYu8gCM9vEHx2LqC6a47er0JvSOXCbyk7x69j+qsPNzRiTxBIKI8igEmvPdhPDtoTw29h1ZJvJd6XLzXfcA7dk6PPNZRg71CFZ+8dBw6PV9MgrxREvY8T/lxu2hUgrxBJnQ5E7EqPPRhG7xhzoY7VZ8zu0lbwbsT3fe8qSlGPDeQjrxEhzA8BS5ZvGmHGrs05xC97mODu8LTjboT9Rs9nC+rvE0bjLzYHzY6uvHlu0xOKjz/h6q8YThYO1j2bLwS8rc8FoW1PP+CYLzJRAE9PUuOO4Uw0rwHDNC7vTrAPO0poDxKbUO7z/6MPBYL7LuKDi08rCVCvM1vPLwTgsK7rEYuO3OiWryCv/S80hU1PQdAh7yrsgs9iEN1vPbbm7seJpC7NTkLuwobAjsomJC6n5KePMA/JLxVeRU9EFVlPD6l5zvsOvs87R3xPPKWFjwhTS885nmWvOd+nzxsX4W8ApOmO3xf5DzX+V69Wt+cvAdKj7wgCLs8SY4Fu1ld2jxIOAO9YRc1PGAMmbx9wTK9i2aSPAFdkLsw9eK7CkT0vLqlozz0KX28As1vu/eH4jrY/P06prjIvOeGJL2EQ/A69ldwOwzhkbvH4Oe6DXNxPKSr0bw6OfM7LKfFPADovTsp5JU8hgE0vOR1pTwb6ok8B/S3PMesdbt/1Y07RHqXvJKbnjz5bbI77nGqPAAXlLyqcig8QjyYOzDVY7wLjJM8BEYFu12zNryt/ie8rOLVvOZtDLtWV+a8/yYqvBuGiLwbXE48Fnx5OkElSD1Ce/E8CzE7PDQpyTyq9Fm86Rbnu9d9WrwDijE8ab6PO8kBZTui+2G8OY5jvE9f6Dx6/0M6jy0tvIx9O7ziez08jZ7hPCyL7jxqJGM5b4OuOjiII7w8Zhi8HaDFvK2fIDrnrwa71xWCuzXgCLsBqd+7+Qy5PKtUoLuWE847ZhFcPIEc9jrADJo8E0qevEaJ0LuN7KM8GrOFvNL+LjtzLwa8yuaEvPxlQ7sDN5a8X55yuz7DRjwPKXy8fWUWO6uzp7ySg748uH0QPbYOA7tyRhE8pMZBPG5eTrzcw5o5oQ4MPMGS7Dwh6Di9jxNsu75mzbyG7pu8N9EeOmyroLznCba8MT0uPOSfBr2Y8Ls7t3DjvCNDgrzlJWQ8cbKZPNtcrrwoePe8VM7JOz9VnrxMG4W9NnSAvDYxAzxTrzo7Fqv9vKauDrycXyi8l70WvPHLDT1x4o480UJNvQJkOTyMUCW8JwUgPY6MUrxeIL48fnoSPJ5h8jwPoba82dVIvFfYADvkIlo8YDheO9+m/rtn0gc8HY6vvBsxdTusgm68yqNguzoKJT0Ewqq8MDjQvHmH+roMNkM8lJEFPf34oLzoMzQ7PHmSvP/KBD20xDc8/c59PMHKbTr19c+7B4aCuyEJHbroGkg89SMkPU90ArwWQdo8l1FWO1HEurt10oQ8TFFlvNvq1LqzkHg82FBrOx+G0LvTHas8tE8nvFt8kziFxjQ8BPYEvA0E+7vZiTs7VxclvcuSS7yN6ZO8sCpsOSMnKTuBIJU8mrCMPI8uFjz11QA8Z0ngu4bIqTylgXK9Eh7Qu5RQlTuIUT68bJc6vGfGwjxBPHU663fBOq08abxditk8YpxGO06g9bwGz5G8Bz9hPNQp0zvfQZY8AwddO1ngprsVaAe7SdrhvESw37lHkjW8riszPKLvsTsNJ1s8UXytuuXykjwyLvy8vxNWvAHEKLwJj8S7ou9LPEuwLL1yB7a8dHLBuxPxjzxRO5o8PLbzvJdELLyj3y67PfMcPbI1Hr3B6yG9QSUbObtbGD3PcuO7RBp3vJx54Dzd4448m8wZPdSbobwk8MY7YWTAvHeDdDpAxnc7pYddvItfH7wKAAq8DlOSPO02CzxaUs67pEiiu+ZV6bwrSb88MZMSvARzgDteH6A9lUzGvNP817yDSKu8zSwqvUexuLw54/88ycjFvNcIlrwJPw88T4x3u9+fIrrhXaM8W58ivHo08zvn6KW7CblKvZgheLwYylI8A6RKvIkkCrwChRi8UCAnvamvPbw2+BE9qHAZvH4ipLyrrNo88xLDPGwb7jyDaGi8xVx9vR5ZtDqwqb88HZyBPCmkjTwnbuo6R7D5unUVibwBiCW8xqWsOheM8rpJOC27M/WIuw4VLbvNXqo8VVjbu2ceSzs5GPA6uFIPPHBlszuL06W8LOmmO9lcZbyHClq7Buu+u4ARIrzZkJI7c9NmvOsCzztkKci8azoMO2fgib28kyQ9qUp7PMjRC70JVFu8aAMHvMQ5ibshE9G8SIHfvJSfljzj9ai7nAqcu2U61jzI7e288SSSvNwKADz3yw69bqwQPGn5sDt6NQQ8BDgKvMyBkLsAN8c89VUBPCtAXTyJ4Mg8JvqdPL3Anzsinsa8IiuZvNTSozyHPD+8M4tNvURw/zr8sge8smD5PBhEGz1+Zb66GEISPC+8LjyV2KK8R2IAvIFQ2rumBbS782H7O2BC0TuOpKU8sbqhvBzhzDv2wos88yVkPLlUgDziiR88CLKCvGgflzyfkYo8ES+EO7pW+rtlSZI7N+iVOZKHBr3f/Bo7wlYHvK4Fcrx2ggs8NIsWPI6SwztVHpS8hvwZvNNMPbwYfwq6sf8VvFQNkTzZIA+8iqoevEMLJzwlyx+7MjMAPPglUjxCWw8712DiO6N9d7xwY5+8OnMivAidyjywc0i7e5DoO5+PizytQEK9EHUPPdywmDxl9f46hoLNvEn/iryY9AI5LKgyPGIBmTtQlA09ndp/PHBs1zoJJiO9yJDZPPDkyTzv/hA8h4cHPUm5ITyjRHk8FFVwPDwJC70LiHa8mpoDvO1sT7s3vVg8fkM1vEC2Ej0KxBk9csO1vCfTDrxbxd674RL4O5TIYzzgv4Y8Rkjkuh64grwGpjO8mBQdvM4bErx9BnY77cUNPLe5QDxSnA68ef2KvFB6VjyCKf28yHBTuzA6GbyD4qS7NXXavCVhKb3GpHA8pO8DPQQrZLxOAxW8GWS7PGMQQ7xIII+86NPhPIkVvDw1ULI8NtqGPFMwbjtL5Vo62uoTvKo7mTr3MYm8/uKavEJp6rw0uc+7KDahvK3hpLsefya8VyfLPHOUI71zLWg7flw4POT8wbwqCSa8qdy5vB3E57xXess7FV3HOawfuDp/xM87N7Tquzod8LxtxxO85kgsvcUe0Dx3UU07F00FPE2VtzwkGKa7LwxFPMqIirz3gvU8YX7Hu6t86rwitOq84CMfOrEO0rpPG107GsVgPJeHpzvQzxg97sJbvMNHkLx4NQI7bOCJvC9ecDzCJkE8jFnvuvVrJb10lIy7Oi6aPCsvqLxh8gq8lXd0vJ5akjxSfYs8vNGxvB3DLrzePjq8V3zRPCMua7sgOKe7lhJquimtoLtJSHa8QF4dPPx0nLwEthe7QjuQPHIPuzxx+zS9zraXOz8hoLy3O/+83phOPHInKztKNxw9noJfPBFCVbwhMxg8XDEAPTDPFbz0KaA7vuATvBc1G71C3Bm9xzHzvPh1x7pR/YK8rp1kO5NxM730ft47+QbFO6aNFTyl3N68HTjuO4ms0TsRZOu7OaYKvQcWdbwCs/c8wFnVvAuVx7zkQsG8G40ZPJvvjDycOuw7C4E2vDhT8DwJdzW8C/6KPGMp0zsgzoY89oCLvFxrmDxR5GW7a59NPeYgBjsaMEu8W5tMvIQzaDwMyT28/S2xu/r+brvUHBW8zvZKvf6VArpqAfA8sCTgvPMtSLttjvI8ImjFPOfyAz3U4zG9l3U0vPS8ID31uR68AYb+PG787bwqqQI9Sb+Nuvfv+rwTWLs79pxCvB/tHDz4T5w7ESHsPLkVyLyaBWk8czQSvUtGAjylIs68FDjwumTgDD2IyKA6HryAvL2PzTsPUBO883OIu+QfkjzDFD476x40PZHFw7pFVuG85xWKvPUDFz0/X668KwtUvDovPLtXFaU7ZwhgvF1VCjunbnI7fs7nvJjmq7yP/uo89E5kO68urTvnQmg8dKktO8igEDxX9Ww8wBXJvODZjjy/sz+8NoA0vEwcV7vVoqw80a2dvEawFzyeFg89FwRDuZkNWryBDco8IZeNPNC/mLxMT8M8QqpvvB4WSbw56Jm7p4d6PDMQ5ryz3ba8u78DPYG34Tznlt87kUIeu2NNgLz9dl48fqEGvLyfdTkrNwC8zVNpPGHEhTxiLCw9zraaPJ1sDD16Tfs8GzPlPH1fXbu2lg09orfLO1FvPbyRst48jxEGvcaTjDtfvQW9doK2vHr6fbyakjQ9Io6aO1ssRDw6juq7TA/TO1UFtrqjcxs8ERiUvAhMpjzfJ4U9BIuAOs7vaLx5cus7ECz8O3YOQD0cPLO8WkIAPW7tKLzJCak81MAevJORPjx6PTK9TWKKPGgYbzw7CuS7sZTeuxsnnLsC+c689WjnPAElRjzv+QQ9NKEAu597GT3ibRw6gx3mvAUQAD2H0Nm8a4SbvIAp1zxfA9U60Y/hu/Ed2rv4Aw080wpzPGDfGrzlzcG6MTiwvPTNULr2Kke8jbErPLkFtDvIOfc6VrtgOiD/rzsyRQG8lzjyvCuNRjwLN/q8wn5EO/R17zwRdYq8O1kNPIzXOz2myQ27Ol8avUMpWLyAJYE8KhIPvewFLb2Eezy7BRskPO9rAD3Y4CC9LmahvBFemLzJMv88HNhcOwT6dzw9B7k89DBEPBbOVrwLNci8PVyqu/pQWrxWsGi8gcSOvBok27wORDG8BGdrO7lXuTz06FW8EvIOvEh7gjyaQJg7koggvOFF3zv97BI9wFQEukiloTsafNE8vsmQPDq65zzrgtQ625mvPK8Y3Twitm87ZpYZPHet5Lyig7462NhEvDQPHb3pMCC9R52iuU83E737Pgi8DB7EPJpctLwwho08CPJBPLtgpzwHC7S7KRi4PEYXHTtRWHk83vw9PBnoz7xBw3E7DtXBu8NhRDv8OV28kkOkuzFGa7yd/Ak7Y0aCvDT5srzyhQc8VQ3aOsXyizyY4XO86JquvAU+erzVCfK8vY08O341Gb18faA7JFN2PHQ/jjxSCt67GbBevAS2EDwgv4A88GOPPKgP7zsPh6U8Kc2PPKGNIDvQypq7KchRvFAkJb3JoQw9S84tPGhqlbxDMog8R5Y2vf3le7uGW8K7GiFnPC/0KbzsFd68MBCTvDWZgTx8kPY7fbG5vMDJF702lKk7SgvpPPa2RbyL6BA9DWoyvIwoHbydUAI8l+ktPM1Xhjs30BS7HP9lub+R1TsLQFM8Vt6UPKsc0bqJVb88WOMVvWMp9bswORI9NE6zvLVbsjzmJgA8y43NPFWtL7w4zQW8MeEnO+3wnDzm/6a8WybRuzTb8jxohZY8xmZdvCPO5DnjMyS8vg+xu12U2Tpr9rA8/e1FPahlBDuf5N68Dzvnu+zGMDyLctI86TtXPKN2C7z8zoe7CdHnvDBC3LyKA7S8cCLuu9ZKQTxccBa8niK9O0uKljxpphu9HaAqPSioU7zC0b+8DWmJvDm4Ab3hczK8h+17vNiuibyIwhW8G3ehvAEAxzsq14O8wIiWuqtxnjzf/V88+hOEPMUuHbzejEU7le2lPCAnubt+JAi65cWSPMkEcLyLXfA8xRvQvIKl37yKLas8x4wkvJSAirwnkGC8ZB/eOxWVPLwSI6y7f8j0vNtu6bs8Ys88r4OlPPH+IrxxlMk8XSjTvJ89Fjoa0iw73JoAPK/uGDyZkPy8Vhk+vFZ0B72plNm7cjMBvbhMkLoIQZQ8KzTCvPOkqzwW1QA9g8GmOV/iiTw1x0U8KsErPDVUczxb6Zq80y6aPC/Fprzue9y81XukOz/Pe7vMYrG6VBgROzmwSTx19/m746MsvDKpgrnY/bM8yWC0vAnMpDteutA76aDBvIOzzjy3CYI6foWcPPVdkby06cO7dcU/vONA5ToPULQ8RIaLvBJrq7v97tY7BD97OtLB3DudEiW6Q6+qPJHa4DtwvB+8cAbgvIurZDyI0zA8hD2NvLSjjLsS6tI8ZZrqudToxLyp8wI9Uf+EuwJ6q7wTuQg9QwfJO5rYAL2UAiO92UnEvOQvi7v5/si76XQzPa9fAL3h58m8sfwkvLeO2jqz1Jc7NjrWvN61TDyUUBw92DOAu1b8A7x9lje88ft/OmIZ0rxZdZW7fCutvPe3CL0xqq86JA6GPFbVirzXJ7S7dVLhvKMABTxniLU82c86vNrimDy/BmG8socWPSy6Cj2MxOw8M2ytvAvCgDzeSoI7tEZDvArjjjy/gZS6DqzOu7/3JLwxFgu7Hp9pO/82gTzPGYe6/Pr9vM6ymLxFzvA7idehPHBbEDwZUj280ht7vHtcqDsFPss88SkePfBaH7v8zC086b2UOyqzpbwEFx68/jegO/I3obuhfoI8xslnOwoj0jsLn7Q8aYKJvHXMT7vmB/+6WkMlvedy+bwnchK9/OPXPBhVcTuC5fA7uoa5OyL58LpNtxQ9ZwvsvGLqHjmQajM9cRSwvHuMpjwWATe9e5a7vI94prsbqoU8ZYwoPXQVOrsU5CK8bOH8PAqupjy1jm68GLe3PLBgBD0i1Ri8Wqjmuz0wOrxEehy9WLodvM2fmTyyFv88+NCLOx7C0Lkd1VQ8WbIYvBSnt7sng2y8LaQrO0fWObwHdMK8AaV1u+6ymzxKjPS8kX6rvHp8K7wkpFM7SobUO3MqOTxQfrg6LfGGOixeHbvd0gM9IKMFOhyEPDvABkQ8D3TyvCin/zyNFLm70u69PMtPj7wBYDC8F/0KPDrtAT3Zeo87FDebPLZdrbz0kcw8q+nivD/I3rxhEBQ76rbsvK4s4bvrdhe7geC4vOvxcrxecZu8Rrs+vC+QAbx834s8a2QEvXcIfDzxVDk9UJr1uhfoRDydy/O8kO+QPH64HT3O+Js7nKRCPR2hVr1rtVO82c48vTur67vy08m75TyAu25b0TxTnr68VMt6vKj3Hzw8Tm28DSLQPLZEUbvKY0M8ArYRPQF+CjyGSf6770O2PBH52ryQ5g69bW3MO10HZzyuClC8Q6QUPeXdgrxvP148U1AdvIM0Dj2EQR88N+yUvElqGzwWF0a7hhr8OoEOF7zYb2M81BO4PGyl8rvvWte8iPJoOwT9k7lwaqo84D+LPHY/+jv3Laa7N9u4OzHs9DwdDwQ9/KMKvXN/lDxE0OO8UTX7PAnosbxMSga9b5LSvJ3KIzxCY8w8b7m7O+qTEj1RbYi7XmsbvQCigjy3AsM6aYmvu5VJP7wmMDI85J6TPMRaJLzuo+g8hGsxPLCZkrxQlom8VMa5ul+Gvzz15gS8qKLEvNBdLDzQjQq9cRrNO7czH7xY1so8vbRRvKKF3bufry29ZQAMvaTAvrstbXW7mnMHu7+zgDvTOhe9Na0oPHUijjz18968rBjovL029rwCr2K8V/MBPOLc2jzkmLg8pg2eO/79ITyZ9A893CtfvPFVnrxCvuc843ecPAApRjx+9su8Q0yhPAL7OzvEeoy8prGEu4bcQDuHJ0+8nGVSPGoBoLqmIdI8y79cPL2O+jz46Fm81t40PRXBzDu9rAE8U38hPeHOu7zGeRM9ylyPPM74zjt0fM+8zstDt30Ycbx047C8wxI5POJrvDvHYMO7nf9GvPyuhLxYni48JnFKPMH32TqaFrY7DPTfvNeGNTz/D348NjB0POREwzyUBwK97Ec5PL4iPLxgJoq8pBosPZPaPDyhqha8MKMUvPdrGjzFxcO8UlvBPPuIUTzup5+7/6fVvMVVrLsa/jG9sv2mPB9UbLzu3hC9IFsGvLnFEj2rM6K8ZkQcPJtLYzzKqWE8lAc5PMrrPDykV6s79sDqPJitzzsjNt+8sPdFOAXM+jwuec27aDSzvB8gDbwXeKW8ptogvQbrJjyKW5m8GPwIPYfbOLySuzo8sCd9vFPMHT2r+Xk8LUBCPBmQ7Drj+wg8HKCDPFNM2buT8LK7I1EnO5zekTy+pqY8Io+NPA3twTyQ/vA606STu06D2Tslxvg7X5uHvIi91Dw76bA7m+wUvEzvtzsP/no8YgP8vIvlMTzcg8S8vHcVvQFZODvom9I8DcqcPIhxCzz5WAu8H74GPOvkSz1rxgy8DcSlOwHyhDwspmU8QMIOvO/QiTvWBJ+7HfShPCwZ1Lsz21A8zhymu4Ao0Dxp6Bk8nMGzuJsX+DxfMGm8dIgAOlnvozx6FfQ8qOv1u5ary7gErQW9oa8quxlkVbxopHs7c34EPZSpwbsst687rc84POPKCz0RTjo8nYPzu23fAz07smw6VebEuz/SMryqP0Y8TL0LPJ6svbyj0xS9Vqxnu/EhET2u2kK909DGPHj1Vruf8TO8/qSbu9bhDLzvDrK6PtU9vCftFLxTPg89DlVFvE9YCr3N7hA7kDiUPKMMDTzUnHM8Np28O6PJgj1dS8I8pxWvvEfN6ju35VQ7g6LeuwsmmbyiGjW8nakjPJRV+zy14t673JF4vK7YzrvoltG8I/ysvF0YT7yXx7Q8YFMFPckt3btzHzY8XL/euhFeLDw+8Dq9O1rtvM2qGLtqY8U7kWyvPE/NxjwHSvy8iHv/PEl9GDtogxA8sWuYPAPI6Dvv7Nw8nRWnvKy2jbtZ4x88v4etO0kCyDuN+ia9qumRukKxRDy1Uce8Kl+9O1JJV7yn+nS8HtSVvCLq6DzU3sm75ZTNvCk6NDz9j328bClHPPP4JTyjFji8gw51u/I7O7yZPEW8SG4LPL52DL2xODG8EZ5ZPLr5Fb278qC89CW0POqAlzzu5Es7zMPyPFJjNz0GuJ077JvfuW8k6Dw1+kY8y1smPJpYKDpF8As6vAI9PIwRCT1OF6+8l/z/O8pwzbwwvDu8TgORPMl737yRtua8W016O5oAgby794y76aasO15onzwtbwK9XtKGPEzGBru4a6Q8JVFLvWjIgzz4tCs870/7O2XkUTzwrgg8TASEvBoBPTwZ4/G7N7csPHeeCDyrk+O7EwPkvEptRjwwn+c7WDEgPfg5gzuKx+o7/H36Oy4rJLyID5I8VOGZvNrXCb1DiQM7P/UuupM+uzyveLG7sUs4PLfEtbyCnx25ijNJvGwLCTznCzg96++BvJv4oDwiWZ883sz2uxjEJDxgnr67Z1jdusRWfLwvfRg8oOm/OQ+j2LtoqIO80RQZPP0scbvN5g07k0vQOg3mB73m6M+8R7OoO8+LH7y3JvK8XMPbO3oE9DxLkuO710kmPVd6mryHgIW8ZuEGvGZQ6zw86fi8Hb8OvbJFpzpvkve7toAVvLNSlTvTdUg8S+CeOzG7TztpUJW8Pwqwu7tkp7zEFNm8pnbLOwU0oruHhLu8kp3MvKmvlbygque8C6Qxu+Nmp7voPNY8yxtQO/FFYrs8eJ27Mj7KvEjYqzyZ5QO6jzawvHHrh7ytxNm7Vur2vFbjhLlZJD87283HPB0mQTzXC7k60hGnPAp/Eb0ZKmo8TUajvFD2CjxjPVO8VkQUvcq/vLyd6Ys8V730vIDcMz0xPwk8PpiRu7x8RLxTutG8z6vvvFSyRzz7ag69eXDXvNxJmzwj0MO6nV04vPtpqLsZyhg9FDvsOo0hSbz0DsO6g3wyuwKXiDwz7zQ8sw/FvEHWy7wtIoS8qNoLvAgpcTzKk6I8bGwEva9S+zza2a87jpYnvE3xNjxZl5i7b7/QutHO3zv208e8IHChO9I+qLwCkwC8thEFPbm0szykW547f4TMurDw2rwqDMK8Zz0SvTCfWjzyzQC8OJgBuuxtyLwalv67kNK5PDPmYD3Twtk7alWnOww6RrktQ7w80usVPZnvGD2cEeg8PsuYO2cUW7wgVzE7rFDpPAMhNjzMcAS9QUoFPCkYTDpEn6c84pP8O3toAbyUgY+7z/FTPPaHkjxdBD47oQODPKbeZbx3A/W8jLtPvUowOj2o/tQ6gJ0PPUWtMrwKdmA7KZmUu3D/Yjsebgw7EOghO/S6gjn4nSo7SOQ/vGJD6jxxqmK7DqKoPDUUyLxwmG28kb7Ju0f2CLsbfTo9E/91POi4+Dzmu+S6n1l0u+o4EDyEzIS7oiPPux858jlVeMi8aEyEvG+hBLtKExo8BRXtOwMTW7xVB+U8hLhTvIx86by8LkS8HMQrPdSGpbxoFci73dZIO1FWE72tK9E6c8mAvKxo+bvqar+7wUsAvHx/37shvOS7T5+tPM1gmbzJTCm7ddmPvIY06LxzrxW8tO6nPEXFBDy79IO7rJ4tvaGykrwfmxe9N2YJPcZFKjuRCFi9FQi6vAwSfrxwG9Q8cuS4u+ivmrtidJe8KRAHvDo9vrwnIpI723qNPJR8VzyuCla994w3u7RMsry5Xs871AvsOy5/GTwRXpm89n2IPDvi0ry4tsI8TIPhPLunbju09hg6qcEFvYNr97qb7dg8c7mYO1xeUj3cpQo9GuduvNvBMLwm8428n36jvMZeCrsUnYy6DyoNPOlU2zxVcsQ7hQPlO6CY5ryhdsS8WniwvCawg7zioBM87ESHvANZ8bycUZK7rJi+O7G+qDwks+28rui4Oh174LwfU128NTWcPH3EJjz9TPC8SnuFvCCDOTzb+kE8Ci46PPrcgLp5uW28XpL7vGqjZrz9YqQ7IVY+PA9rKbwka+y8VP7xvCKCnrx8NPe8YoAMPV+HtTxJJQQ9/Gdzu4KPYDynIqS8HfCzu0xkEDyknNe7jL5uO/xPlrtgt+089PCWvGkDvrqtywC9VCLbvBCF0zyDTZ06/BKcPH9mc7w6u768IoIwPXwOgzw58Gm82bHau9iiQjqscLC8WuahO6qhqDuhp607P/uWvL4ia7zJVAo81vBPO27X1bsU/8o7mdb4u9NYADwqdyS9MyTLOz/IjbwHhAS9+BiuvNrbbLyJJ4S8Z8DMvDfMDrsmBEq80TYyveStOb0UktO8hCPGvEqdsbsPbCU8E8/uPKQmHDyenTs8liUXvYUAnjy76De830N2vIa8mLr1HqA8/oGNPL76Ar0zxge8+gwkPOH36Tu+cRq8dJp5PA/IlLzN3MC8FyCKvJGwxryYzvw7iMK6O0rY8jyMvE+9RzDOPK0irDzAJnA8hYAdPFkbiDuI75M8y5InunaSjrlhDbW7rIkwOzLs5LtAOBM7SfAJOynHyDsXYTo77lT+u8UOpLv2yyO95DlPPXA8J7vSaZI8iOcePG2exju17i294lVsPO8SMzxIBt47fIc8PF2zzDw1V4g8HkxCPb0O/LzGwAw8RM4YPaXklLx9Qwk9z6ntuostK70Uujo85c6VvFloUbtkuoe8nE5WPGkByLziEAS9R9gTPFI5YTx5QJI8aW+UvNYlmLwONcq8uYq1uz8anjz4DXW8ogIhvEGvODpY2lI8v5pLvO0MuDw3Cyy8fdueO/Q7PbziV7+83CiXOq47+LvgJIE8ptgKvBNZqLw8Iz87skVfvK55vjyZjZk8bZwsPOnthTrTIcw8/skwveyT8zv+4M07dZkcPaLcJr2DoNw6SeEXvNkfn7uzZ9k8LietvDHJnTskfJ08CFibvNuEHzzKMNi8WJU2PLFr1LsJaWi8nv59Ovk9/btQNCQ7YGi3vMOp9DpdQa08aEULvVp03Ls9eF48JpI9O6cd2brOUGG5qA1Qu9sLCD10GiU8jVTMPAw33TzNY/48ABZ4vIyUtzuyVLy8VBPrPHdEuDs423I954RavIJ7Cr2MyJ88yvxzueSu8jxcCJy7nPG3vFK8Ib3xLQS9+f3Hu/HUKDwIrzS8TBmUvN29oDxPHkY7ooafu3b6iry1/ZA8ptQUPOfgaTzWBjy8xiACvSncCT3mcfQ8wVmUPGvZrDwJtRc8gTPYOx9g1jxaZau8MVpqPBZHqDzTK4O8cGGzOhcFqLydQqO7nbVPPFDNnDuKPB87k1YQPXrVSrx4wEk7trgSOyYnw7w9Vqo61739PBW4MbykJKY6oKxMu9s7E7tu/MK7mF2DvEfxmLtsopc8N8YtPEWgmDw+as4818STurbsGrrWfkK8qFqiOpVxObxenpy8Ce93PIR9Mj2Kess7PkG4vFcy07zd3GK8c+DuPKeVBD346L+8gXXHu1jqGbzvWDw99rU4u5RrZrwa8Hc8QCcPvWpEqrup1kK8XnGrPF++1Dxh57E7CjrRvJOjgjsmCbo7eeKPPIykzjuVnsY6otI9u1WJzrmIZyq87qTNPE5sYLxjSfC87f9QvCXixrhkH4Q8JNQAvHGNHjyy5hO8Fi26vB+dFD0DzLy76mnGPMKEkruGkZ28XeMjvHrL3byQ9gK8/lK9vAMe8rx4mn+8Fu0JvEMqLLzOTmO8Zlj6O1VaVroyASq9M/SJPDcboTvdSve7vHHdu7WPEztp8Ii8n6yqPEfXuTy+BRI7EcEbu05qk7yb8bG8T77cOyXMmbw7pZK74IkbPcVM+DucRf68k1U3PV1PDL3Y2aS8bw39PA5UebxBPR09pvm4PJO9Cj2sV6k8LLkHvJJ1jDzHas+7awMVO4EUVzxzV/287GHOO4Q/0jzxeFU8j1XKPPil57qQE/U7eV6uPCznfzzrlaY7gCfnusNkFbqiHRg86tN1ulR/t7urWSe8K73aOqJPqLwJgN28Xl3ivBUXcjzgSqA8sexWu/VQlTyq2c470wG1vE47EjqIFZm8boEeOrPDbzzLV9o8tknJu1AXt7zWLqA8102CvNplnzwOuSA9/IhGvKhdBb04d0s8uhZ6PIV19LtpF6C80UBGPCpx4zsJafM8b5xRvAXRfDzk55K8PgHhuWp/gTyT1/w7kDElPQogvryme5a7B3wxPEDS7jvra428cfUyPIZhFb1BQg09CuM4vH8EvDxS4tw7rma+Og/5BTxpHpG8K4OVu9+Mu7wTwJQ746hgvJfv4TwZS2S7jarWus1OEjxwgKo7+JEfPL0knrwY4ls7NQhkPJ4XJLyXcBa8BcorPFw82zwmHkE8sq+jvIoRy7qm7JG84YK2uX98sTyYINC7QyhwvLG85Tx7TMQ8R9KHPSKacLwkMOK8ztTcvG8rE7xkowM5fTiMukhmhryB79y8xeCKvPIasbxhvN48UpAcPPdfWLs1FVq8Ymp4vHcYojz99iG8mvesPPf21DzYbUK8ZsEhvKzrT7xwZ+S8sYlOvLsULbsW0lY7eAlmvKwu7LyENOc7Z9K1PIvfiTz/s7a8rmICvbK8iTwogE88K8jCvK9UGbgHEnK8N5h9vPdqMDuXBI841+7nuzXwFTyXYRq8Ukt6vLifyLtYyBI9N2S1vLWqIjzb6vc8wxCpvHwj0TrfZe88nJqbuwZBMLtaiyi8l8j2u0eDDzv3l+a8qlGAvKY9hzxGt5m8rZ3xu49ktToGGpy8BkIDvLwHmjw2pGG808hnPPsjlrxaseo8xic/PCZ7gzyci208XwxPvJ0NirwJhOo8F4GKPA==
        index: 11
        object: embedding
      - embedding: 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
        index: 12
        object: embedding
      - embedding: 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
        index: 13
        object: embedding
      - embedding: 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
        index: 14
        object: embedding
      model: qwen3-embedding:4b
      object: list
      usage:
        prompt_tokens: 3775
        total_tokens: 3775
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '8789'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a focused execution agent. Complete the following task using the skills and instructions provided.

          ## Task

          Search for content about document element types or labels. What are all the different document element types mentioned? List them all.

          ## Skill instructions

          # Analysis

          You answer questions over a document knowledge base. Most questions are answered directly with `search → cite → answer`. Reach for `execute_code` when a question requires computation, aggregation, or structural traversal that a single search cannot deliver.

          ## Tools

          ### execute_code
          Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.

          Inside the code, these functions are available (use `await`):
          - `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
          - `await list_documents()` → list of dicts with keys: id, title, uri, created_at

          Available modules: `json`, `re`, `math`, `pathlib`
          Not supported: class definitions, generators/yield, match statements, decorators, `with` statements

          ### search
          Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.

          ### cite
          Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the `chunk_id` values from search results (from either the `search` tool or `await search(...)` inside `execute_code`) that support each claim. Every answer that uses search results must be backed by `cite`.

          Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.

          ## Document Filesystem (inside execute_code)

          All documents are mounted as a virtual filesystem at `/documents/`:

          ```
          /documents/{document_id}/
              metadata.json    # {"id", "title", "uri", "created_at"}
              content.txt      # Full document text
              items.jsonl      # Structured items (one JSON object per line)
              toc.json         # Section tree derived from heading_level
          ```

          `{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids, or read `metadata.json` from each directory and match against `uri` / `title`.

          ### Reading files
          Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).

          ```python
          from pathlib import Path
          import json

          # Discover documents
          for doc_dir in Path('/documents').iterdir():
              meta = json.loads((doc_dir / 'metadata.json').read_text())
              print(meta['title'])

          # Read full text
          content = Path(f'/documents/{doc_id}/content.txt').read_text()

          # Read and parse items
          for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
              item = json.loads(line)
              if item['label'] == 'table':
                  print(item['text'][:200])
          ```

          ### metadata.json
          Document metadata: `id`, `title`, `uri`, `created_at`.

          ### content.txt
          Full text content. Use for regex or keyword search across a whole document.

          ### items.jsonl
          Structured document items. Each line is a JSON object with:
          - `position`: sequential position in the document
          - `self_ref`: item reference (e.g. "#/texts/5", "#/tables/0")
          - `label`: item type — "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote"
          - `text`: rendered content (tables are markdown with `|` columns)
          - `page_numbers`: list of page numbers where the item appears
          - `heading_level`: H-level (1–6) for `section_header` items, `0` otherwise. PDFs often collapse to `1` for every header.
          - `tree_depth`: DOM nesting depth — varies meaningfully on HTML, near-uniform on PDFs.

          ### toc.json
          Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, position, page_numbers, item_range: [start, end_exclusive], children}`. Slice `items.jsonl` by `item_range` to read a section's contents. PDFs typically produce a flat sibling list; HTML/markdown produce a real tree. `tree: []` for docs with no headers.

          ### Cross-referencing search results with items
          Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in items.jsonl. Resolve each ref to a `position`, then walk `toc.json` to find the deepest node whose `item_range` contains it — that's the section the hit lives in.

          ## Strategy

          1. Search first.
          2. If the top results contain the answer, call `cite` with the supporting chunk_ids and write a concise answer.
          3. Reach for `execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
          4. Call `cite` with the chunk_ids that ground your answer before writing the final response.

          You MUST call `cite` with at least one chunk ID before producing your final answer, **unless** you are refusing for lack of information. Answers without citations are considered ungrounded. In a refusal case do **not** call `cite` — there is nothing to cite.

          ## Important

          - Variables persist between `execute_code` calls — you can search in one call and process results in the next
          - Use `print()` to output results — the output is your only feedback
          - When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `search → cite`.
          - Use `await` for all async functions inside execute_code (search, list_documents)
          - Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
          - Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `cite` tool separately to register citations.

          ## Guidelines

          - Follow the skill instructions carefully
          - Stay focused on the specific task described above
          - Provide a clear, complete result          
        role: system
      - content: Search for content about document element types or labels. What are all the different document element types
          mentioned? List them all.
        role: user
      model: gpt-oss
      reasoning_effort: high
      stream: true
      stream_options:
        include_usage: true
      temperature: 0.3
      tool_choice: auto
      tools:
      - function:
          description: |-
            Search the knowledge base using hybrid search (vector + full-text).

            Returns ranked results with content and metadata. When picture
            content is in the result set and the driving skill model is
            vision-capable, picture bytes are attached as ``BinaryContent``
            parts so the model sees figures alongside text.            
          name: search
          parameters:
            additionalProperties: false
            properties:
              limit:
                anyOf:
                - type: integer
                - type: 'null'
                default: null
                description: Maximum number of results.
              query:
                description: The search query.
                type: string
            required:
            - query
            type: object
        type: function
      - function:
          description: |-
            Execute Python code in a sandboxed interpreter.

            The code has access to search() and list_documents() functions
            and a virtual filesystem at /documents/ with document content
            and structure (metadata.json, content.txt, items.jsonl, toc.json
            per document).

            Use print() to output results. Variables persist between calls
            within the same skill invocation.            
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
        type: function
      - function:
          description: |-
            Register chunk IDs as citations for your answer.

            Call this after searching, with the chunk_id values from search
            results that support your answer.            
          name: cite
          parameters:
            additionalProperties: false
            properties:
              chunk_ids:
                description: List of chunk_id values from search results.
                items:
                  type: string
                type: array
            required:
            - chunk_ids
            type: object
        type: function
    uri: http://localhost:11434/v1/chat/completions
  response:
    body:
      string: |+
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
        index: 0
        object: embedding
      model: qwen3-embedding:4b
      object: list
      usage:
        prompt_tokens: 4
        total_tokens: 4
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '20877'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a focused execution agent. Complete the following task using the skills and instructions provided.

          ## Task

          Search for content about document element types or labels. What are all the different document element types mentioned? List them all.

          ## Skill instructions

          # Analysis

          You answer questions over a document knowledge base. Most questions are answered directly with `search → cite → answer`. Reach for `execute_code` when a question requires computation, aggregation, or structural traversal that a single search cannot deliver.

          ## Tools

          ### execute_code
          Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.

          Inside the code, these functions are available (use `await`):
          - `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
          - `await list_documents()` → list of dicts with keys: id, title, uri, created_at

          Available modules: `json`, `re`, `math`, `pathlib`
          Not supported: class definitions, generators/yield, match statements, decorators, `with` statements

          ### search
          Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.

          ### cite
          Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the `chunk_id` values from search results (from either the `search` tool or `await search(...)` inside `execute_code`) that support each claim. Every answer that uses search results must be backed by `cite`.

          Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.

          ## Document Filesystem (inside execute_code)

          All documents are mounted as a virtual filesystem at `/documents/`:

          ```
          /documents/{document_id}/
              metadata.json    # {"id", "title", "uri", "created_at"}
              content.txt      # Full document text
              items.jsonl      # Structured items (one JSON object per line)
              toc.json         # Section tree derived from heading_level
          ```

          `{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids, or read `metadata.json` from each directory and match against `uri` / `title`.

          ### Reading files
          Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).

          ```python
          from pathlib import Path
          import json

          # Discover documents
          for doc_dir in Path('/documents').iterdir():
              meta = json.loads((doc_dir / 'metadata.json').read_text())
              print(meta['title'])

          # Read full text
          content = Path(f'/documents/{doc_id}/content.txt').read_text()

          # Read and parse items
          for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
              item = json.loads(line)
              if item['label'] == 'table':
                  print(item['text'][:200])
          ```

          ### metadata.json
          Document metadata: `id`, `title`, `uri`, `created_at`.

          ### content.txt
          Full text content. Use for regex or keyword search across a whole document.

          ### items.jsonl
          Structured document items. Each line is a JSON object with:
          - `position`: sequential position in the document
          - `self_ref`: item reference (e.g. "#/texts/5", "#/tables/0")
          - `label`: item type — "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote"
          - `text`: rendered content (tables are markdown with `|` columns)
          - `page_numbers`: list of page numbers where the item appears
          - `heading_level`: H-level (1–6) for `section_header` items, `0` otherwise. PDFs often collapse to `1` for every header.
          - `tree_depth`: DOM nesting depth — varies meaningfully on HTML, near-uniform on PDFs.

          ### toc.json
          Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, position, page_numbers, item_range: [start, end_exclusive], children}`. Slice `items.jsonl` by `item_range` to read a section's contents. PDFs typically produce a flat sibling list; HTML/markdown produce a real tree. `tree: []` for docs with no headers.

          ### Cross-referencing search results with items
          Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in items.jsonl. Resolve each ref to a `position`, then walk `toc.json` to find the deepest node whose `item_range` contains it — that's the section the hit lives in.

          ## Strategy

          1. Search first.
          2. If the top results contain the answer, call `cite` with the supporting chunk_ids and write a concise answer.
          3. Reach for `execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
          4. Call `cite` with the chunk_ids that ground your answer before writing the final response.

          You MUST call `cite` with at least one chunk ID before producing your final answer, **unless** you are refusing for lack of information. Answers without citations are considered ungrounded. In a refusal case do **not** call `cite` — there is nothing to cite.

          ## Important

          - Variables persist between `execute_code` calls — you can search in one call and process results in the next
          - Use `print()` to output results — the output is your only feedback
          - When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `search → cite`.
          - Use `await` for all async functions inside execute_code (search, list_documents)
          - Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
          - Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `cite` tool separately to register citations.

          ## Guidelines

          - Follow the skill instructions carefully
          - Stay focused on the specific task described above
          - Provide a clear, complete result          
        role: system
      - content: Search for content about document element types or labels. What are all the different document element types
          mentioned? List them all.
        role: user
      - content: null
        reasoning: |-
          We need to search for content about document element types or labels. The question: "Search for content about document element types or labels. What are all the different document element types mentioned? List them all."

          We need to find all the different document element types mentioned. Likely in the knowledge base there are documents describing element types or labels. We need to list them all. The answer must be grounded with citations. So we need to search for "document element types" or "labels" or "element types" or "document element types" or "document element labels". The question is ambiguous: "Search for content about document element types or labels. What are all the different document element types mentioned? List them all."

          We need to search the knowledge base for content about document element types or labels. The answer: list all the different document element types mentioned. This likely refers to the labels in items.jsonl: "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote". But maybe there are others. Let's search for "label" or "document element types" or "element types" or "labels" or "document element types" in the knowledge base. Use the search tool.

          We need to search for "document element types" or "labels" or "document element types" or "document element types mentioned" or "element types" or "labels" or "document element types".

          We can use the search tool. Let's search for "document element types" first.          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"query":"document element types","limit":10}'
            name: search
          id: call_539fpdo9
          type: function
      - content: |-
          [b79aaef6-707e-4840-b3d0-b9941fd1b9d6] [rank 1 of 1]
          Type: table
          Figure caption (#/pictures/0): Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
          Content:
          Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.

          |                |         | % of Total   | % of Total   | % of Total   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   |
          |----------------|---------|--------------|--------------|--------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|
          | class label    | Count   | Train        | Test         | Val          | All                                        | Fin                                        | Man                                        | Sci                                        | Law                                        | Pat                                        | Ten                                        |
          | Caption        | 22524   | 2.04         | 1.77         | 2.32         | 84-89                                      | 40-61                                      | 86-92                                      | 94-99                                      | 95-99                                      | 69-78                                      | n/a                                        |
          | Footnote       | 6318    | 0.60         | 0.31         | 0.58         | 83-91                                      | n/a                                        | 100                                        | 62-88                                      | 85-94                                      | n/a                                        | 82-97                                      |
          | Formula        | 25027   | 2.25         | 1.90         | 2.96         | 83-85                                      | n/a                                        | n/a                                        | 84-87                                      | 86-96                                      | n/a                                        | n/a                                        |
          | List-item      | 185660  | 17.19        | 13.34        | 15.82        | 87-88                                      | 74-83                                      | 90-92                                      | 97-97                                      | 81-85                                      | 75-88                                      | 93-95                                      |
          | Page-footer    | 70878   | 6.51         | 5.58         | 6.00         | 93-94                                      | 88-90                                      | 95-96                                      | 100                                        | 92-97                                      | 100                                        | 96-98                                      |
          | Page-header    | 58022   | 5.10         | 6.70         | 5.06         | 85-89                                      | 66-76                                      | 90-94                                      | 98-100                                     | 91-92                                      | 97-99                                      | 81-86                                      |
          | Picture        | 45976   | 4.21         | 2.78         | 5.31         | 69-71                                      | 56-59                                      | 82-86                                      | 69-82                                      | 80-95                                      | 66-71                                      | 59-76                                      |
          | Section-header | 142884  | 12.60        | 15.77        | 12.85        | 83-84                                      | 76-81                                      | 90-92                                      | 94-95                                      | 87-94                                      | 69-73                                      | 78-86                                      |
          | Table          | 34733   | 3.20         | 2.27         | 3.60         | 77-81                                      | 75-80                                      | 83-86                                      | 98-99                                      | 58-80                                      | 79-84                                      | 70-85                                      |
          | Text           | 510377  | 45.82        | 49.28        | 45.00        | 84-86                                      | 81-86                                      | 88-93                                      | 89-93                                      | 87-92                                      | 71-79                                      | 87-95                                      |
          | Title          | 5071    | 0.47         | 0.30         | 0.50         | 60-72                                      | 24-63                                      | 50-63                                      | 94-100                                     | 82-96                                      | 68-79                                      | 24-56                                      |
          | Total          | 1107470 | 941123       | 99816        | 66531        | 82-83                                      | 71-74                                      | 79-81                                      | 89-94                                      | 86-91                                      | 71-76                                      | 68-85                                      |

          Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.

          Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.

          Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.

          we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.

          Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.

          Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.

          Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on

          3 https://arxiv.org/          
        role: tool
        tool_call_id: call_539fpdo9
      model: gpt-oss
      reasoning_effort: high
      stream: true
      stream_options:
        include_usage: true
      temperature: 0.3
      tool_choice: auto
      tools:
      - function:
          description: |-
            Search the knowledge base using hybrid search (vector + full-text).

            Returns ranked results with content and metadata. When picture
            content is in the result set and the driving skill model is
            vision-capable, picture bytes are attached as ``BinaryContent``
            parts so the model sees figures alongside text.            
          name: search
          parameters:
            additionalProperties: false
            properties:
              limit:
                anyOf:
                - type: integer
                - type: 'null'
                default: null
                description: Maximum number of results.
              query:
                description: The search query.
                type: string
            required:
            - query
            type: object
        type: function
      - function:
          description: |-
            Execute Python code in a sandboxed interpreter.

            The code has access to search() and list_documents() functions
            and a virtual filesystem at /documents/ with document content
            and structure (metadata.json, content.txt, items.jsonl, toc.json
            per document).

            Use print() to output results. Variables persist between calls
            within the same skill invocation.            
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
        type: function
      - function:
          description: |-
            Register chunk IDs as citations for your answer.

            Call this after searching, with the chunk_id values from search
            results that support your answer.            
          name: cite
          parameters:
            additionalProperties: false
            properties:
              chunk_ids:
                description: List of chunk_id values from search results.
                items:
                  type: string
                type: array
            required:
            - chunk_ids
            type: object
        type: function
    uri: http://localhost:11434/v1/chat/completions
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
        index: 0
        object: embedding
      model: qwen3-embedding:4b
      object: list
      usage:
        prompt_tokens: 3
        total_tokens: 3
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '32407'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a focused execution agent. Complete the following task using the skills and instructions provided.

          ## Task

          Search for content about document element types or labels. What are all the different document element types mentioned? List them all.

          ## Skill instructions

          # Analysis

          You answer questions over a document knowledge base. Most questions are answered directly with `search → cite → answer`. Reach for `execute_code` when a question requires computation, aggregation, or structural traversal that a single search cannot deliver.

          ## Tools

          ### execute_code
          Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.

          Inside the code, these functions are available (use `await`):
          - `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
          - `await list_documents()` → list of dicts with keys: id, title, uri, created_at

          Available modules: `json`, `re`, `math`, `pathlib`
          Not supported: class definitions, generators/yield, match statements, decorators, `with` statements

          ### search
          Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.

          ### cite
          Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the `chunk_id` values from search results (from either the `search` tool or `await search(...)` inside `execute_code`) that support each claim. Every answer that uses search results must be backed by `cite`.

          Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.

          ## Document Filesystem (inside execute_code)

          All documents are mounted as a virtual filesystem at `/documents/`:

          ```
          /documents/{document_id}/
              metadata.json    # {"id", "title", "uri", "created_at"}
              content.txt      # Full document text
              items.jsonl      # Structured items (one JSON object per line)
              toc.json         # Section tree derived from heading_level
          ```

          `{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids, or read `metadata.json` from each directory and match against `uri` / `title`.

          ### Reading files
          Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).

          ```python
          from pathlib import Path
          import json

          # Discover documents
          for doc_dir in Path('/documents').iterdir():
              meta = json.loads((doc_dir / 'metadata.json').read_text())
              print(meta['title'])

          # Read full text
          content = Path(f'/documents/{doc_id}/content.txt').read_text()

          # Read and parse items
          for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
              item = json.loads(line)
              if item['label'] == 'table':
                  print(item['text'][:200])
          ```

          ### metadata.json
          Document metadata: `id`, `title`, `uri`, `created_at`.

          ### content.txt
          Full text content. Use for regex or keyword search across a whole document.

          ### items.jsonl
          Structured document items. Each line is a JSON object with:
          - `position`: sequential position in the document
          - `self_ref`: item reference (e.g. "#/texts/5", "#/tables/0")
          - `label`: item type — "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote"
          - `text`: rendered content (tables are markdown with `|` columns)
          - `page_numbers`: list of page numbers where the item appears
          - `heading_level`: H-level (1–6) for `section_header` items, `0` otherwise. PDFs often collapse to `1` for every header.
          - `tree_depth`: DOM nesting depth — varies meaningfully on HTML, near-uniform on PDFs.

          ### toc.json
          Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, position, page_numbers, item_range: [start, end_exclusive], children}`. Slice `items.jsonl` by `item_range` to read a section's contents. PDFs typically produce a flat sibling list; HTML/markdown produce a real tree. `tree: []` for docs with no headers.

          ### Cross-referencing search results with items
          Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in items.jsonl. Resolve each ref to a `position`, then walk `toc.json` to find the deepest node whose `item_range` contains it — that's the section the hit lives in.

          ## Strategy

          1. Search first.
          2. If the top results contain the answer, call `cite` with the supporting chunk_ids and write a concise answer.
          3. Reach for `execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
          4. Call `cite` with the chunk_ids that ground your answer before writing the final response.

          You MUST call `cite` with at least one chunk ID before producing your final answer, **unless** you are refusing for lack of information. Answers without citations are considered ungrounded. In a refusal case do **not** call `cite` — there is nothing to cite.

          ## Important

          - Variables persist between `execute_code` calls — you can search in one call and process results in the next
          - Use `print()` to output results — the output is your only feedback
          - When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `search → cite`.
          - Use `await` for all async functions inside execute_code (search, list_documents)
          - Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
          - Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `cite` tool separately to register citations.

          ## Guidelines

          - Follow the skill instructions carefully
          - Stay focused on the specific task described above
          - Provide a clear, complete result          
        role: system
      - content: Search for content about document element types or labels. What are all the different document element types
          mentioned? List them all.
        role: user
      - content: null
        reasoning: |-
          We need to search for content about document element types or labels. The question: "Search for content about document element types or labels. What are all the different document element types mentioned? List them all."

          We need to find all the different document element types mentioned. Likely in the knowledge base there are documents describing element types or labels. We need to list them all. The answer must be grounded with citations. So we need to search for "document element types" or "labels" or "element types" or "document element types" or "document element labels". The question is ambiguous: "Search for content about document element types or labels. What are all the different document element types mentioned? List them all."

          We need to search the knowledge base for content about document element types or labels. The answer: list all the different document element types mentioned. This likely refers to the labels in items.jsonl: "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote". But maybe there are others. Let's search for "label" or "document element types" or "element types" or "labels" or "document element types" in the knowledge base. Use the search tool.

          We need to search for "document element types" or "labels" or "document element types" or "document element types mentioned" or "element types" or "labels" or "document element types".

          We can use the search tool. Let's search for "document element types" first.          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"query":"document element types","limit":10}'
            name: search
          id: call_539fpdo9
          type: function
      - content: |-
          [b79aaef6-707e-4840-b3d0-b9941fd1b9d6] [rank 1 of 1]
          Type: table
          Figure caption (#/pictures/0): Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
          Content:
          Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.

          |                |         | % of Total   | % of Total   | % of Total   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   |
          |----------------|---------|--------------|--------------|--------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|
          | class label    | Count   | Train        | Test         | Val          | All                                        | Fin                                        | Man                                        | Sci                                        | Law                                        | Pat                                        | Ten                                        |
          | Caption        | 22524   | 2.04         | 1.77         | 2.32         | 84-89                                      | 40-61                                      | 86-92                                      | 94-99                                      | 95-99                                      | 69-78                                      | n/a                                        |
          | Footnote       | 6318    | 0.60         | 0.31         | 0.58         | 83-91                                      | n/a                                        | 100                                        | 62-88                                      | 85-94                                      | n/a                                        | 82-97                                      |
          | Formula        | 25027   | 2.25         | 1.90         | 2.96         | 83-85                                      | n/a                                        | n/a                                        | 84-87                                      | 86-96                                      | n/a                                        | n/a                                        |
          | List-item      | 185660  | 17.19        | 13.34        | 15.82        | 87-88                                      | 74-83                                      | 90-92                                      | 97-97                                      | 81-85                                      | 75-88                                      | 93-95                                      |
          | Page-footer    | 70878   | 6.51         | 5.58         | 6.00         | 93-94                                      | 88-90                                      | 95-96                                      | 100                                        | 92-97                                      | 100                                        | 96-98                                      |
          | Page-header    | 58022   | 5.10         | 6.70         | 5.06         | 85-89                                      | 66-76                                      | 90-94                                      | 98-100                                     | 91-92                                      | 97-99                                      | 81-86                                      |
          | Picture        | 45976   | 4.21         | 2.78         | 5.31         | 69-71                                      | 56-59                                      | 82-86                                      | 69-82                                      | 80-95                                      | 66-71                                      | 59-76                                      |
          | Section-header | 142884  | 12.60        | 15.77        | 12.85        | 83-84                                      | 76-81                                      | 90-92                                      | 94-95                                      | 87-94                                      | 69-73                                      | 78-86                                      |
          | Table          | 34733   | 3.20         | 2.27         | 3.60         | 77-81                                      | 75-80                                      | 83-86                                      | 98-99                                      | 58-80                                      | 79-84                                      | 70-85                                      |
          | Text           | 510377  | 45.82        | 49.28        | 45.00        | 84-86                                      | 81-86                                      | 88-93                                      | 89-93                                      | 87-92                                      | 71-79                                      | 87-95                                      |
          | Title          | 5071    | 0.47         | 0.30         | 0.50         | 60-72                                      | 24-63                                      | 50-63                                      | 94-100                                     | 82-96                                      | 68-79                                      | 24-56                                      |
          | Total          | 1107470 | 941123       | 99816        | 66531        | 82-83                                      | 71-74                                      | 79-81                                      | 89-94                                      | 86-91                                      | 71-76                                      | 68-85                                      |

          Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.

          Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.

          Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.

          we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.

          Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.

          Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.

          Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on

          3 https://arxiv.org/          
        role: tool
        tool_call_id: call_539fpdo9
      - content: null
        reasoning: |-
          The search result shows a table with class labels: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. This is likely the list of document element types. The question: "Search for content about document element types or labels. What are all the different document element types mentioned? List them all."

          Thus we need to list all the different document element types mentioned. The table shows 11 distinct class labels. We should list them: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title.

          But we should confirm if there are any other document element types in other documents. Let's search for "class label" or "class labels" or "document element types" or "label" or "element types" in other documents. But likely this is the main source. However, there might be other documents that mention other element types. Let's search for "element types" or "document element types" again.          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"limit":10,"query":"class label"}'
            name: search
          id: call_f9c6zlic
          type: function
      - content: |-
          [b79aaef6-707e-4840-b3d0-b9941fd1b9d6] [rank 1 of 1]
          Type: table
          Figure caption (#/pictures/0): Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
          Content:
          Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.

          |                |         | % of Total   | % of Total   | % of Total   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   |
          |----------------|---------|--------------|--------------|--------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|
          | class label    | Count   | Train        | Test         | Val          | All                                        | Fin                                        | Man                                        | Sci                                        | Law                                        | Pat                                        | Ten                                        |
          | Caption        | 22524   | 2.04         | 1.77         | 2.32         | 84-89                                      | 40-61                                      | 86-92                                      | 94-99                                      | 95-99                                      | 69-78                                      | n/a                                        |
          | Footnote       | 6318    | 0.60         | 0.31         | 0.58         | 83-91                                      | n/a                                        | 100                                        | 62-88                                      | 85-94                                      | n/a                                        | 82-97                                      |
          | Formula        | 25027   | 2.25         | 1.90         | 2.96         | 83-85                                      | n/a                                        | n/a                                        | 84-87                                      | 86-96                                      | n/a                                        | n/a                                        |
          | List-item      | 185660  | 17.19        | 13.34        | 15.82        | 87-88                                      | 74-83                                      | 90-92                                      | 97-97                                      | 81-85                                      | 75-88                                      | 93-95                                      |
          | Page-footer    | 70878   | 6.51         | 5.58         | 6.00         | 93-94                                      | 88-90                                      | 95-96                                      | 100                                        | 92-97                                      | 100                                        | 96-98                                      |
          | Page-header    | 58022   | 5.10         | 6.70         | 5.06         | 85-89                                      | 66-76                                      | 90-94                                      | 98-100                                     | 91-92                                      | 97-99                                      | 81-86                                      |
          | Picture        | 45976   | 4.21         | 2.78         | 5.31         | 69-71                                      | 56-59                                      | 82-86                                      | 69-82                                      | 80-95                                      | 66-71                                      | 59-76                                      |
          | Section-header | 142884  | 12.60        | 15.77        | 12.85        | 83-84                                      | 76-81                                      | 90-92                                      | 94-95                                      | 87-94                                      | 69-73                                      | 78-86                                      |
          | Table          | 34733   | 3.20         | 2.27         | 3.60         | 77-81                                      | 75-80                                      | 83-86                                      | 98-99                                      | 58-80                                      | 79-84                                      | 70-85                                      |
          | Text           | 510377  | 45.82        | 49.28        | 45.00        | 84-86                                      | 81-86                                      | 88-93                                      | 89-93                                      | 87-92                                      | 71-79                                      | 87-95                                      |
          | Title          | 5071    | 0.47         | 0.30         | 0.50         | 60-72                                      | 24-63                                      | 50-63                                      | 94-100                                     | 82-96                                      | 68-79                                      | 24-56                                      |
          | Total          | 1107470 | 941123       | 99816        | 66531        | 82-83                                      | 71-74                                      | 79-81                                      | 89-94                                      | 86-91                                      | 71-76                                      | 68-85                                      |

          Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.

          Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.

          Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.

          we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.

          Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.

          Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.

          Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on

          3 https://arxiv.org/          
        role: tool
        tool_call_id: call_f9c6zlic
      model: gpt-oss
      reasoning_effort: high
      stream: true
      stream_options:
        include_usage: true
      temperature: 0.3
      tool_choice: auto
      tools:
      - function:
          description: |-
            Search the knowledge base using hybrid search (vector + full-text).

            Returns ranked results with content and metadata. When picture
            content is in the result set and the driving skill model is
            vision-capable, picture bytes are attached as ``BinaryContent``
            parts so the model sees figures alongside text.            
          name: search
          parameters:
            additionalProperties: false
            properties:
              limit:
                anyOf:
                - type: integer
                - type: 'null'
                default: null
                description: Maximum number of results.
              query:
                description: The search query.
                type: string
            required:
            - query
            type: object
        type: function
      - function:
          description: |-
            Execute Python code in a sandboxed interpreter.

            The code has access to search() and list_documents() functions
            and a virtual filesystem at /documents/ with document content
            and structure (metadata.json, content.txt, items.jsonl, toc.json
            per document).

            Use print() to output results. Variables persist between calls
            within the same skill invocation.            
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
        type: function
      - function:
          description: |-
            Register chunk IDs as citations for your answer.

            Call this after searching, with the chunk_id values from search
            results that support your answer.            
          name: cite
          parameters:
            additionalProperties: false
            properties:
              chunk_ids:
                description: List of chunk_id values from search results.
                items:
                  type: string
                type: array
            required:
            - chunk_ids
            type: object
        type: function
    uri: http://localhost:11434/v1/chat/completions
  response:
    body:
      string: |+
        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"The"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" search"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" result"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" includes"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" list"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" of"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" "},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"11"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" class"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" labels"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" The"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" question"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":":"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" \""},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"Search"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" for"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176228,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" content"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176228,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" about"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176228,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" document"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176228,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" element"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176228,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" types"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176228,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" or"},"finish_reason":null}]}

        data: {"id":"chatcmpl-605","object":"chat.completion.chunk","created":1779176228,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" labels"},"finish_reason":null}]}

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        data: [DONE]        

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      - content: |-
          You are a focused execution agent. Complete the following task using the skills and instructions provided.

          ## Task

          Search for content about document element types or labels. What are all the different document element types mentioned? List them all.

          ## Skill instructions

          # Analysis

          You answer questions over a document knowledge base. Most questions are answered directly with `search → cite → answer`. Reach for `execute_code` when a question requires computation, aggregation, or structural traversal that a single search cannot deliver.

          ## Tools

          ### execute_code
          Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.

          Inside the code, these functions are available (use `await`):
          - `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
          - `await list_documents()` → list of dicts with keys: id, title, uri, created_at

          Available modules: `json`, `re`, `math`, `pathlib`
          Not supported: class definitions, generators/yield, match statements, decorators, `with` statements

          ### search
          Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.

          ### cite
          Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the `chunk_id` values from search results (from either the `search` tool or `await search(...)` inside `execute_code`) that support each claim. Every answer that uses search results must be backed by `cite`.

          Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.

          ## Document Filesystem (inside execute_code)

          All documents are mounted as a virtual filesystem at `/documents/`:

          ```
          /documents/{document_id}/
              metadata.json    # {"id", "title", "uri", "created_at"}
              content.txt      # Full document text
              items.jsonl      # Structured items (one JSON object per line)
              toc.json         # Section tree derived from heading_level
          ```

          `{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids, or read `metadata.json` from each directory and match against `uri` / `title`.

          ### Reading files
          Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).

          ```python
          from pathlib import Path
          import json

          # Discover documents
          for doc_dir in Path('/documents').iterdir():
              meta = json.loads((doc_dir / 'metadata.json').read_text())
              print(meta['title'])

          # Read full text
          content = Path(f'/documents/{doc_id}/content.txt').read_text()

          # Read and parse items
          for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
              item = json.loads(line)
              if item['label'] == 'table':
                  print(item['text'][:200])
          ```

          ### metadata.json
          Document metadata: `id`, `title`, `uri`, `created_at`.

          ### content.txt
          Full text content. Use for regex or keyword search across a whole document.

          ### items.jsonl
          Structured document items. Each line is a JSON object with:
          - `position`: sequential position in the document
          - `self_ref`: item reference (e.g. "#/texts/5", "#/tables/0")
          - `label`: item type — "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote"
          - `text`: rendered content (tables are markdown with `|` columns)
          - `page_numbers`: list of page numbers where the item appears
          - `heading_level`: H-level (1–6) for `section_header` items, `0` otherwise. PDFs often collapse to `1` for every header.
          - `tree_depth`: DOM nesting depth — varies meaningfully on HTML, near-uniform on PDFs.

          ### toc.json
          Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, position, page_numbers, item_range: [start, end_exclusive], children}`. Slice `items.jsonl` by `item_range` to read a section's contents. PDFs typically produce a flat sibling list; HTML/markdown produce a real tree. `tree: []` for docs with no headers.

          ### Cross-referencing search results with items
          Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in items.jsonl. Resolve each ref to a `position`, then walk `toc.json` to find the deepest node whose `item_range` contains it — that's the section the hit lives in.

          ## Strategy

          1. Search first.
          2. If the top results contain the answer, call `cite` with the supporting chunk_ids and write a concise answer.
          3. Reach for `execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
          4. Call `cite` with the chunk_ids that ground your answer before writing the final response.

          You MUST call `cite` with at least one chunk ID before producing your final answer, **unless** you are refusing for lack of information. Answers without citations are considered ungrounded. In a refusal case do **not** call `cite` — there is nothing to cite.

          ## Important

          - Variables persist between `execute_code` calls — you can search in one call and process results in the next
          - Use `print()` to output results — the output is your only feedback
          - When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `search → cite`.
          - Use `await` for all async functions inside execute_code (search, list_documents)
          - Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
          - Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `cite` tool separately to register citations.

          ## Guidelines

          - Follow the skill instructions carefully
          - Stay focused on the specific task described above
          - Provide a clear, complete result          
        role: system
      - content: Search for content about document element types or labels. What are all the different document element types
          mentioned? List them all.
        role: user
      - content: null
        reasoning: |-
          We need to search for content about document element types or labels. The question: "Search for content about document element types or labels. What are all the different document element types mentioned? List them all."

          We need to find all the different document element types mentioned. Likely in the knowledge base there are documents describing element types or labels. We need to list them all. The answer must be grounded with citations. So we need to search for "document element types" or "labels" or "element types" or "document element types" or "document element labels". The question is ambiguous: "Search for content about document element types or labels. What are all the different document element types mentioned? List them all."

          We need to search the knowledge base for content about document element types or labels. The answer: list all the different document element types mentioned. This likely refers to the labels in items.jsonl: "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote". But maybe there are others. Let's search for "label" or "document element types" or "element types" or "labels" or "document element types" in the knowledge base. Use the search tool.

          We need to search for "document element types" or "labels" or "document element types" or "document element types mentioned" or "element types" or "labels" or "document element types".

          We can use the search tool. Let's search for "document element types" first.          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"query":"document element types","limit":10}'
            name: search
          id: call_539fpdo9
          type: function
      - content: |-
          [b79aaef6-707e-4840-b3d0-b9941fd1b9d6] [rank 1 of 1]
          Type: table
          Figure caption (#/pictures/0): Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
          Content:
          Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.

          |                |         | % of Total   | % of Total   | % of Total   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   |
          |----------------|---------|--------------|--------------|--------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|
          | class label    | Count   | Train        | Test         | Val          | All                                        | Fin                                        | Man                                        | Sci                                        | Law                                        | Pat                                        | Ten                                        |
          | Caption        | 22524   | 2.04         | 1.77         | 2.32         | 84-89                                      | 40-61                                      | 86-92                                      | 94-99                                      | 95-99                                      | 69-78                                      | n/a                                        |
          | Footnote       | 6318    | 0.60         | 0.31         | 0.58         | 83-91                                      | n/a                                        | 100                                        | 62-88                                      | 85-94                                      | n/a                                        | 82-97                                      |
          | Formula        | 25027   | 2.25         | 1.90         | 2.96         | 83-85                                      | n/a                                        | n/a                                        | 84-87                                      | 86-96                                      | n/a                                        | n/a                                        |
          | List-item      | 185660  | 17.19        | 13.34        | 15.82        | 87-88                                      | 74-83                                      | 90-92                                      | 97-97                                      | 81-85                                      | 75-88                                      | 93-95                                      |
          | Page-footer    | 70878   | 6.51         | 5.58         | 6.00         | 93-94                                      | 88-90                                      | 95-96                                      | 100                                        | 92-97                                      | 100                                        | 96-98                                      |
          | Page-header    | 58022   | 5.10         | 6.70         | 5.06         | 85-89                                      | 66-76                                      | 90-94                                      | 98-100                                     | 91-92                                      | 97-99                                      | 81-86                                      |
          | Picture        | 45976   | 4.21         | 2.78         | 5.31         | 69-71                                      | 56-59                                      | 82-86                                      | 69-82                                      | 80-95                                      | 66-71                                      | 59-76                                      |
          | Section-header | 142884  | 12.60        | 15.77        | 12.85        | 83-84                                      | 76-81                                      | 90-92                                      | 94-95                                      | 87-94                                      | 69-73                                      | 78-86                                      |
          | Table          | 34733   | 3.20         | 2.27         | 3.60         | 77-81                                      | 75-80                                      | 83-86                                      | 98-99                                      | 58-80                                      | 79-84                                      | 70-85                                      |
          | Text           | 510377  | 45.82        | 49.28        | 45.00        | 84-86                                      | 81-86                                      | 88-93                                      | 89-93                                      | 87-92                                      | 71-79                                      | 87-95                                      |
          | Title          | 5071    | 0.47         | 0.30         | 0.50         | 60-72                                      | 24-63                                      | 50-63                                      | 94-100                                     | 82-96                                      | 68-79                                      | 24-56                                      |
          | Total          | 1107470 | 941123       | 99816        | 66531        | 82-83                                      | 71-74                                      | 79-81                                      | 89-94                                      | 86-91                                      | 71-76                                      | 68-85                                      |

          Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.

          Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.

          Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.

          we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.

          Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.

          Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.

          Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on

          3 https://arxiv.org/          
        role: tool
        tool_call_id: call_539fpdo9
      - content: null
        reasoning: |-
          The search result shows a table with class labels: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. This is likely the list of document element types. The question: "Search for content about document element types or labels. What are all the different document element types mentioned? List them all."

          Thus we need to list all the different document element types mentioned. The table shows 11 distinct class labels. We should list them: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title.

          But we should confirm if there are any other document element types in other documents. Let's search for "class label" or "class labels" or "document element types" or "label" or "element types" in other documents. But likely this is the main source. However, there might be other documents that mention other element types. Let's search for "element types" or "document element types" again.          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"limit":10,"query":"class label"}'
            name: search
          id: call_f9c6zlic
          type: function
      - content: |-
          [b79aaef6-707e-4840-b3d0-b9941fd1b9d6] [rank 1 of 1]
          Type: table
          Figure caption (#/pictures/0): Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
          Content:
          Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.

          |                |         | % of Total   | % of Total   | % of Total   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   | triple inter-annotator mAP @0.5-0.95 (%)   |
          |----------------|---------|--------------|--------------|--------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|
          | class label    | Count   | Train        | Test         | Val          | All                                        | Fin                                        | Man                                        | Sci                                        | Law                                        | Pat                                        | Ten                                        |
          | Caption        | 22524   | 2.04         | 1.77         | 2.32         | 84-89                                      | 40-61                                      | 86-92                                      | 94-99                                      | 95-99                                      | 69-78                                      | n/a                                        |
          | Footnote       | 6318    | 0.60         | 0.31         | 0.58         | 83-91                                      | n/a                                        | 100                                        | 62-88                                      | 85-94                                      | n/a                                        | 82-97                                      |
          | Formula        | 25027   | 2.25         | 1.90         | 2.96         | 83-85                                      | n/a                                        | n/a                                        | 84-87                                      | 86-96                                      | n/a                                        | n/a                                        |
          | List-item      | 185660  | 17.19        | 13.34        | 15.82        | 87-88                                      | 74-83                                      | 90-92                                      | 97-97                                      | 81-85                                      | 75-88                                      | 93-95                                      |
          | Page-footer    | 70878   | 6.51         | 5.58         | 6.00         | 93-94                                      | 88-90                                      | 95-96                                      | 100                                        | 92-97                                      | 100                                        | 96-98                                      |
          | Page-header    | 58022   | 5.10         | 6.70         | 5.06         | 85-89                                      | 66-76                                      | 90-94                                      | 98-100                                     | 91-92                                      | 97-99                                      | 81-86                                      |
          | Picture        | 45976   | 4.21         | 2.78         | 5.31         | 69-71                                      | 56-59                                      | 82-86                                      | 69-82                                      | 80-95                                      | 66-71                                      | 59-76                                      |
          | Section-header | 142884  | 12.60        | 15.77        | 12.85        | 83-84                                      | 76-81                                      | 90-92                                      | 94-95                                      | 87-94                                      | 69-73                                      | 78-86                                      |
          | Table          | 34733   | 3.20         | 2.27         | 3.60         | 77-81                                      | 75-80                                      | 83-86                                      | 98-99                                      | 58-80                                      | 79-84                                      | 70-85                                      |
          | Text           | 510377  | 45.82        | 49.28        | 45.00        | 84-86                                      | 81-86                                      | 88-93                                      | 89-93                                      | 87-92                                      | 71-79                                      | 87-95                                      |
          | Title          | 5071    | 0.47         | 0.30         | 0.50         | 60-72                                      | 24-63                                      | 50-63                                      | 94-100                                     | 82-96                                      | 68-79                                      | 24-56                                      |
          | Total          | 1107470 | 941123       | 99816        | 66531        | 82-83                                      | 71-74                                      | 79-81                                      | 89-94                                      | 86-91                                      | 71-76                                      | 68-85                                      |

          Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.

          Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.

          Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.

          we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.

          Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.

          Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.

          Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on

          3 https://arxiv.org/          
        role: tool
        tool_call_id: call_f9c6zlic
      - content: null
        reasoning: |-
          The search result includes the list of 11 class labels. The question: "Search for content about document element types or labels. What are all the different document element types mentioned? List them all."

          Thus we need to list all the different document element types mentioned. The answer: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title.

          We should cite the chunk IDs from the search result. The chunk ID is b79aaef6-707e-4840-b3d0-b9941fd1b9d6. This chunk contains the table and the text that lists the class labels. So we can cite that chunk ID.

          We should call cite with chunk_ids: ["b79aaef6-707e-4840-b3d0-b9941fd1b9d6"].

          Then answer: The document element types mentioned are: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title.

          We should mention that these are the 11 distinct class labels. Provide the list. That should satisfy the question.

          We need to ensure we call cite before writing the answer. So we need to call the cite tool. Then write the answer. Let's do that.          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"chunk_ids":["b79aaef6-707e-4840-b3d0-b9941fd1b9d6"]}'
            name: cite
          id: call_bevluili
          type: function
      - content: Registered 1 citation(s).
        role: tool
        tool_call_id: call_bevluili
      model: gpt-oss
      reasoning_effort: high
      stream: true
      stream_options:
        include_usage: true
      temperature: 0.3
      tool_choice: auto
      tools:
      - function:
          description: |-
            Search the knowledge base using hybrid search (vector + full-text).

            Returns ranked results with content and metadata. When picture
            content is in the result set and the driving skill model is
            vision-capable, picture bytes are attached as ``BinaryContent``
            parts so the model sees figures alongside text.            
          name: search
          parameters:
            additionalProperties: false
            properties:
              limit:
                anyOf:
                - type: integer
                - type: 'null'
                default: null
                description: Maximum number of results.
              query:
                description: The search query.
                type: string
            required:
            - query
            type: object
        type: function
      - function:
          description: |-
            Execute Python code in a sandboxed interpreter.

            The code has access to search() and list_documents() functions
            and a virtual filesystem at /documents/ with document content
            and structure (metadata.json, content.txt, items.jsonl, toc.json
            per document).

            Use print() to output results. Variables persist between calls
            within the same skill invocation.            
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
        type: function
      - function:
          description: |-
            Register chunk IDs as citations for your answer.

            Call this after searching, with the chunk_id values from search
            results that support your answer.            
          name: cite
          parameters:
            additionalProperties: false
            properties:
              chunk_ids:
                description: List of chunk_id values from search results.
                items:
                  type: string
                type: array
            required:
            - chunk_ids
            type: object
        type: function
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