haiku.rag/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_docling_document_structure.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 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 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 = . 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 @ 0.5-0.95 (%).Law = 86-96. Formula, triple inter-annotator
        mAP @ 0.5-0.95 (%).Pat = . 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 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 = 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 @ 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 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 = 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 (%).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 @ 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 @
        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 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.'
      - '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 $_{Caption}$, $_{Footnote}$, $_{Formula}$, $_{List-item}$, Page-$_{footer}$,
        $_{Page-header}$, $_{Picture}$, $_{Section-header}$, $_{Table}$, $_{Text}$, 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
        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.
        $^{3}$https://arxiv.org/        
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
        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: 3WGkudYp6rl8ISY9JGgFPDHusLqp15Q9paRbPY6uaTvJ0yQ8x/PnOoXwCj0zZX495g2bO2eyUb33w1O9WCtYvYYP0Tu+/6m7GKCGuSwARbrXr7a7HeUdPSwxQTxb20g8nDMmvNFb0LzTkI28Qvw1vEv7EzzfgR48CKOfPDFe37w0y387qnJ5O6s2Xrk8it68w8DlvAbNTroLyuy7/pHSvLfTKbwHawC9pJONPMnqqjxAm9M8RMQEvK72+jumAhK9tZNLvPjwV7xpqcg7bO8ZPGRsbr3CRHi8QPVYPfti0bx5lvc8dbySunUTUzttVT48aUXxO+dLgLyjiy48j0IeO7CvCLyxrw29bv7oOkZOTLysKSI860NqvMCrwDtkY7q8w28svLM0hjxFQus8RQDJvPa1f7xnDwS85D7IOuSyODtpe7q8VfFrugm+f7tT1ck89YcXPcKdwrzw8Lw8YMdDPMPFDzwBg/O63qugPD0KhzvyOE27Ki0SPC6/G7yGGh08nhYju88lPbxZxj075w92O+375rsW2NG8ws1VPVN/RrxLKyQ9aG4zvDssG7zxsDW8fy6IO1xvQTzTNNw6OeisPM18yLzbSTk9cSaEPNaQlTo/4DI9xrr5PClAHTxCO+c7XsOuvO50YTxo4967nFYEPKZ0wDw1bFK9mz0ivIp9arzw1Zc887jluQwBjjzhSdy8I02qPO8DELzulwO9ZJG4PDRfFLvDDgq8a7jNvLzcMzxarjS8jyAHvGqK6LqgC9e698OcvJge4ry1M3E8tqBNPBVurLvd0tG6sjl8PGcGqrz8FRA8oUikPKMDaro3TbY8dlHgu4fhjDz0Mrw8dAjNPKr3m7stFle7qdaxvAXS1zvVVvk75TsiPGYWaLvxybw8ZuZfOtU7jrzdeYw8YDvEu9XDkLuM0yK8D4NlvPc+rLvQUQi9P5y5vHYrM7xhflU8N7s/uza/gT04Qy09eT2bPDh7sjww8bG8D+Cfu+CDoLwB0Tk7svYjuwV3vDuOSyi6iJBPu8XGwjwk0c06QIgsvO3snrzKbMg6j2hVPC2TCj1G6RK8OxAQPCJV+TlUUEK8AWwqvDFYSruNHuC52qYzvEocbDv3D/67trrnPMsrgDv8FyQ7sEiEPJUwJbvvxEs8twZlvGHqv7t1B9U8GDcNvEMF2rvi6CG8g1mXvOd4U7wvQFy8Zh+ju+owbjvrBTO87Y9Xu2HzZrze2CM9t0oXPZ6WVryKGT08MyyJPJLcQrxawau7Lx5vPOI12Tw+Nhu9oRvmO0Zi2LzrmXS87S+bOVEqn7y8SXi8QIOzO4KywbxR/hG8SsKxvCmnnzpJPk48hneTPLqVkrwWCg29BJ6Eu0k+PrzBBzK9OAJ5vCziibqgRI06XKsPvYlq47t+4T66fZgrvAW4ijxbZUo8CY0ivSGVHjxs+W27/t4lPWd1vrzLNtQ88l5SPCQA3Dy7na28PM5XulCi7LtdQCo6FigVvCa+QbuDcjw8aiF/vDDsGTuwAdG85TLpO1QXxDxE9IS8m8qVvOESt7ubLV0898+6PF6MjLyGLS07OpNWvG7n3jzveTk8qrEFOllYaLzPhvi6QzVvvBpVibryKDO6rN8PPUk+hTup7GI8shAyPJ1UUDu/0x08Yu3cvN1embpatg886tyluf9VnLqdNdY8UNP5u/GvfDtFd6I8ML0VvFoWkbxThRs8YEs5vQHFSLt2zV28rXlLuxU6sjtvV508QQKcPOHzTTz60Vg7x/ymO7ognzxjZoW9EXk1u/SNDzwedYq8quERvO765zyVUgg5HvIiPPScnLzAMLk8fRSiO30U8LyvFW67WukUPID9wzyG/Fw8jWwqO5m/QDvE5pu83qnVvM930Lz5Ye68GyF6PIIoOrqQ8CI8aoYYvIi1lTwuDQa9GV4evP7FxbrT1oM6YncPO69vEL1b69W8IqTauyU+mzwhw0M7l03CvJHURLy9VJk5DF4fPebmL72FJce8rn9QusbpCD0Dfhg8TK95vMQmwjzsmSM8OtMoPVDAtbx/pbO7RQOQvCWSL7xT4l6600GUvMb4dDua6Tm87xGWPFx/1Tu/7qw7gqlIvPo16Lx2FL08Z94PvL/ycTu8iZ49pdSevHe+g7w6+NO87qwmvYckd7ygzAw99rO+vBjwgrw0pio8GtFruWKUF7wxVyM8S6WGvI713juxKrc6sEiLvYDng7xZT/87XaArvOwpprsvYKU7DzIkveEcQbwd/BE9I8F8vLqlp7zp6AE9+L35PBnonDz5ZsW8zUWAvSbo8TvrBvM8fIDrPIL2Zztkwqy6UYwKPEx3rrzJ/4a8z2YuPOyteTssCS88N0p0PBnmirw56tQ8D80PvDJuFDsxZEU7vFcAPIIIETu4r9i8QgFZPNUhD7xOnQO88baRu3BxobpgmiY8n9ZsvNjMbjqHUOi8FYS+upXNVL310A098fcLvInexryJ4ES87apaOt6gErzSebS8xKwAvWw4NzwtbiI815eLvO1/xTxx6p07qaRovOp7yzsRgoq87k5IPMxWBjzWzEm86lACvBb8KryO7RU9nqtHPCVqgTzurH88cWSQPAyojjwMAxW8mt9wOZmFvDxSla+87RQKvURjczvuO8S7KkzQPLfnDj2+j4I8KoyWPHCHjzyt0ua8j4HSvN2riLuo+GU73ThDOy0HJ7y/Rbo8Uo90vN011TsqpEg8jKmOO6hrlTxARti6Q0ZqvIp7QDz6dS87hmATO1HTKLw+eTe8L4JKPHzzGr3CjFq8KMnGvHdWv7xh4FE8/mmZO6zBjzw3eaE7G62PvEyBkjvAQo47vJ/iu+ergzw2KJe8WIyCvGz/FzyDwke8/Ne/PNeijjyrXOM63SW5OzGsB7wGRtW5R1svvChECD2IcEs8QSQEO7lx7zxWzhu9f2nwPMMx1jvnBIG7O1yVuhxN1LzhEIi7SeN2PDzriDqMRUI7I+ROPPX0szuxiUe9EHGHPCvACj1vdQA8yTSyPCaSKDygDbg81n6sO+Q6/bz5z0W8aThxu9zJozsuTYY8yc4IvPaeCj3fCzQ961RIu5d2UrwIZzS8OKEzPB8G/ju/SdI7CfXpOpBmBr0upru8U2Z9On1MrbtSVD6842dvPPcs/zvXINW7/aMHvWWGRDxfGpC8kqxRu1JM67x3ieK534QlvclgML0g7L+6Kx3tPLrdeLyRbs27JP7cPMxaC7yw7nu8D/L8PK2MLD33Jeo8L9zoPO0nAjuQTok7NsjTvI10NbtBtr28s9emvGFi9rzwGXS8L5wFvUZS27i6t9i61z+xPJpsN71QSfO73KEYu7AqkLzmJCm7Wp8avSXTi7zLt5Q8qZkouo5kFTyCCby7OQd9O/fiDb3+QM28iQ+yvAzEUDydfh470qbKO13n8zxarZQ8a2KlPOgf2LzEIzM97dsgPNfAEb0C4A69O5QNvN2F8DtEjZi6uuGKO0Nmkrv2Nrw8FwrXuyrKgbsBtKS7NZsvuey6dDze4W08OB8MO3UHGb16CgQ7RW6yO/aXMbwdATG8NnEHvKV+uTx0XIw88RhnvL6+sLvZpZg63dPLPMt6NLmt3Oc6pOn3Or900bz5nJm8aGajOvcIiLy/NaC8CvdMOzRP6zxvxCO9WbKluccoiLzoJ+C8IGEoO+T6BjzYnwc9WnwPPNFNRLtW10I80h7YPGWvV7yA7RU771T6O3SPCb2gnAO9YezUvENhaLwtwmq8a0vhuw6fCL0FmjY8lXeNvDoQsTs0qy29tEKEPHXMpbtuTda7G9X0vETUIbu6gPs8OnBFvAbNj7zeFJy8oVvTPFn3wjtcJQa8d21FvDRo4TyeCgq8MFCYPJdgiDskHKE8MnS2vOPShjw4now82gkfPQAvKrwD/ZU6WVBpO90r0Dyzkda73VEWu/yFCjxpoE28WREpvdEVLryBmLM8y7IRvT+hfjpRRj08nOOGPJsJAj2ySya9mgwZvH+PBD1MO1O742CwPMO8bbwa0yo9Oa4BvH4PAL3wWoo637ADu+Av4jssIoO7ZAjHPA1hmrzXGfI7ygEOvQzLv7rlMrm8hNucu7/nED23W1y7hCC7vFgpQzwLFBa8pZARvFMtHzzU/pc7lFhUPQ6JdjsD1rO8E7yhvNgNKT1Ytc28DB87vKDeBjw4SeU7fzmAvAP9XLyq+lW8SiJBvKoARDqzdMc8U6qSPCvGATwgpj48+p0vOxrEOTzycZg8d8SnvA4k4Two2e+7HoKYuxCmgjwz1Ug8K1HIvEuaezwJnIM8Odyuu97+Bb3r2PI8cvzPPOg7yLxpOa88iLwlvLiDSLy7Nru7KmWMPIhKurwzsiG9gIvqPB51sDydzjQ8Q4mdO4HbLDqb14o6ShJTu68GFry2HAC8hYswPKmRnzy7jAw9Z4GHPH3J+TxHYV08m2jPPJvXKLtnks48LCFOPNf7kDxzyJw82n7rvKf50jx4+qi7iVLWvEnborxP/Qo9hCslvA4GkLs+/X68AOTbOlUJhbwzopU8rsvpvM4iGz1xmJw9YW/mOjgevLxilIs8C/aAOxZ5GT2GXYK7v7iyPMGMfbyVvJw86MzqOqNUXzx56CS9L3G3PClG2DqJABs8jhZ/uv5zwLziL8y8lg7zPCn/HjwQgSc9a5UsvKGYCj0kGLW7Sp/TvAqoLT0zdL28x3YbvHADDzzuhOs7I35XOxR8XTzAH7U8NnXSPAkMe7z7bPG72o4RvV/uvDto4DC8wuGgPLsoKTxHT207NtKRO2L6rDzX1LO7xCY+vbmvgjxS6oi7nmubO9fVzTwe5dO8izSkO2tB3Dz33Ia7pI7dvH1zqruyRpA8ua1OvAmLHr2BqB68Pe6Su2mn0zwZGgy9FvLCvHC0BbwmbL88ZH24unDhmzzYaF48aWeRPKVLnbzqJRy8rpJ0vGE6Orz3GG286qk5O+z0rLx5z4G8ZOH4PEzapjzBSsK8e1Duu0aqBTylhRi6nYoAu/OCGDyi8uQ8UC6RPIWdf7zLZxQ9PzEFPFivkDxcS9862ODLPOWTujx190w8rxSJPKP7hrsCITk7kglQvOs4DL0taKy85qmhvAkXfb168Xy8g3rjPCfXdryRvH87XSwDPD4InzwI6nq7sYFdPINInjtkGyc8asaVO2oDLb1/2pW7Ny8Su8IpXDyyYHC8ep3turprh7z91fk6iDbCvKkFDLx474c79Xoku4j6kjzFmLe81aibvKGdoLzyVPa8sKwNvFYICr1vgik7ounoPOfpljzGIjG5rhOuut2r9DsWKjk8xAzMPIQSPTvS/ug80wN7PFqW2LuU1zi8tk6LOxvOFL3qsAU9GfKMPH+tebw/DyE8EqqyvBPKIjy7l966q9A7PMqShbz0t5m8xvHlvEe35jyf9eG61k9LvKNt77yPAEU8bk/BPDmxfbzJSAg9oum7u8eKUDvcPbk85lFuO6fFiDvn9Z87eFtlO+LYLTy77R870NmlO+aglDpTF4g8ZoEHvR4lt7ufiyo9sw6+vCtrsDyhLTk8ck++PHDFkbsgqX28ceUOuxIInjxxq9274oBAux2e2jz2/pk8kpXlu3x0oztWPYm83h6bPPsuRrvjUqY8pSXXPNVNGTyH9qW8QvR2uv3cwzxkYr88+8T3O+bQBTx8qQy7WHhsvOqZIr3cMBm8xpMzu1XwgTw6vai71XIBPO3+yDyKMyC99T1HPXG6Wbzi2LS8892au87I1LzmwoK85K/avNf9VrwY2nW8vZjovFaEJTxOKEK87tGHPNPgGTwJAOE7D6ZiPONsF7x7wRA8z1zrPJQB7ruk7c27qyHFPNqU5ryY6RI95gzZvI9pCb0+XKM8ARIcu6uHxLtSCD+7ZzHVO7e9ybsESTo77JCPvDAIrruki8o8/TYuPYATYryD3tw87BhavM9fIbqfBCK8Wx6WPO1BKDyFs0O9v6FZvHiT6bxO1nq8cmr2vFzzQjwgGTM8vtAMvXqk4Dz6DjE9gehcPLAndzzCqrE7a7qAPKbvTzwuBqS8f8/zO3Jmg7w6kkK8hEp5u0lzIzxhHEG6sdY2PFdylzwCPro7noyyu8QtDjupzro7jiDtvMSDMzs/T/06KmycvGFmJT1aERU80JI4PNORibxlW/y7R/M/vKYO1ztZorc896tuvJ6oO7ww4f47wX9IO/NoirtY6Y26mv3vPJ/OUzymXgm8aNDQvNPIFjvPQcg8RYFXvJ4H9zp5+Yw8dk7BurqXtbyYYIE8cxuzOodub7ygNow8JjcGPIBFUryd5Rm9fzapvF2prLz8eiy7E/EKPR8EU73MxRW91uj/u9xqIjtEFpS7GrwsvLMYajxa51A8IJGuOy5Ncbov7i684lJevFvci7xDgbK8saoevK0zKL3aumg7EKPEOrNNlby14iK769dNvI92Uzx5uzk82TaovCvApTzXa0y8Vmf+PIFg8zw6hCk9vrDvvDGlAzyoqqQ7lcdKvHH3vjwt+vc6smutvDkKIrzXS947SXsJPJqw3Lsbk4A6+8KrvJSnX7wVxAc8PZD8PLlNyTvYzHc7cUx0vHL3vbr8/ZM8ElwrPdqDBzrlGoc8F6E3vLXpq7zoPIC8yGPiu3b5Wrz9kEE8jKrEO2Kfl7vgtJ88GApVvEFdJrxppow62lYXvVrYh7zQRCe99lZGPL+mCDuh1CC7BhxSu+Zoyru8Ww49B5MUvYChCryxFSM9fbDavJ+HGD0Z2yG9E7I9vGhvSDzKZZU8btMWPcCiUbxvvyO8QSAQPVC5GbsVQUy8xrWePAyvyDwXz868tmKduTwCH7wQit+8wRE2O/QF9TziOs08Pj2rOxs7p7oKJ847Lp+ku+7iQLxWCm+8m7CUOllRvLzUlfm8+bbTOvwmhTy5LkO9U76avMT8Nrry7IY8F+YSPH7Yrzv84QW8uPVLvJWMG7v0Gj48PfCuPG6TnrlXI5o8G2WhvDEjpzyIKVe8iMnsOsmqtbzPuau7r7eouxhDDz1hV9o7XNiaPM7eCL3ndog8PWSOu3/8xbzr7j+75visvDTvN7s3TAW8L/O2vADJibujw5a8ceOrOWVLbbvi3Ts8SYYBvUnV+zyrYZ08vS0iu7AumDwyHJe8S+YxPEmv/jzqyAS7+NE/PXIuhb0wg2a8ImaDvRKoILxQErw7IMiyu/T+xDy56Pm8YGWWvG6fSjyk4ou8vhq1PPxYh7xSJFk8/MocPU9akDw+niy8MVvXPJ7QAL04Xhy9bZwAPHs2Jjxb/DO8mOEFPagPcrxjA4g8eNQTvMZPDT1l/kI8nA6Qu1fMizua2zu8icTquxLAPrylDFI8Utm+PASMwzkI0dq8CUWyu67MhLxFVBg9CnWPPFtW5Ts6FRU7K7uaO/mYpDyAM8I8/GASvTYTnzyrCr28OwfaO4Wjlbz/+hS9m5bqvMMBAj3NltM8JJTpOy0bQT3xouc7o3PvvIPOwTs4mq65xq6LOzd8c7s1Png8DgCQPJzRkLxuTLg8C7OuO6sK9bwF3m+8C1UROz4LmjwfgCC84JgCva0fcTyiXTy88huSPHMfUbyotgg9LYwwvPGSGrspPhu95I4cvaOcHDpbt/i783QTPI6Frrus9QO9OJqJPCUkvTxuipi7WN/cvCmw87y16T68SRHiuR5E+Dw8PBY9YdbSOwMOtDzphj09ZjQkuv0nErxRGuc8VI4rPGOP0DsSfeu89bYEPUfVr7xZRDW626Y3vJh1bTsqY1u8OgX+uNqiJbxHWuY8MOCnO3c/Kj3ZKAC83/0ZPYZrmjw9qp+8okhIPYd6crywIgs9QP/6O2nbeDv7KIe8P3qSvBwa5LuU/IK806wnPD+/gLppX487xI3mu5/1Xry7s2k83rX4O1gfOrt6Lj48xM/2vDMZbTrEifk7K8u+PEVxET0g+xu92BQuOY9wLLm4LDO7qCPhPE5Hl7rKMji5z91dvHBX6jtmzbq8KvTgPJHwijqjPFa8cwKLvKdeobsvlsq8zH2rPDv+OryiKi695zTRvOxG1Ty4TI+7EhMWvGAc0zsBD0M8rGmwPA6htzzP1rc7zdybPG/TEDvD2w+9L0MTO+Axrzx+mRu8aLy3vD/HuzsfJYW8P041vbvZ8zxFECS9LKfqPERWiLxEroU8ddNyvJ8IFD30hl08kB02PCbckDvn+kI82aJ3PAR/Sbr6z1k7cMoqu9jcOjw+wAY9XV1DPAQUkzxL0QS7O+sOuwbiczwEW6i5FD15vMlG/DyXM+67DjVGu+WIr7vyMJc8cNOrvDQD2DssRZC7WNoWvTuy9LsNriE96TycPJ/3wbjGh4K7vDCgPFHlST1QWz28REaOOg3JbDy5pzc8aGh7vHcygjzkwhq837DTPMjsjLt7FKA8EIA6PPo1gDzmC906iA/husPu8TyJjHm8R8skvHFQazyQr5E8pTYIPHnCpDqUYRG9UokJOxQ5+jqJz+I60L0nPbTjqrzFLaw7jLajPNW/7Dz+1f87jPF7u/PQBz3A2OW7ay0TvPcIq7w2dYY87Ep8PG+OprxcMg29UhCWvL1l4jzA5Pu86n1uPBoye7wFu/S6sso8vLvFu7thUyW8kG6Vu36QmbtX4Co9H06nujap6bzBRyK7b9DjPJVuqTy/DA08oZwFO58UVD0/IAQ9salxvLyGYrubBmQ7QL9+vFx0MTtAx4i89+4iO2IMDT00IRO82oZGvDVZozt/ErO8TEiHvKohELzWU8Q8NSUBPdojd7w56548d28Su7UyQzz/aTu91uLBvGhLsTuLfGo7637XPHY8fzxSiYe8Dc4fPRBt7zvJp507weMCPae/rzrx+Ns8aiCFvA9pyLzkzLU8I14sPPKTkjzczle98YLuutS8/jvBB5m8+f7DPLeqCLzwgVS86+ZNvJdC3jumJUC8w6eNvNj0Hzx+sAi85Nq/PHjIOzoJU726eyvBuwtY+rtKZvy6tCBrOrw++bwaFO666cUCPd8GXrwxvSu97hHvPKGmkjw8Alm784ydPDT3LT2R5zS8bcaZuYSn+TwhPYU8jPR8PDmVzTsuAXu8gMYNPIfEBT2cyfq8NaQKPDTFm7woYwq8jHM2PFusOLzfDSK91+ZUvPch6rvQAjm8f8h1Oyz+LDxzp1S9ylcGufewZbuJS8g8rYoyvRRuBjxt/Ww8MZvLukq21zzpHpc8XGtwvK5frTzDwVi8W2++O0oflTsiRqe8GtSRvNFtDjto9f06W7SQPOiYPzx9r7I7p7VjPEElkLyrw8Y8PSl2vKIpAb19+1C8mvbEu1xYxTwwaE67TAGLPHyqQ7x9qLY6d/ZCuyhkrTw3owc9rla0vHhikzy/ge4803YNvOwNBDvXoNM7/csKPM81pbyypRo8hV1WO6e9Iru2Iuy8UUP2PB98Urkaln88xL36O8eoC70TLOe88FaCO79rXLsI5Nu8ibRDPIa32zwtkuA7d8BDPQQYHbwiFdS75tIRvAzzEz3FyPe8GYEFvQDwJrwn3Ga7jynevNeKWzw85ug8uF8nu5LMHTzIvLy8uAZLu2BEgbzDC/q8OjoSvE6JNLufXNK81ObxvAjVJ7wrMA29VxSqujlYb7ynfe88Jmo/PHL6B7tafum7KSayvHAOpDztZBi8b/gdvKGgkrsTgw68TnSlvEKccbwf1s27i3K2PM/tzjzFikw8pCSRPDX8Br0sVVc8pcg9OjpDgjxpNs68yGT5vPspprwMkHM80pNNvIdPHT3HTXk8mkDYu8ImDbt1MNu8Ei6XvFExbDx82i29jamGvLmpCjw/tru7OGXVvJFJCjyK+Kg8dE46Oxj9M7zEwhC7npFavJJlmDzzg1475symvNCC7Lx8YnW8sVUEvG4EfTztoBg8kxbGvLdD4zwcoBY8xfsEvNNiSLrqAz87ia52vBGfhzx0odm8qISLO8Q/vbwcd6C8SItAPSNuqjwYJuG7pFiHuyW8m7zcOM+8Jk5JvYtATzqTwT+8KoOwOG9i+bzdXD+6p6Q/PNcRKD1NJqQ70ZNCPP2zwjpSleA8tAQtPQ9ZZTyKS6o8xKIOPD1lmrz0bQe8HLcGPVd/qrmGJgy9PmxRPG2bL7zbWeg8pYSWPJB7+bu0jxQ8bpaKPE5pBT2ObOA6cUaJPKrKLrx8zb68d5pEvYA2Iz0hs9o7yYgSPaUPcrvllgs8Fr2ivHOPpTwIMUe6jVhBu+rSrzr9qPS6eGN7u7hilTyOCsC60xY4PFeGBr0jjnS7mydPvA11HDvXRig9n2zkOyrcKT3MrTi79qzJu1/dmzzFQ6S8qMVpvC9hAzpqMMC8Hib8vGsL4LtNnXu7M3X2OxV/ursT+Bw9z7A4PHZOM7z8RtO85ck7PfdbW7onbta6uTYHvDtAxrztL2Q8CwRSvNLtNbwwOhy7TG+FOtGtG7yv7fq6qQV2PNzvmrwskiQ8lsihvJvQh7wOXyq8Y9bkPDbqqzwvLRU8nN0mvX7nvrwC9aW89bjMPKtfh7y68G29cPLNvAr6fryqjIU8eGSOvFEBKLzo2m277UNPvG7BPLz1cV87UEhzPLbf/zzp+jy9Y+Tfu+tJEby2kAw89CEkPEe7qjuMqH28ex9OPNrvzbxuPQo9wIGzPAT0TLujAX+7jbtFvVbPBrz86xg9RmjyO5uOUj1H0hI91mcIvDzrrrxgsCq8z5K0vLKUX7wQY6I6LUmIOzxuFz0Pi2A8IEkIPDf937xo+qO8Q8WyvIRLibn9qQI8WtEHvL8KC73cUsK7oWYgO+cZhzwo+Ba99VUuvD9EEL32Ce68cC4KPJM8Wjue8sS8XNeOvGsLrDyRj9s8c4Edu7kKMry+4S26HNUfvUc0KLwrIjU8gqPjuZMTwLyHqJK8GUftvBDhYLw6uP+80xo2PRDR9jxkmTE9+mSTOnpOcjx+jw28JUQTvIf1KrwignM8eYeIPEiQU7opMxs9ANLFvERznLsLuQK8rVlwvJR3vDw74jM8m4KAPK/isLw/Cv26NxwkPZADpDulCWa7VRytuzh9RzyDUOi8qVEHOzyyjTxNXAs7B0qAvOTCBrzIIXc8bzY4OX39FjzYqYE8WPpAO9cYjjtCtpu8Yj+PO/DikrwCm668saySvHXmyLxX7OS8a/XyvANNOLvfUxa9t4WtvA+XPL2hWbu84gOhvIuaejxDTaI8lJkZPbMHpDyXFjI5lD5FvT6eQDxGjvW4HD98vHhMUrx/W8M8P3xnPJPcuLzNA7y7kyGnPPoSg7xMJIC8Fw05PMNBkbxBGd+7fWRKvA7kEb2zNl270DKbu0v9Hj2zc2S9ftfSPDbrhjz0xxQ9HPiAOP9QPjyWm5880Nk7ugp4arz/Pb07440Tu4g2ALpv7hW8o0t2u2mEMTwb5/S59c4svHDsJbwSZ/u8JK0nPVwhU7v5GAE8cDObPAk0pruDJdu8przaPPQQCzwPYAc8ZjdSPIkeXjyDBwQ9SpMVPVHgcryfsM06DvAWPZNsMby0K+M80H+ZO4TqCb3gmKE84OZvu+W+ZLsg7UG8lhOfOTboP7wAWPW8CAMDPDnXozv2Riw8JHU1vE2LjLx/t3u8DCskvMsM/TuZVqm8gPZUvHitxTuPLkY8/lmJvEdq2jwBWSG8tgUiPDuq2LyC+ZK8r0Spu6msjLyHABI7BCTxu1uuB7wSJV28md5QvBCwxDxlf9I7gFL4O/pUEDwtOII8U5oOveNn/TxxzFs8PymUPE8jDL2Ge7u7yGeTvEDlArxi7aU8wgrNvP9OdDyMrpw8B150u6Q/dTw0aK+85SqNuhYPmrsiNyu8JzbeO9Jz57vTho08dK7AvK943ruyrfQ8p40KvcMuQby6ayw7MwpguycQbLwMW7q6Xn6CvOTXhTx4xrY7qSiAPBFZCT0W/fM81auDvMaUwDsWy8e8VvUWPe6Gibor9mQ9/T2kvI8By7zkFfE7tg5Bu5wHGj2Ab4W7OSghvHpwD70sQQi9HKvduwTHlTx4ZR87ReSQvKUSoDw4UWw7sEQlvIFAnbz6NO07qqobPGI/nTuwgpq8LODHvGLcmDz3yew8a2OhPPAZhDzc4po7nz6bPBTcBT3l1rK8Eka9PA3GaDwgOvO8k8efO9zNc7w7RSe8oxODPEosyDuibck7OC0YPWnsKruRoRw7FvxvPOctBL1F0Uk7Dbv5PDKbYbwS6uc7MuwfvF/XALwMbRW63kEVvPV3erwRDzk8D+woPOEqCT2ouwg9KK6NOtD0jTu+vyK8CMEwPMuekrxMlJu8qeJhPGb65zzTojk7T6iIO+txpbzPrW07d3AWPRgfpjzMiCe9sIaUOysLirxIMjI96VHkuxn4VbyepuQ8vEf8vOIt5rtBdX67WrEkPCfsbzwnuae707QJveWRHDzERhO8qpOHPOQKSjt2CYS7YMvXu/4lfjvfLZS8Ki+oPImVkbwZumS8EI+MvPpNOjp3I4k8ZBmwOxJphjtkUKS7rtfMu+sK1zzWL3y7fhHCPIAJlDqVt4K7fTq3O+/4Hb3nWGy6eib7vEpHlbxhfLK85KSkN17wQryjfNe7VZDKO0CiELx3Rz29y/H2PMqEnzyN12g8JfqTvD7zmTsviQS91YqZPIhz3zzaFxa8XHqbOwxgurz0Apy8k4e0u3rSWLyTUqu5QUDFPCKOVLtPtRy9EusGPVNNOL0l6oy8VJAIPWUiRbw3FB09c9uhPHvg4zyW2i88eu2bOnsAJDwE5o27ht4TPBLzDbxKthu9N52GvH1yozxmgiA8sh2dPMP/YLsND+26Lnf3PDSfPzwgrp47ndWCu/dDgztx33W6zEo7O1rXgzoSf+G7mq8kvFfrrrztQo+86ZIsvJpqnDsFJlc8P5BHOpFnDTv3Q927pPpkvA4Z1zvni8O8arsRvFHnUDxLGA08JmJEvCWCwLympSo8o28rvBAh6TyScAQ9bSGjuzjlH70uhYQ5Rv9JPBqptbtpH7a8w8LSPKKTKzvhs508GaOzux0sOzxXcpq7FYfGO7yT8zv8j0A7v6UZPZoxsrznZYs8sEjTuiIEHzy8tIG8mJSEOn2wGL3dp7w8L0eMvF8gbDw8GIG7yWOnupvyorrtzMm8t4mKOyEbCr2jIxA8ZoIgvALlujw83ok8BhIOOw4nGTtrhpA8ukCGO1cIBL2z7qs71k7JO0Adx7w/Fbm7vVeaPGma0DzoC4A8024JvIecZbxFX3W8JcMbu4h6/DzAFBu8My20vMfwvDuJeJA8BNU/PZeCors0yYa8w0GDvA31NLxZ/sY7NWkNvKNz9bxfana8nli9ugUN1by0wew8mgQUvBJigDtNE0+8LK6UvGkvkjxO4VS6lW/FPDIiMj2jnoy81ZghvKZ8VbzkkrW82Ee4vPtHSbxQFxI8Pt2NvPFDprwhIy08r3CmPK9/6TqMipa8R2wXvTxBHzwGk0o8b+YQvDad0rvub+G87TDYvCxUQzyxnZU7IevtO7APuzvqFv27nou/vCOFdruMqw09U4LQvN2Bf7txss88jl7svMKmKbw2Scg8e2YDN+eEDDygNvk7iLmGuzdZartov8i8gmwTOZPAnDy6C0a7KDgNvKre4jqMVti8UyQlvMLJAzxepTu8V7MCPOwCC7ybH208RB+bPCBiPTxld8i7ozq6u+bjXbyzPsQ8+7iyPA==
        index: 5
        object: embedding
      - embedding: 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
        index: 6
        object: embedding
      - embedding: yVjIuRuakTuKVwo9CoxyPC4czbre5ZA9bLlFPQiBELwIIRM8VImFPJRHOD25ij09iVL+OuugQr0amCO9/tppvfhKZDw3vL+8i7gJuLr+wjqgqK26wLasPAhrr7t4vYs8dPfcOpzlzbzDUZe8dut/vAPzUjxrMVs8V5AlPCMvrrwcYik8LOa8O7MHQjoocJO8ZJKBvOeHWLpBHrk7AL8FvQMjnLyNcCq9uKNyPCR+rjzaN2I8jyzsu/rkjTsWnoq8SFdfvLUO07vvAbs7B8cHPPvte72WIJS8koYVPSYaZ7wIAQc9R2sFvK+QNrxmVHS6AHRwPHjZNDoTJd47KO7hOpBUyLvSDs28JTcFPFrhvLzSiNA7PHvQu3LpmjxbKw69aPNCvD+WwTsj1wQ9nkKyvD7Mfbxsayk7zFX8OjMcADy4WYa8YTpmPDbbX7zg76U8OcTaPAg+grxFWRg9R7OaO1+NrLx5QgG8yJquPORANDqtOYa8bviqPIyP+bv1UBE8zP5UOzdPCbyOQ3O7omKbO+2Id7ylY+S8c1QnPVwdiLydtfs8z1NbvJyUPrySGJ67BwNdO1VlyLpL2Ae7m0aoPIoYsLwOcBQ9pXW/PNk3vzlnf8s8blPpPLKe3DvNJ7I7IOtavFT5szz6B4a8CkFHu/JG8DziaV69YWilvA2XDrxwle88d91DOgZT4zwYuhG9MVPmPLqchLyZ4Bi9nwkYPFHbgLv7bqS6YG7fvDhcmDzlbri7PIHgu1WNsrvo3rS7HBWFvFt1Kb1Mg687Vl0yPMXH0bv7HCQ7TMEQPJ0V1rxPbAs8rGaiPPm8vjuzyMA83DnGu1hM3Dz/6ic8J/udPB0YKrsJ0RK7RxW8vEvYLDz6tFA7e5iLPBya7btsmbo7CwsGvLDqvLxpL3k8xaXNu3fCRLy2NHG8B9JyvJ7OAboCm8y8Ra7Du15Zhbx6vny7Zqw9O9FzNj0kCkc9Nv4SPMWj2TyWelS8zrfruxpwZrx6ZPA7sH2ausCqj7txOGY7PNjDuwIEozwHUom6K/1OvA3kjrwfaqq76IW7PLtlBz0Jpjo6RdQjOzYamLyCoGO8SlS8vEpyqzsDogU8QigTvI8nZLqF3p27zJHePBGJEDy+fRI8e+z4O2hBqrs2Ea48EE6wvKSZX7rE8lw8X/KXvCANsbt/1UO7nvmmvFfgiDpGNqS88gMRu+6sDDyBAFC85OUgvFr2Q7z/5ag8SeQsPZLGoTkMcUk82Z90PNhMu7zYBgG7IXJFPB9M0DzqARq92MKcuxdy2ry7J5O8DkoxOqnxorzMl5i8yg4LPC6QE7140dK679eEvLfoHrwWZG88Oe2XPK0Vnrxzlvu8jsRXO60ZLLxKaFC9jsaJvMSrYbvlJ447JJgLvdrBCbznZNm7Ksgwu8bc0Tz4djM8/KxOve+JVTvvx/S7RVAyPUQRk7xvNi88/Z34O5+HuTyb6ry8BFUhvA+hd7utYiw73tNVu8RwXbr6Tqw8OACHvFDpPTtB5qi8XImhPCxsXj3UK7a8UIvwvFYlEbuPenc843PsPE6qebzRJPw7+C+PvK5WuTxR4vA7EoSzO+ReIbuQs5i7obo7vM4OWrtOfUU8guFaPVIOwbv1GR09mt0QPMKLl7oIlxq7EC08u4J4x7tGAI87ODsoO60OI7v6B3k87+uBvDNZ57tGYiG74RcevKKufLzIq/072Sc0vWmXTrxfD5+8L5qLu2n4JDzunc0888O/PCgN8Lr1YXY8bJ0GPLrPfTw65G+9u7Oyu9eIPzwcuoS8+13DuxDWqjxaOqW7npmCu9OI3LwCXPI8jjE0PPeXF72H22q8msO+O3tD8roUUUc8CAVdPB99CLmQzPm8qwsOvVoFB7ybo0C8gmGaPJVbz7oZeao8B4x/vMWBzTt18zW9EqiKvHQW17oO7uE79XAXPKV7O731nPS8HkJSvJxDqzxo7ok86V/zvOs2krtv/F+8YmoZPes0vbxdtUS8Ks4avK3nqjxwfaQ7OWMNvAG42jxzMJ88Fa0UPayno7x/AeO7RLCAvEtljrqEv7S7fTyRvC4qfDuLnTe83qyoPN2RjjtbExm7++XGO3ML7bxZI0U8mbYVvD+X5juz9549s2UJvadm8by2OJy8FxwEvWftXrzRwMU8R/WzvPfrerz/XyY85ffvu7dehDs7pQo9W/SLvDk9BztesYa8Bn9NvbkaYbzOyS88xJ2SvKZ41rtgsuK6dEISvZSnFryAcRM9DB0UPNs4hbwaoRU9CDa3PJ9QpTxX1668PbU+vZ1GvTuQtCU9vE7ZPCYnjzzVgaw7xTVCuqSdV7v0Cq676nAvPOTJ2btuEfY76rcqOzdKfbpXXLI8U+zDuz5uFjyGREo7xwubu40RfjspZJc75tB9POepj7znYuM72uWCOhKyFLxZ/108Wp2ruwOOB7uc1M68SzbWOzQLZb0Ct/A8UQEVPFwU5bx6KXK8ksQYvKAW47llJru8x7OcuzTpzTwrTLq72CuBvMlqDz3Nrye8gnIbvL+rk7tVQOG8Q+J0OyZVuzq/Rqk88K86vNRrf7yR+Ow8obGdPJO4cTxdfqA8UkEyPH4hGjxDIue8di2AvCayuzxdaBu8T189veAYgzsLr4K8AT6/PMdJMz08X9s7a/icPMdC4jvrTqW8tjf+vLzSabwlFoC7yi6RPDuJIjw4ssQ8dAALvW7V5risers7RRyEPN6QoDz28+W6KDJOvBMCSzsOtEI74dg0vOdBqLwFiiS7ZBnDPPM91byvtcq8Wn18vN7EfrzPzak8IvyzPGBcvTp0XIO8a+YVvHsYhztul1w8RQzYOTG8yrkRCe68fpvqvItgTzuDV6e8vH7WPCOLjTzXheC6g7MgPF2nXLxQS/G4WL7Xuz2ESDyi4zM8efgevFD+BT1/nUC9QUcyPSJ9DTzwT5Y7mtwQvPt/xLxo+U06X4kiPC575jrqLY48w5WcPOlE4LvRaUi9NxgRPWMBCD3ufrI8ktThPBhBh7x+L6I8BjL8O4dIKr3VW0S8NEuZuyHECbyJCBY8w/GAvEMy0zwTPCA9XDk/vB0D/Lun1/G7Nq+EOxSI0zuMvzM8AWWHPJTulryBEyy8gevAu3RsrbyIH+W7X58vPMGRDjz5iAS6ZlGCvI4+iDzzf4C8koQzO53Zg7ylC4E8OznHvGsqQL2a0IM6Me5qPAjSk7yC0ja8KcMtPZ4VnLu2Nsi8CXTnPIqxwzwKBhc909cSPYyqXzvuTbK6gSwGvPrP+Tvcl4+8kg6OvJdmDb3/oFW8BoMCvWl4lbqItuy7CynVPDvvKr1hsDy8bsrFOw+st7zlJ0Y8BbzcvMeAEL3d/sy7+EvAO06AzTyOtXi7aUoSu7LkC728Dfy8MYK6vEO54zx2ih06cLmTOsyRpjzq9di7ubSjPBFMpLy1FDE93uxHPM0Vwbx3BGu8t/tfPCUXgDxxIZq7CZq1PCfm2DtuHAA9DRhcu1I3/7yowRU8b3oVO8KqYDzsJRU8AqUKPGDPFr2jAa07MH0dPOuYvbz3XYm7+5uTvOkIJjweOhQ9cd68vChMYLwJyqk7zF0/O+kxQbv9UiY84bsvPHLnpLwqSMo6RCH8OzdKW7xoIdm71kMcPIWtgTxTUQu9txU0PI7Gdbzleai8lIwLPIpfNTzqUgA9yUCNPB4p7jqu7SI8s33XPHc1abz080E8KXnSO0nFGb2oGxi9cqkDvdgUXLxbsUW86lDVuxVOBr3+HcA5uZg7vO5ECTyGQdK8yW/CugEByjvAmDK82AwSvZ57VLw/KQM9EcewvPV947zWf/u8YxWpPJVGCjzxW5k7Sw+mvG5IGD1kVna7ptmDO3UglbpiSfM82iOvvOVybDy8n/E7LxASPT91L7tmm384rl0dO+29Ez1kbUe8OTqGuMGP3Dovrb68qmvMvPsYirvEfak8gowOvdc1Vjse39k8O9iPPO8x+zw3/y29zKOQvMzRAz3SaXA8amvxPPeDDb0RwDI9GzurO6P5FL11nQW8m2dovNljMDpZ9l88TVXmPCv4d7zhqzA8p5btvNs8EboHJry8Fxuyu47tUT1E0eK7LC43vHbb4jrZamu86uUQuywGrDwyRUg8thMkPSTmzjq3xBe99nCvvNzNIz0UDZC8kGyNvKsU4bqW9fC6FnDiu2mCJrxApCu8GBDbvKXU07zuROA8tkVcO2Rm3Tqla5A858ukuzV+XrtO51o8UpuEvKxSiTzRdYu8Vh2qOWRlETwsPCA8612KvFW2Jjxq/MY8N4c7vOS2GLx31aM8BTXuPFDHWbwA6hE9fqkXvP/zEzqM+Zk7BYDVPJFisrwntbS8k+6RPOVqADx+KKU7i9YtPFqdijuoZp88IgmAvF3v8ruRGUI7s5SoPK7JqDzYaB09IRmpPKbYEj1iOWo89+7zPIBHQbg29OQ8GbIFPBcIsruBTN08v6IBvY++sDwgi8G8tc6XvJ7ukbyekg49lcH/Own2WDzO5hO8RNahO0ZwLrwRJbQ8ZzmsvNKtET3NaoU9M+xhvMTjgrx4XH084+mDPIUvNT1vota4f4ndPNr1Q7wl9no8ML/muzU8wTy2WGO9oYIAPAoXlzqqUjy89+qEvGKPPrwzWbm81z2aPOYMFDx72iY93RERvFVUID0/R6s6N7XkvDQ2Gz1hHcG8kjPnvDj1Dz2AvR479x8BvBPriTt9/bk8SCkkPNbCjDti/Jo7jnoGvT3vpDqDxLW8X5BsPAkECDxGGCe6q06ouos037rbX6C5ltHrvKBf9Tyz0qK8oXzhu0p4kzwfLhC86fgPPFMswzy/R1G6SxkuvGfF+bv0K3s8LhwMvIffIr0xdHW8PT6fO/o15Dw9TCm9JBKpvEnJhbyXPcE89LnruzsmqDx67Mg7NiAWu6sei7y3zOC8TfxFvASRZLzij6C8bY0lvPr5Eb0bFkm8KUnMPLzZ0jyiaOi8D5MQPEdJGzuh8au7UO0OvFVozjsjMvs8asZlPDh6CLsaNNU8y3Z4PLk4Ez1sRZE8NH2cPGHi/zwAYeG7rQTnPBXW2bwOeNC6zmuWvOfCKL3znKS8PNGjvJpYS70ABNG8KlSDPFfWObxGK5c8JlfWO//U6DxQ/U47vtbHPJeiCTy4Nos73FAzPGTLsbyGKN26wDAwvBOFdjzu7Xm8gaKdO3BUvbx8GmU8vZ4IveDKuLkyxu25+WFUvN/djDzRe2y8kBt8vKJfVby9UBq9wIX3u3BZ3rwdSBu7NHA/PAFPXzxRCEK8/6W5vN8mJTxMUhs8d1z4PEe1LTx3zAQ9uOiPPLfP2jxZ09a6wXB1OzDL47w8poc8NuZ8uu1h7Lsj3x88tuqxvAodbzwcUaC8R0ThPMv7gDvyPqG8RQShvBQwgzxN9RE8fRjZvLIqDL0uzIE8nkJ/PGhwPLyh3x09xBGku7BsSToGT0O7eyc9PLjh3Tp/sdA7mvpCvPufujq3SwU83/WIu3qPIjvqjF88FsrSvMlQprvwex493bI0vHNanzyMgHM7nz3PPE6yV7sZJR68p57zOTPCWzwHwlK8yXkjvJSfvTxcZY08DQ8/u9o797tKMaG7USv4O17n37vPbRE90ZYyPYBmAzwXeOK8OhZMvOqIiTwOvL08MnxtPEIlN7xREq87llZHvJexAb1U8ya8rJtXPGTtEDuuJ8G7BO5WPCuSET10lPK875bDPFcgRLwC/yi9IOgBvBwL8Lzq6gi8i0H+vMayiry2FYa6XuOrvEm9wTsQrz272UJHPIXXbTwktuS7+SBvPLLdgbvNKdc78kTiOwm+lrs4Ayy81paNPBWgtbwkcB09qOkHvXl8Cr0YI6E8WJenu57g7rzTq4G8z4xwvBJSYbznDYi82ew9vIgJ6bvCw6Q8RanYPG9gOrwFKvA8j0qHvKBnHbw6xbU7h9f6O5sVGTzf0gW91vEOvYWOzrxpUxy8khwLvb2JqDwnhVY7wM/nvI4v5DyIKBU9/DP9urWjijyxLZQ8e5A3PCWrHzzMdOO8JVO2PGxUoryJdCm7720vPBX0obhqyae654CFPGwy+zuiJuK6QyKXuwdSCTwO7jM83LyevCa1Czwnxlm73HEYvK+tBz16Pv071ULhPLEYqrwUxkS8n2nTvLGgCbsplTs8bQ/ku1XQdrzbB5I7zacYO6OWrDwrf8a7n2CkPEj3SjtSSuo7IPwNvKQlIbuCCow88nSTvKRbfzspjxs9QshUPIXwrbzRPp88cZCXuXKoeLxgdxQ9AxAoPKyahbxd7vu8d5eMvLqpk7yMoGA8D+r4PB6z3LzEQCy9DgXpvPFFP7tQVXg749PovKvAOTqALL089068O1Tppbt7NbS8BhAovJ8627wpXDW8AkKdvHl7Cb3y4nq81d7OO1A8Qryo5sg7zdghvdte/DsHioA8dMyJvDh9KDw1nAy789MIPWEkND1l9yI9lw6PvMbI6zuw2nA75SC9vCqd5zwSmkg7bHLGu+bbYLzBDL+7NcndujkgoDtwM2q8asDOvMFqWrxKTo86wuVwPHmmBDxHj7Q7mYFwvCObPzwFL6w8poYGPVvkU7w9iJI86XS0u97CwLyJOjO6uh0Mubj+qrzt+6U81ocjPM47uDsIfaY8qgyHvP66vrsuGIK6X2skvVXCiryJhzy9m0CSPKHE6zsD/xa7vhruOxdOMrwSYiA9Gy3yvCakObukUCM9ZLcHvXNDozyEa0i9XtSKvGIFMLwLNlc8E8BOPWMsVzpdp6W8+H3bPOce0TsF/Ye7cU4lPJggtzyruqW8m8oYvPkZOLxyQaG8qyxuupoNqjyXk7A83L+aOw2NKTwskK462RQFvE4qhryzNi27ECLuO/Di1LvXnge9Ks1pu1A8CTt3PPu8gg37vHjgFLyqzME7NecyPPjHHzwfdpW7UWtBvBgUnrtj0wU9cidhO41f3zknnjU8EksyvWrXuTzGT5+8WUnePLoglrwo9KW76lGUu4QK4jz2XGs8zcqLPCkuYLyXIg49kOZSvESmprwJkYQ7u8zmvMUGFbtKB5s7fqsivGzA/boXFdK8kG2QvC4kqLzmOUk8Oo8kvS1jzjwwe8Q8xktuPPP2oDyATKS8MO5yPNWhvzyugVa8S2wKPbggfb1mi8W8FqIjvQrWm7yOwYe7MrwivNKY9jzVUqG8DBCdu8mTPDy5HPu8rCwQPQwnhLvLpAs8Q88hPR/EODxwmq+7V6TAPP4StLz9/C+9xpGZur43pjsDNJa8oUoCPUUkarw9AIg8jc5mvNUV2DxFMoM7R58mu5VfNDwN59G7ZaF2PKWWr7tR0bS52NiOPO3UuLtmhKe852Y7PNnnCrywJn48fUuDPEgsGruelB68zH0zu3LaAzzbMf48Z4QWvVLF5jw1phO9UNHjPBWPqzqq+O68t9jmvGlJRjySTsA89VoTPKcNBj3vQHS7C1pNvRfjuzv3ROU6ijQGux9OH7yYIOg7stokPFknQ7yymp08WUwrPCH7zbxI5aW8FUADPJwrxTyJHCm8dsLOvFDGijvitOa8QrDuOyDQKTuL88w81p1RvH3dCzyKLwW9RgXZvLdr9rreHHQ7tolmO17M37t8uvG8FlkIPKzl9jqsT9u72pwAvbB+J71wx4i5Xvw4u4jd4jwOQiQ8GR//O4ZoPzzmNAo9LzLMO0lC/7xsros82NahOZ0nbjzeu5m8xGKsPDNcQzp//eO8zXcKPJ844LojU7S8yt4+O8xRi7uwYgM9sqgfO2pUTD0GcD+8boZYPbAxfDxpVsk73TYsPSrj4bnMY988jVCPPBxWlrt3ARC9OVEfuj/MDLz+R7K83SS6PD5wh7kY9767T4yrO1Gkr7yi0T88BsAOuzsxCTwBz108MlLovE4PUDyEZNA8BMapPFYM+TyWxN28p0DJu8IUmrv+x0q8H70JPW+kETtiY5+8OUuRvKSLJTyw6Hq8nfnQPKLOsDr2I1S8AVjtvHFH0Tvj8wC9ErqwPFvYu7y7CyG9h6ZZvP9O3jxo+x+8oPYOPLsesbkY/o48gvWnPIY5zjt6g3g7TfnsPLD9NLx7YQ696fh+u0tvujy4HZE76wS1vA2+GbxXVEi8lR3+vHoFTDwUktS8FLb2PBIQ77vVB3M8HU6UvCV29DzFoTk8gXUgPBpzpTy72kM7DJcwus6BXDuMY9c7wGQQupKkOTsSvq08L7FvPP/b8jzXLNi7kXSzuoBeTTw84DA8nlxLvFFt5jznSxe8m28evPKLWzwYScw8EG0BvbT6RrtwyHe8VOcWvQOuHzyhIO48KwfMPIjZFTzsZBW82qmIPN7kSj2Or527jVAVvCt+kTwIArM7yeCLu3XBrzsMoAa7RVKXPAlZCbwYtbY8zIkiPH7c+zzovqC5uj4MPCXiCD3RUoG8fc51O6EHpjyKiug8WXoLvHsLwrvIu/W8Z0rIOlBBBLzHw9a7xOoDPWTcELuZoVE5NdikPHYnAD31Pg87+zbLuJu0CD13dAq80pxQvMtEYLyMQwU7Vy10u9sYjrxSlw69eKo9vJfW+DzobTC9taTFO4rYgrsZkNW7SDNdu/65DLtk4t+8W4G8OsLHNrx4KSE9exH8uz/3I71kuuY6RGzWPDmXSjxQjM484ISWO792lD07LQI9HhjHvIg1B7w4m16869JIvEiI37zpiZC8wkqtu596+TwXPFi8+WGbvKZYQjotu9+8Ghz1vBoqHLxO9sg8CNWKPPZdCjwNO5s7OfaCOaA8rDsjQBW9Q/XAvMUHHbxhgEU8S4SjPI4B1jytIA69WF0ZPW8qTLw4S4084MLFPACVCrtO+fw82v83vPPq6rzuBkI8UDkoulXrQDxWKj29FOdQPJoYxDwWf0y8CmEjPMAWDrz//vS7WvyYvLnShTxqoEo6WMYYvXPMlzxc5y+8Nm1cPMbUFjx195C8CwUivLKAGrx6Un+8qrDcuwzgz7ypmP+7IiGjPJTJzLxhpeS8SvpaPLk+1TyLD4o8/8+oPGv2KD37Hpm68gmCOtaYDT3C1fM7KPVrO90xVDxcbB28siNtuyQ+GT2Tx/a8w4CmO6Q7nLwNfyG8Pg8bPCvUfLz6/J681hg9vCjcuryzzXC77FsrOwmDlDxtoRm9CC3QPB5mH7yzWAQ9/vBavaOBPbvbxIA8opHGu9JN4Do6V8070s14u9SMUzw74Ka7RnkJO30xcTz2vDm8c10PvDBrHTttkS45qnfvPOoVDjy5u1A8I1hxPGY+6bsYJb88slycvHV+C71biR88UhOjvFZKhzvNSym8c7QAPDiIv7zetc+7vtERvI5sGTzElhk9PxvGvNSj+jum0n48XLQDvPfkzDy5mZG8kRwkPCmRtbwZHTs7jdgbvMP6wLzmuwK94iMaPNA9cDjkjok7KYutu4PqBb3bGhG9bzh1PJKojDv/Rve8T3FHPLdFBz1pYHs7i5PFPGp6Pry6oOG7qok6On756DwxCti8aIHavA6dJ7wzZ7U7c0AIvBMUCDy2KrA8Hz4eu+Bqy7sgwIa8sgeNOv6A37wKjJa8drqWO2j337s+5ae836pOvHeVj7z5G+68UQkLuwSwuLty6Ng8cVY5PDfwwrynJWi7yFOLvPN7Sjx42E+7WemzvIKBWbxi80+8kgvNvHPknruMZ+U7pPbIPGxuvjsItHc892qfO6vhHb2g8aM8+B+3u/9hZDvMpci8HKg5vZ4nsbt/iqY65YGEvIoKSj15oDU8jo4xPCT5TrxrIwu9RXHUvO9OITs8whW9K3a0vEvZijv9O347trUhvIYnRrhTI0c9weEhPKU/bLyd06C75OuiuhuejzxLPzU8TwXAvL09uLx6Bcq82zwfvD6GBbwOqS48pkjEvO6/Jj32OkQ7gdyLvE3RyDuuEIG7oxKQOzRlJzwouRW9X06Su317B713S+K8lbgnPUUTgDw+Z0k7TUQlOzFUzbyxUv+8KQmfvB6PlTtLQHy8khf5OxLVj7xfmcm7rHk2PNQLUj27EqY8LpSUO8M8PjvSdd08trgnPUYmojyDpsY8mw+mOxV0nry8aI87z8AZPT9NqzufWPC8DsRcPPRzG7s9v688//41PNfv7brXwUK7vtUKPOEJ0zxn8j+7yd9jPNJVprwTQM28vnMkvT0cNj2KyOY7+2wRPY5A0rz7Z1k7N9oJvDItejxWoIs7le64O56k6zvFFya552tru6EVAj0f9P47ke1dPMw0JbwxJx+8mOx0OkbYr7pgxhc90QGfPI+1vzwPWWO8lDO/vEVA1zyhBkC8RYmUvFUJHLrkKfW77iqXvGTl1ztRqha55qsbujAgDjyIn/c8FEY5O7mda7yja4y86dEdPd3/QrtfUDy5CteYvGLNQL0vvVk7AMKpvOEcrrwIlhu5RSFDPADlsryYIEO8z+adPLelrLzQsye8m9EovG/If7wB44e629rePLn1RTxYZHy7TH8nvQrtJrz9XvS8zZYYPWStJDvwCG290h9dvJK4iryso+k8kl6bu1lBaLpED4O7SgIPu4WoVLwTxE07btQxPFA3dTwFzjG9NG8qvPGAf7waymM8sQ4gPCHEZDzJTri8TPejPEJa1by8bwI9Xlv+PFd/TTw2vs26WmoRvVPOgbsWKMk8FbqPOwQVhD3F7f08Irl7vHXXHrz2gR+8spa8vDKfuToniMM6Vqwju9PEBT2rgE08ndG1OwmXEb1W5Ke8WH5PvCI2jLujQDA7uFUBvGYe0bwO/mO7WoAdPE6qgDztVtu8vstwvO/+kLwDk5m857agO35eSTz6hTG9oTLsvA9dpjzzKMQ8uTZ2PEeYkzsbLhq52m4pvNn/mLycdf47Y+Msu8pdk7zcMrS8UWyAvHv9iryg1hS9cXwWPc2bpjwrKsY8g4a8OypAS7rRmI68azUnu6SAI7yzuVy7mAYqPFVslbzKvKo8TISmvHiIo7vaGqW8SFSPu24Qzzwqcyo8ZhjRPEwScLxzAWW8v0AwPb/4mDzjwE68yBKCvMbdDjrT6oG8hZ3GPByNibskPqg7fqrnu3wS57sB0Wc83oQJvKYUdjss32w8wa2ku5kYy7sMEii9HoLpO2uuzrx9hgK9CWVbvD2XrrwO9NC8mdXjvD+nijsxRpa8C48HveQO/LwHjuG8KqfRvEVROzwTXH482FPEPPYPNTwxJ8m7NaJXveqDUDxYel27Ue4+vMQbfrzo/oI88+GoPCs6prwFhKG7h6gGPTcvZryHNKi8fObZOdPqjzpIJI2819CnvHM7+rwefIE8mJTMur/XvDx821C9V19QPEqtQTw28CI9Y+ocvF8WOzvXKmk8zIpxOz8R0rtpzp67JtVvvGkfILy3Y867Qmg2OxMu8Dv4OnQ6jfbiu4NyhLwgKb28ZQ9PPaSpMLwVW308VbYuPJ96DzwhYqm8yKC9PG4i1zxsrBO8/YRePI/OAz34DJs8w4IgPec5p7wKfZc7W4xIPboAE7xRKMw8zFxhusIWA71aFXA8RuKfvKpYzDkAKJO8FouNPGpRjry9Uum8N8F1PEIVhDx0opk7dyGjvAJYULwgxZi80VAIuz4fBDyAIHu8R7A3vLKhojqIAHw8F5qOvGsvAz3MMoq8yEWzPMifdrwpGJe89QcLvGV047vqqDS7FcWuu43tIrwdBYE7bdS8OwjEYTz7eYw8i6GmPHYn9TlyaoU7xOw8vVXQSjxLB4Y878HAPG8XzbwTtYA775H3u7eL27vQa3M85foOvFilEzvc8IU8yCQqvKO9rjvvIRS9MOr7O+h1nTnFlqq8SMMoPCwoqLx1xR885rKevHjOI7yYQ8k80xcVvRJnWzolDJe7NapePHQtKbsSpJ+6MKo8vGzXrjyEupo8/wfkPAzP+zyaz8Y8vTBtvN2O8zuHe+u7rFMDPVXuqDvtOlc9oOymvNp6lrxMogw8wUIevGna4DwH4Zy8t2SrvIp3Gr2iSKO8DKUWvK8+ZjyEMYc7xQ2tvFs0fjy53i48Lx2gO8woSLwne2g8X6UqO26qmTxRGXe8X2fHvJAD3TwuKqE8L8QaPEL9nDyXcx08khZJPA3tCj38Npm8dKJGPHtTLTwX5Iu8CqA9vIp90bwgLIC8XpZfPGtv4DvQ1W48zRT4PMVHvbvS4i28lbacOxcQ/7wVXre7ALMfPXLGrrsM1Pw6l6Dhu0oe5rvEX2a8dWk/u4RfILv5trg8Ob2XPF3RiDzM4748NeUhurNqlbpEEg28JRtXPNZCULwc8MW8pSGwPLyyED0z8Bo7RfqIvP/6wLyl4YO8UTg2PT+uAj1GK7S8cZ/nusdwgLw9n/A8sTPKu74QlLzbtYQ8hkzOvJiparyVxnO711+APDtrwTwYFwU80jjrvCNecTyfoOo7EoiKPHFSWDqhXra7TCygOhctWLz56he8btIJPQCL7blIWl284spFvCyDU7v/sfI79N5jOypcjDtqK4s7euSeOfRcljx/7ZQ7KkcJPXVVJLwc4m282qpBuyutHr3dmgQ8nhWnvAyUg7xkOzW8JAdbOYyXZru+Dmq8yZNoO+EedbvF9li9HlxYPI+mmjs8Yi48LcKru3I96jqz/BG8miqQPASt4jyjFT27W5yxu/7oGbw1hHi8VLUOvHRzvLyJx/A7RQqePMhDb7tsyEe9KadLPTh75bzIA9y8zELZPN7Y0LuBHQ89uZbcPAGY7TxTtCg81xz+uwjn1zthvxc7f6CPu6V4BTzWMty8iaS+u7Ac5DxQGAA6lcyrPPlIa7wNAIg7y/nbPOk+FTy9kZw7NtxPujmA5LukKg08AImIOxlJ0brKEHY7fg6ouzNbGLwecha9v9+/vAKCrjyXe/c8c6AyO4eBdzwkRYM8Eb+MvFsq/bvWJ4u8QQhevAnpKTzsymg8W1J5u+UZpbzUewA8DypUvFIbtDyVbe88i0XjurkTI70QvXA8YHELPFf8qLv58f28U4C1PCUYQLz67N88MndZu1p/XTzgEym8uczJO5rPsTwE+qy7VLINPUtEXrz/fHk8l9GdPEFjVzy5igC97Z4XO3cQB73oJQU9F6tjvNOI0Txlp9w7U+Shu8p4srssLo+8vBMiuwgf1bym0OM7sGYrOy2dBz0NPo48cPQ4OuJpCzzVetU7erzDOwUBt7yCRvs7zIMhPNZbZ7ybou26LtxCPOGeGj2z1K671MROvPvM87pucQO9NWjNOfb/iTynLLm7c1GjvGRuKDzKcwc8VII3PXLKrbwTi+O88dqYvGYJY7zncfA5U76Gu3TEeLwhP9K8si9/vBkf/LxQRgk9GFmRu7ZJ4ziUHua89S28u4M5qTxBKGO8w88rPE0GDz3aZGu8knwwvEJ1Ybymf/C8zQyDvBqgKbyadSM7NPWcvIhS8bzLokA796rJPK9dxjzNKIW8vT3yvA5mKDyJI4E8zDizu+ROLrwnIAW9YwOPvBuYSzzHWB46qbRwvO+qcTzb9w68poKbvFAqcLyNgyk9h4bPvPCoXzuOwvg8AjB+vBIzqbupAkI8idFwvDCaPbyC68y7B2r9OgpekbobcAa9BLnsOqDIAzxz05G8j7KYvCImuztjy4y8EnbHu6pA1TtoKoe8+xdvOqiDh7uOM8E8v2WXPDMQsTwY1BE8L9tjvCrOALu52to8LK4cPA==
        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: njqIuTFaqDyzuEY9VYw+O8HqiLpgBag9UH83PXB3Jjyn1RY8ZKyUunzbdD2I9A89+kgsOVurJ711DTC9NON0vWZwBzy7SSw8bbCVPLfJNDr6aya8LRfUPJB9TjvEVSE9zPwUPAISXrz4C6O801pWuwWspTwV+ts7+waCPETpzryVejA89tK1OuNgJ7rhuNO6e1TFu8z/7rpaptE7D2UUvSn7QrxxjQC9ffGmPNjspDzbqbc8ZE7aOxbZtzv8xtC8PTQ/vBKgZrqnCEc7JUE7PDHyfb2/qKe87tVXPXOuy7zwsW08ylq9u3UaZ7wMUuM8E01TPMcrI7slWo87MORtuiBmvLvC3gC9h1e7OqTo9zrbtes73fGZu+AQnDwCDBO97tqtu8wJ6rvZEBU9TJZovPrjdryL29i7lBvfOSU5PTrFlpS8OTDiPKa1M7yOVPA8vk5PPBJlj7zk3eA8ovDoOuo4rrzbdxG8EhSyPOG/Zjv9QhU7S3+gPMhhw7tXg4Q8MlVMu85+J7s6gt27kAGYutaIIby5I4G8kahdPUL4sLzmKgk93h8jvIgKFLwLvAg6ONs8vNeG6zvftrQ6NcbuPH6EK7wfzFE9UGMuPAexITwq2uI8V3pTPcBRZDwPJzc8Tq67vAe+bjyhN/67MucDunUj4jweoD29XXKYvFG3ZLxduKE8ZmmXu2q5wTwjkNG8ZATGPKaAWrwktjC996qaPKFDjzsw3mY6DNvAvIjrazxxE9675+yIu7eARbuUz5k5kA3evBvFLb2mgT27bkYdPIPcqjvaJ3W6Wvr7O/NthrynQgs8DeabPIPUObtW+ZA8zDe1vA9xgDwv0AI8hm9EPD3YyrrKZiy7isa5vDMteDwaKx48SKuwPMlA37w2WSY88LbqOymKpDkuVr081bhfuwO/IryMnve7QwbRvJ/5CLwzvQG9WKreuzIbnLwe4YI8lNrBunaFWD3JMDk9ycpkPNrCAz2wFaS769sjvGwFBbw7R0A8YLQGO0Lhy7vck+K63DImvDM4sDwsLIi5Tk85vEGp1LsBKBk80+2oO6qD0zzk5GM7xL1JunCi4bzjKX+8/4pCvAHlIDsR07E7G792uukX9DvoCgy8MVq/PIlFqjuNie47Z4brPGopWrsenH88s+izvI/iI7wxg5Q8fBnYu/3cljuovFm8r7VovPEAXTqWSJm89b++Oj89eTurqJ68hXIAu+crCbyupVM8k6uUPALRLTz8OK48HlNoPN/E0bywnPG7t37lujhHJDy+qCS9URmWOoMRq7xW0PS8nJShugDog7yPj6C85DsUPOQHsrw1uNA7v5V6vFAC+7uvkaw8wxqsPEopiLxOUoC8uSkxPJKjQ7zXRE+9U9CAvCnHyzoFwJ05+DoAvSEAQ7wt5um7M34lvC8XmzxJs648l9NJvWNE/TpY5kO8RpJRPRRniryt9lw8yeWGO2dFhDvMHaG8m5rSu1U4nrs43Ow7IC19PE+YMzwkzLo6aZ7KvHfDyTrWOKm7iAF7u/RR7TwK89e8ebXKvKsTvDv3aPs7vHdXPJHQobw9U2g8mrWZvJxVjjw4HYY8APpSPA1TArwEK5i6u1Dru9bcSjqmgF88AlUTPcu2DDu3fw09Y6/Su6XAszizKOI8C2RjvM707rvHBqk7o95GO6w70DqWNek82YOivC+CBzp3gXE7CMVxu6oakLzd6Ci6O4wRvVsaqbsZ8ba8DUS1vDSAOrtL6DE8RVh+PAbmfbkcbBY8n6IovIJeNDwth4m9J9dVu8GxITxnJzy8ffsOuxv5VDzq5CG8XjYrurwhS7zBMNU8SvTqu6M+Jr05qTq81gaFPPavijic0bE8YshPu/0HuLvQI4C4EiL0vCRqBbxK2PG7a7WmPD2IsTocIVY84x7ruaKpDj1pAeC885bbvGDlGLwvrlg8hUhYPG1F+ryDeoi8X+0zvHowljyqfIU6w9zrvL/NNrzc7Qg8G3jKPFY0H71Ktq280WxMvGg67TzNa586zVnuu3aJhjy2Ti08qyLrPF0lzrwYHCc6jEvPvNwwHLzAZ+07vfeFvF91/jvXt1u8FRaNPKkaTDy9FMo7HeKkvH9qi7zPM6U8hqkEvC1p3ToGNXg91AoSvVOltLy1u468NUbyvKve8bu81Oo88FykvJ8Oirx8g2M8JcZRPPPJCzye/7U85LGQvMHAjLvpjaO8HWtwvZkhMbzaCJM8zoUsPOkJrLpWuNm5iNgWvTjkjruAQOw85X9cvPXqJLuMHdQ83HxLPGjzGDxbkMm8eIdfvfNpHTyHwJU8CFO+PCH6qjzKLnQ7eVZ1OnNPoztGq667A9ACuiQKmbqBvnK70TqGO27oSbxSa848zdONOylWrDwqXGc8nSqQO/33wztNZJa8Tu6bOxrPDL1xYcu7HI7zOylqYTugKIc8f1jgvG4CF7wZPO28Dvl9O2sIUL2BnS49u401O3UxKb01pDK6NOplu+7PR7oCJLm8HpqlvBKmrTzTfY26F/YwvI+Cwjzek8m8UrucvCnxIjuLu6+8EI2lO4aIgLt+i547lew9vDAn/rqX5AQ9yF+OOk1uCzt+CBE8Cm+SPH0uqDzptjK8RHiOvASt2Tx9c4u8LA8UvUBuabsBEG67iPvvPH3OHj2J8PQ7U0nTO/GtTzplzH28PIlXOW4SkLsXSGs8KypOPIUunjzwWpc8z13kvO6v+7t59zw8sNpPPC3VQjx5FZg7BMHtu3Bhujz+m8K7McbxOyaFVbw5s1o6VsM9PKGtA723R6Y73ElWu6OYJLwfQNK6IEd0PM2WuTsZGpy88eD+u6aSRro344e7fASsuyW33TxKp0G8ME5LvEVlgDzZv328KtiFPBuw5DysORk8P83VO2sJZbtlMKm8hQ2evEMD4zyWRPM7suVgu6wFtTx5pZu8WK2+PEommjyXp8c78MYOvOzTCbuKgQs7nShyPKMecrzXA8I8ALkTPLYG0rvMRvW8yCfdPFmgEj1mMJ08khSPPGRhBDzurQ49XYg6PPkqJL0Eqku8wzHSO9I4obqy4Y87v81YvKvfFT31SA49Fx+XvEuRTrxCiJG71DooOmG9iDx3bpQ8pNQROwAi6rsYFTi6x4luvB7HMbyxd7W7QNm8O5jkiDyefrK7olXcvGEyxTyZS/28yDijugHci7zDJ8o7iPO3vKTsJL1esWM8LQ8zPQJG4bvVWG28T5CbPL6NO7zkQ+y8QdcVPapmejwnzfw82q2HPFIdzjtxjqu7o5VBvKRQCbxFF8q8dVpgvINkzbzC7jU7+H0VvRBiaLxr7uW8iYFmPI/+Nr1Jpn+5xf+cucO05rywJry6Fr4AvR8BBb0iTjw7E06PPIjvDLyd7hw7G2pvu7mFFr3TRUa8CpS/vBfh4DytU7q7h/n5u5Vu8jzf6r+775MZPJPQf7zC/b488OVtukbVY7z2dZK8n/lwu5UVLjyWVfk7KMOgO0gBkbrtCD49WHoUvHzz4rz7Log7QJArO6+ElDx9RWI7Z/I6OxEABr33HCO8RLiqPCYQRrzbI3q86Sk8vDCrGjxBgBU922mUvIAnLrwkSrc7sAuwPKhikrumciG8278KvCJAILttN8i8P8r2PDbribzlFX07gN/ePK83/TzSwRG9rXp2OcDbyztYbte87rSvOsPD1rg84hA9o68hPBxKj7zjrGE8d/waPbtQibwUUG87kjGEO6MILL15IRS9ld1fvPOsA7yo0qi8R4CFu3jW27xSik08UIVzvG9iyDsZGau8sPu0uVG64zvNIlQ7QFwrvY6iuryPmgs9AG4OvSE/yrw09WS8jyQvPOUExDtiZmc7IGrgvOME+DxiFP+7H2s0PC+6gzyhLfg7kjJ2vJdrVDx1m7q7JnXcPBPVjbz3Ya28RcIXvKPu1TyAaVC899cmvOSGjbuNLVy85N5DvSyugDsvnUQ8OlS7vMFMBjuMjNs85QXAPKo71zwjW2m9jPKsvNlG3Dyu3ug7hsQIPcojC73oCDA9Uw2Vu1wfN73HrR28FDasuxVzKjyRKDE898YDPT446ryjqHw8c7GavF1SKzsl61K8GQ+HOZ10Fj2Nc1s8oe1+vDH3AzxKR5S8Qxg/Ow/AxDmq06S7dM4GPUaZgjuLzhW97BCxvPn80jylfmm8Ue0mvOExEDsALUQ8R36ruwvgNjyCU0U8SC7ZvJ6Qt7tOlvc8ZIO8u3TIcTw5eic8VhgsPEduRTu9JgE9d5VmvEROWDuWj+s7lQwTvNDKW7zBnuU8KpDFvMXfwLsGJMc8mfF1PNy0hzo5l8k86LGmPGQd1LzdCMQ8CX9GvK1tojvvuBG8Ks0nPOouXLziaO68SufXPKuDxzyAoam6dCaaO5dww7zNvNc74U8Fux1pNLzdx8e8u1OkPMYmQDwV3T09rdEPPB5E0zwKmg89RyGMPI6+nrrWqWU8kBuQOmWdU7zdgzA9k7v2vF27UzzYif687rsyvHGFo7zo5BI9baKJPMwAQjy2pCM7UddoPF0CmLvOZpM8NN4Ivfj67TyFgGU9l1e3u0JiO7zElg09ViArOjwCLz0TBkK85TvtPNJ4VrrzD4w8U0p4OTtvFTygHTu9i2OoPPr5tbqFmlm8e+zEO3LySDtA8QO9vtHTPITmrjxuc0s96jN/vPJU/zyNBiY8y2m8vFkm7DxxwAC8eIjsuqDyED3R4/k7/Z60u23c+LtD0Lw8G8mdPGzn27qs/2A7haDtvDZNgjxTlcS8klUBPIlPIjyhzqc7Z0qKvA/gU7wzOeu6dJnovA5qBj1nBtm8moGpO3w2nTz5F2S8kNuBPDhqBj3YPi07sHj8vB484Lvz9sY7N0PSuuGSAL2h4Hi8ze1GPAHfAj3cdp683ReYvFQqnLzBH9087FxVulJEhDxMbTc8DbcWPA/XibvoEnW8WPP5vA0+rLtT/yu8j2hFvKMuBL0vcrm8nlbPO71QtTzfOZi86P4/vI4tazx5u0G7jpqYu78gWDyCruI87cq9PKfSJzyHbr48kl9SPJuejTzx9fA6oRzsPKPWwTx1uoI7mZLuPAOBK7z/Hy48JSMQutwWJL2i/w+9LEo6vKQsz7yKLsq8YQoHPIZJrbywDPY8g6fZOy323jwALVm7vxutPI+LKbyhYgY8puUDO6mVQrzjz6W7Zvz+usbd5jo6sgW9SYqtOW2Qnbw44a82CS6SvJDh1LwLOwE8ybGxOvAbSTwQLJy8v28Uvau6g7zEkiW9nPirurkSuLxuzl08nhMsPA0mxzxo9Ja8JaugvL754jslSEs8DttmPEZRSLzVzHE8dvLgPHk6Jbt5Cbu7xn8UvGnkLL2VP708n4ZBPH0ZJ7yrD3w8jDzvvCt0Izwo55y8IiU/PKijZzq1lZe88SnjvBx1Azwj5ra71aGuvFDRnbzXqew8WTp7PMWVlLxSIJo8OLRivCz68DtuxjQ7YxKDO4UoTjt9Bh87rhQmvIHYWzzCrew8RLrlO0/dqrsvf8w8eiYYvVKWjbwZJDw9jOJuvA9xCDwZNks8ze/TPHAJMLxHNnC81rtwu+LJpzxLCZK8ZLPru/hsjzxb6Io8UyDiuybtv7oJPD68k5pLu+qv0jrjQYE8zRUvPeBSrzu1naK8/Z6Ou9osBTxfJzc9BqXRPLK2B7zRp3I8+HCUvOVuZrwOxHK8T7KOPDBzvTzXUGq8IihFO1Cj5jzOpS29cyIbPTVqAbwdCIq8b42XvD/l2rzEfje7+02CvLx4V7xgPV+8piGlvGjWQTxv+4u8JIwKO9hfyDy6Zi47vnbLO8TgvrzGWdQ89KJgPCazzTvtEf87jWzUPI/ce7wmnAQ9JYELvSRAf7yx2bE8rGIju3sTorxbYSa89z7Huy3NCbw/c8k6082kvJfpf7r0Byo9BhDrPFjzALvVFQg9XsXHvNsyc7tYD5S8s5cIPHUUljzHHui8aITLvF+dGb0Yj4u8r8O3u9kkJDut71A8zIh5vOiMfTw2ASM9WnZhug6jqDzDIXU84DmxOwTRljuTvhG9X6I6PAOOirynES28k2+zPNpl2DvSb+c7LTwfud4UwDtLCgG7A8WzvMeoqTts6oA8RUHWvGXH8Ds40KA77aQPvULsyTytnWA7Zn/CPG2JYLwQzq06PZKjvBvfsbvplIA8giY8vLf9H7x2dH67dZuoO7b6oblDuiG8plinPGF7KDuYF3479J4SveHSsjyHloQ89fg1vFoLrLshfyU9nXYCu3YulbxiD9U8qZyVOyAsJLxNdes8x1P5OuLEn7yh6Sm9F+2uvAt/FryDeou74zI5PYQPqbyhkYm8PVgyvBdAATt0Di48fL/ZvOEgc7wEcOI812HtO3QFSzuvcIi82jBqvOkJ+bw72+68jpyXvJCVLr14qZQ8K2ZuPNLTxLw5BTG8Y4klvFufkTy0Isg89vS6u8be/jrh02y8cKf+PElMHT2RysU8lK2jvP6BVDyc5oC7OWAxve5HxjxdUnC6LqyuvJsTLrx49Ua8+KqIPM8RZDyftoK8MI4QvSpPzbwRI+G7cKutPBCMYTwvP7u7nYtNvJl+yjv4fv47zQsCPbPTNjy1E0M7SFP6O7j5GbzlIPu7wmEMPH3DiLwh4YO6J0PGO7MXkjts37A8hGGHvBu+g7zLdym70dvyvI6zVbwL+A69IUXSPLryELwaYY47yEAMPB5x0rmfHxk9FvgPvWxXbTtMZ+Q8nXKNvGfnGDxt4Gy9tHL/vAYkk7o6oCI84SMpPXmudLslaDW7AcTJPIKk1jxgvkG8x8duPGyG8Dwp2TK8OkmHuxto+7qQA6C8yAjvuz0FhzwYrLQ8mCohPJjpX7yeYsA8bh1LOGXSebvRQzm8zUurO0zKCbtfmNC7GmEbvKW+bTyjNCi905anvNDUx7tRRyE8lG2jO2huxLkn15E8LVp2u/RKK7wET9g8uUXlO8HzxLsDvoE8/ZrwvFEZSTzQvaS7CdeEPC+uBrxymJa6jpoTPDDt8TwkrT07M94JPIW+ZLxF/0E8D3x5vLbG9bwUl487xrAivTh0xrtfmlS751HGvNL8DTu+4xG7f1RevLW8jLzLARY8fB2KvItWpDubHvw8WXmtu2NM6juTPSG9ll55PBjcCz3Dox88hAY+PaQfSr2VZpS81Jc/vQDHu7uTwxK8k0iXvHhrqDzq8ie95a41OzUkEjzh3Zy8bHO1PMUEEryCiMU7i2EzPTbkKDz5VhA7ekcauwoe47wGtee8f/GkPKgcSjx8GrG68FbOPHJRt7x+8Zs83uZpN/4t5zyYsBA8wDyVO33SLjzRwzW8QSOiPH8Q7rurzEa7aLnQPJ6xjjsHORC9fWyYN5azObwd7048SsGzPN5auzyUJcU6nWRWO3NWTTyc1KU8tSKuvCZmvDytL8y8jvnvPCcvmrx9wOu8m819vM0fWbqyw3g8OI6/PLDMAT3agi08q8XTvNAE3zw5/T+6QAwtvA+ZibzuBoy7p4kKu5F5grwDry09sGyDPOdDELxpvSC8nPNpPK2iXTyiHkO8tM/nvLzsdjsy2pe8btnSO7/TjDsJzuE8F6ELvFHtCTifeN6855vsvDgZqbxI/ae7m9UhumgYizw1wda8BEaDPLfA5jwp/i+8ppoMvZ/B0byg9Qi7+6efu8IPvjzFjkA8x8WAPO1ZlTxFDN88m8DHvE0snLxNk4Q87gSyPPVvgzxlnuW88pm/PLZOtDlq1YK89zFKugdHDrtPsHy7Ut8IPLWahrv0hqQ8DXw5PCTx/Dy7Kae75zQNPWDB0TyEbYo8ebAWPc02m7yANQ09zOFtPKnGRztL97q8kSv2u63sb7wgZ7G8BCylO9YbvTrT87i7DWosvDbndbzKUJ88SouCPMC6q7sLFWE5obnbvL4aHTrkH708f4/zO1f++jzBsOG8kKUru0w37rvhc6m8yXMKPRbkezptdZO79OBrOg/5FLyggc28Ua/hPLEqVzz6fEi8YuwHvZoM6rv1adG8oCGxPEcRXbxj4im8cZ4+vF0QTT1YNFK8XfV3O2mTgzx74RA87JNhPG9EVDx2ezI8U/u2PB+kyjrfJ+u8Icp7OoMnmDxixVk8Yc1MvImBJTr8pbu8AoE+vUexDDwaGcK8WuFBPPrcortw6RE8EB5EvGFxFD3RnBA7rGQVPINf2TvZFtg6lFcvPAePDDyt9Vk7zt8MvPqaETwdyLc8iWjYO+OOEj2jgv86Y89yu9vMG7kPv2k8daSZvG3i2DzulY66OYjiu17OATzKX148jhTLvKjTSbuoZxi8nhUFvR3AgTvT7ig9INvPO+mOFLwjluI7dc00u+W9LT0IR2+8NwdHOaxJsjzinTg7854kO/J+0Lu5pwy8kk+EPHAl/bs9H5U8+leRuszUwDzAjMm6ubBku8AvpDw9p228LvYJvApuoDwr1AA9YL7gO91Bvjtpieq8YIiWu0d1XLyPi3o8GTzTPOaCILxD9K+7U6ydPKVJ2TyXAY065IGzvPBrHj30enU6UaAePECovbxmYyg8Ej6iumFBgLwBEea8T59CvK8LozyHI1i9WzOIPFAUjTxurgU5nPhEvBrpVrwWHaY5kmWTOv9qI7zLko88NXV7vDVeAr1F9JQ7KyTPPPNTjjzfMCU9obAIPOYtbz3EzZU8D694vFPBKjzw7MC8Ai3qu4FTA72cVPa7vX6/OxgaBj0yUJe6sw5UvM8w3budYiW9XW/evEb4xLz440M8ZR4PPfbKiLxVN9S7J6MqvDecxTsQ4ja9x3mzvBrSpbyR0W27UNTJuW/SjTyN3w29CWcBPSVgkLy3uVw80DsrPHqBEzstQAM9WmoWvIeUxLw7vp08DIqJvIoBIrhFcgG93SvuuzoXGzz/isu8SbjWuVTliTo5R5y8SG2TvBMnpDzKgTO8g6UDvV+AiTw0wCC8ByMKPNPNvbrNEb68ZpmAvNEkcbxkR2C8oT7Su9Eq07yZf3e6YSZQO+8syryRaNK8aXDPPIAKrDzc1fI6Hi6aPPArMT23Vao7xIu7u+7kAj2kZgM7SqKIPJRuK7oGMQe8uV+0OzhfMT0UoQe9ogZ9uz8J0bxcvYq7aAmYPAxHoLzLZri8lQFQvLohNLw1bA48R7IVPEyGVTy1jw69TzUSPMbCKLxC67w8OkNZvQ3VyDyu/y48cCgMuwyMHDp67X88q8+suw8hDzz7ksq7OrBvPCNNtTzKJEy8+wvyvJScoTzVc+a7Cs4LPcUDHjx1qsw89S0TPLr8HLx05kI7ApQmvHDwA72iUhC8LZGsvKkj1TzKKk26Vk/DPG65Rrxenpi6qBM3vOSusjz511M9IeklvBoPrzwkozw7g4hLvGa2wjymZ3I7eMwVPHF7k7z87dI88+2XOxs5wbvOlKK8IM8MPEq64jqHPg88P/6cuutYxbzWDkC9+FT9OuLPgrzn+vS8qP1xOxIXED0t02m87wdUPYn6xLys4Ju8Dh2Hu8CPgTzo6fe8xiczvaMcGDv7Zx48ApVAvPEwabtp3Jo8iHJsPFJ2oTx/SdW8ulLOu+M7jbzKW8C88usYPFsNPbwq3pS8nPusvJI8EL09fKq8Ndisu6CPAbwCfw49jSJcuqGmYrwS42O85yS6vEVZSzxgLJm8ODcAvZ6At7xAPzw7dxzavBePlbvg78u7yqKIPLZnejxAmxw5g/5HPDDlI70GL/Y8WScRvdYgCTymF8O8nIH+vHXZpbyARCk75/DVvJLfMj37GAc7vtk+PPJf37uFkOS8x5ZFvCM3BTwktA69HxoMvc98YDwSgA+76sKjvMcbn7tsigk9j9ZrO5VIVrzP0Lq7O07hOidYKzzSjnw7ACWrvCTBSryBO2u8KgPquywpADzzLJs86qEIvUhQ6Ty0pVQ8Dp5vvPOi8jiibHc7AQaIuBcKejsh/QG89yulO4aB17y00OG849JHPRzVCTzn2jQ8gKVjPFIV6LyXOZS8rIr/vCsaQzz7gC68m3WQOotGm7xrQaC8DgW/PMzpJT0ECPo7/7fgu1+ZY7t60J48whbQPOKB6Dyf06g88C4iO6Pf47y9lAw7nKASPc1BizwmA9O8yN2APBtwPLyLtZQ8cGcsPKf9ILwrDaI78m7fPHBNKT1ZET283T1uPAfeo7yoqK+87RZQvQ2kUT0bbLm7YR0TPQO3q7yNzeG7IXNkvBQXTDxZZgs83y6nvLR8tzrWFQA7Vnh6vOi9jzw/+i66/bXEPP9kzbzftNi8HQjMO0klJLpC5UU9Uym5PFqC0TzNwvm7uOqKvPGloDuiONa7M4gSPLbJajyG6Ra9zVDVvKTV2ztcT1k7hS0tPIwjZ7ydtgY964XQu1GjFb0mbEO7cpYmPQ/ksbzvC846ab0bvCZoGr0Gq3y7qpdivLrUabx0kb865tw/vGu2IjvqOpC813OsPFphh7mh+9m6yJuIvDtvMbwkNoG8r5yjPH9fajwnmEG7rmsWvaTrhLzDtjW996SjPKYsHDn1M1e9sfjburBkC7slFX48PFGevKppj7xSfDu8BK4vuo+3vbtZkRg70Fw0PCpd0Ty4cTW9tG9svDiakLs2Oxw9ZyxVvA4GnDwAQzO8jRn9PEUXM70raB48gNcRO3z1o7sNat87GytRvYJlB7wJEM88c7QcPMyONz37+wY9r6rdu1yyqbsXDrm8OoT8vB9LSbuY2iw8NLjKPB9TEj2DmiE8B4OFO3Ao87zx3uO8uz7avJ9qRrtIsA88DTBMvCZr4byDuVe7LeLVuobqizyimwy9K14tPHf7Bb2ZYku81V2au/DjMTyZerq8g8JhvPlgwTyxPEQ8dYKZPK7eRjrMeLm647YRvXewdLzTRtU7U3RRPKSMqbyWs6q8rOQJvebH3LwFOrC8CXIwPeJkQjwYArc8U7YjOjF8Jjx50Hy8XMMDvObyITwl1vu6Vno+PI14MLxyjaM8DqbDvKrEfrsBQRm8XIWOvKMSyTwgg4C6eV2HPEo6M7xtuDW9nR/EPNshZjw9WxK8pgudusDLSzxgSfS8NqoTPBlLkjpKDgA7T3ZzvOaKELwtHoQ8A0wUvO+pK7y5X0G8vuRPO/S8XTxt/0m9PklWuxGTiLzorv68s8ASvPwllrzVVqK82e7lvFlZSrtxh4O8jNwMvdJsJb3u/TO8YTEPvVmTxrpbibg7hcMIPUZT/bqd8DS6+VTnvAXAijyQRb28r9oIvMOBsLsuNUM8czRZPNGJu7yyN6A6MjI0PLfpoLqsMNu8aSIdPeo9E7zhJ8y8ZMhNvFwo97vuVxe6BtRNvPoo7DxYmGK9BbJ0PM4hAjwoY7g83xwTu/1cdzx0jI88uCApOiz1Xju71MS7Js0yO0DLULxj2Vo7Ly0BvMXnfzyT8Xm8BisdO6dpDjlUNA29r7AhPc8vZby/9II8irGMPEjWWDxOfL28BWPkPKNQyjyvGZs7u0oSPOWKvTwOcrQ8NaR4PQqUxryxL2k7qNwDPQpuvbwvBJo8xLVcO3R1XL0gYCI8RyyDvD5FjbxzKbu8NxiwPGDtp7zvbg+9LTCxPGYflTvCdHc8WYuEvLJ6w7xJO+W8fTbOvKlA2zxGhme8Dpnjuj24P7y0GMQ8ja6yvCG8Dz2rRa87p+i0OwDPprw3PMQ78vI3OpEMMrt6gYA8Ef8uvI9Qiry96dq7dzHau/n+ODwPNag8n/PqO08yDzpuFZs8cmM1vVBbQDw1fd88Onl+PNeGRr0QITi7rww0uyJhBjwAwwo9v8WZvJ3bNTz6fZ885RkdvL8TaDx2Itu8UoD3O+Lgbbt9dYm81nYlO8EAkTv23xc8/4YavJU0oLvi/6s8A8ravHJhgbsGgkg8RC0oPBkDYblFioS4NEdXvNk/7DwezsY7HIu2PMkovDydnBc9K/2UvICwqrsGRp281gmIPJKNRrvkFmA9twzYu+hjFL0x9e08+Y/rO7ZC2TzraAS82ZCWvMQS0rw7Whm9hXQqOo8cxjsvdoK8kanUu6W9rzy8oG08r/HYu/GKU7y97P88PyHSOteljjwSGl28Gq4MvRSj7Tx4q8E8PVOrPGw+YTyv6106UshNPPFl3DyCM6K7ddw9PHdPfjwQpmO88Q4XPJ2eH73oOCK85AjpO1HyJLwpamM8SB36PJ14GTtEbzU8CvbjPE/7kbyT07+8JQ2pPC68UzvT8Hy7NVgdvLiaELzNhwa8VQ1MvK9xcrwx95A8a9luu9COijxKtyw3SctTvMhnhbxYsiY7YD8oPEUwhLwLelW89yYHO8yfGj3+NA28TvRMvO2l8LxfjIC8VoEMPUOrJD3+MCW9R7s8u5dgUby6cAg9OlNdu6S3sLxkH6k8eaoHvd8mIrwIKQQ7EnjAOwuMjTwafC48pxczvU2ogDsBKf07OOFvPNPvIDyBU4A6jwoOu5SifbyPfgu8fJ3OPCdHIrxa4J+7dVMtvO9/DbsLatg7Q7oovPyzBjzgVDy8Ub0Uu2pOjDyl0dS7aKhtPJJVAbo/kVW7HZggvG6VAr2dNbK8J3bXuaqktLzkKwO9zVeRvLAnmzu8Hsm8nwMaPCgYVjwFo/O8NYCzPJusfTz+Lgy86cBFO3k71Tqf2OG7Gq7gPKz0xDwLzWI7RAVtOzFvhLwkNJC8cNp0O06Iyrx/fFA8sp4RPR9AobiTEbG8w9RQPZjGUb0V1dC8mQQQPaAAL7xXzwA9txF9O8BU4TxP8Pc8fCEjvGP8Szx39am74kYCPIq/ojy2uw29jjQiPMFP3jxzWnY8KMVcPCWdxLtO/iW7x7ruPC77nbu9W6E7xhWTPBTfLjwncaU7Ym8yvIUqPbtsJTq8pTWjvAMupby0WQS9ThCdvLGF7TuvluQ8z1zFuhEKjDwqaeW70TdgvEupgTuNqfW8pQuCOz7l/TyUKXo82td8vMSQ9Lz59PI8geKUvDKwNTybpMc8SsiNOmHuubwJp467LHUCPHKaibwXlha9arm3PIursLuU3Y88DV25vEbCl7t40Q28TxYhPKoLrjyu07G75s7aPJF5YrxgN188ImxAu5l5WTxJiRi9Qj9GOymqLL26YAc9Ca9TO/ImsDxG5ts6l/7wOxVwizvsPMK8QrCpu6DDCb2KN907o9rAvDcEAj03VV88KpiWvFSVdbrwHCK8ayrSut3SYLycDpk8yrlXPDTMz7yoZ3e85pHqO3/bAT1+u687MAeLvA950bt54Jq8UOJXvE8g0TzYT8O7B6egvGQUzTwoyG88AS9fPUBKgLyPGKm82QAsvGg7LLzzKjG8j9NvvHLVMbyZKyS973oTPA6xwryWP+o89OO6O1sUA7sP8p68AquUuw/vyDyqJzq8YEeAPMo+2DwpHeu7G3YHvMN4Db18pOS70/E2vOjkdLuI/xs8nysFvJ0wHL0CDyA7nr9sPFRG/TtaoyO9IB4Cvf0lhTypWFU8Q4iGvIv3Qzs8JAK8XtWNvMOzLzz2Hgs8/Df0OpoEnbthwKy8VammvEZ2gbxnzVE9E4EGvUJJgzy7CAA9mbeOvGDRrjvEN7w8uQ4RvICjiLy4NZO8kpe6u4RM/js3CuS8WZY2vISVgzmEFni7lleHuxbTMrx5R268DqwnvLleAjxp6Ty8TniYPJ3zdrtcTeY8sdfZOwnOPTrHCqM8cjz8u44lVLw6vvA8+tUjPA==
        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
      - embedding: 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
        index: 15
        object: embedding
      - embedding: 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
        index: 16
        object: embedding
      model: qwen3-embedding:4b
      object: list
      usage:
        prompt_tokens: 3883
        total_tokens: 3883
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '7823'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.

          IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.

          CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
          - search("query")  ✓ CORRECT
          - from haiku.rag import search  ✗ WRONG - will fail

          You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):

          ## Available Functions

          ### search(query, limit=10) -> list[dict]
          Search the knowledge base using hybrid search (vector + full-text).
          Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings

          ### list_documents(limit=10, offset=0) -> list[dict]
          List available documents in the knowledge base.
          Returns list of dicts with keys: id, title, uri, created_at

          ### get_document(id_or_title) -> str | None
          Get the full text content of a document by ID, title, or URI.
          Returns the document content as a string, or None if not found.

          ### get_docling_document(id_or_title) -> DoclingDocument | None
          Get the structured DoclingDocument object for advanced analysis.
          Returns a DoclingDocument object, or None if not found.
          See "DoclingDocument API" section below for how to use it.

          ### llm(prompt) -> str
          Call an LLM directly with the given prompt. Returns the response as a string.
          Use this for classification, summarization, extraction, or any task where you
          already have the content and just need LLM reasoning.

          ## Pre-loaded Documents Variable

          If documents were pre-loaded for this session, a `documents` variable is available:
          ```python
          # documents is a list of dicts with keys: id, title, uri, content
          for doc in documents:
              print(doc['title'], len(doc['content']))
          ```
          Check if it exists with: `if 'documents' in dir(): ...`

          ## Standard Library Modules
          You can import any Python standard library module.

          ## Strategy Guide

          1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
          2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
          3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
          4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
          5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
          6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
          7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.

          ## DoclingDocument API

          When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.

          ### Properties
          - `doc.texts` - List of all text items (paragraphs, headings, etc.)
          - `doc.tables` - List of all tables
          - `doc.pictures` - List of all pictures/figures
          - `doc.name` - Document name

          ### Methods
          - `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
            Returns tuples of (item, level) where level is nesting depth
          - `doc.export_to_markdown()` - Export entire document as markdown string

          ### Text Item Properties
          - `item.text` - The text content
          - `item.label` - Type: title, paragraph, section_header, list_item, etc. (lowercase enum values)
          - `item.prov` - Provenance (page numbers, bounding boxes)

          ### Table Access
          - `table.data.num_rows`, `table.data.num_cols` - Dimensions
          - `table.data.table_cells` - List of TableCell objects
          - `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`

          ### Example Usage
          ```python
          doc = get_docling_document("My Document")

          # Get all headings
          headings = [t.text for t in doc.texts if "header" in str(t.label)]

          # Iterate with structure
          for item, level in doc.iterate_items():
              print("  " * level + item.text[:50])

          # Extract table data
          for table in doc.tables:
              for cell in table.data.table_cells:
                  print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
          ```

          ## Example Patterns

          ### Counting documents matching a condition
          ```python
          docs = list_documents(limit=100)
          count = 0
          for doc in docs:
              content = get_document(doc['id'])
              if content and 'keyword' in content.lower():
                  count += 1
                  print(f"Found in: {doc['title']}")
          print(f"Total: {count}")
          ```

          ### Aggregating data across documents
          ```python
          import re
          numbers = []
          results = search("financial data", limit=20)
          for r in results:
              matches = re.findall(r'\$([\d,]+)', r['content'])
              for m in matches:
                  numbers.append(int(m.replace(',', '')))
          print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
          ```

          ### Using llm() for classification
          ```python
          # Get document content
          content = get_document("Q1 Report")
          # Use llm() to classify sentiment
          sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
          print(sentiment)
          ```

          ## Workflow

          1. **ALWAYS start by using execute_code** to explore the knowledge base
          2. Run multiple code blocks as needed to gather information
          3. After collecting data, provide your final answer

          ## Output Format

          CRITICAL: Your final response MUST be valid JSON matching this exact schema:
          ```json
          {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
          ```

          - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
          - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.

          Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}

          CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.          
        role: system
      - content: How many tables are in the document? Also tell me how many pictures/figures it contains.
        role: user
      model: gpt-oss
      reasoning_effort: low
      stream: false
      tool_choice: auto
      tools:
      - function:
          description: |-
            <summary>Execute Python code in a Docker-sandboxed environment.

            The code has access to haiku.rag functions (search, list_documents,
            get_document, get_docling_document, llm) and any Python standard
            library module.

            Use print() to output results.</summary>
            <returns>
            <description>Structured result with success status, stdout, and stderr.</description>
            </returns>            
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
          strict: true
        type: function
      - function:
          description: Result from RLM agent execution.
          name: final_result
          parameters:
            additionalProperties: false
            properties:
              answer:
                description: The answer to the user's question
                type: string
              program:
                description: The final consolidated program
                type: string
            required:
            - answer
            - program
            type: object
          strict: true
        type: function
    uri: http://localhost:11434/v1/chat/completions
  response:
    headers:
      content-length:
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      content-type:
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      choices:
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        index: 0
        message:
          content: ''
          reasoning: We need to find a document. Likely the user refers to a specific document loaded. Let's list documents.
          role: assistant
          tool_calls:
          - function:
              arguments: '{"code":"docs = list_documents(limit=10)\nprint(docs)\n"}'
              name: execute_code
            id: call_qmo5y6dt
            index: 0
            type: function
      created: 1770373391
      id: chatcmpl-318
      model: gpt-oss
      object: chat.completion
      system_fingerprint: fp_ollama
      usage:
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        prompt_tokens: 1759
        total_tokens: 1818
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      code: 200
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- request:
    headers:
      accept:
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      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.

          IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.

          CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
          - search("query")  ✓ CORRECT
          - from haiku.rag import search  ✗ WRONG - will fail

          You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):

          ## Available Functions

          ### search(query, limit=10) -> list[dict]
          Search the knowledge base using hybrid search (vector + full-text).
          Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings

          ### list_documents(limit=10, offset=0) -> list[dict]
          List available documents in the knowledge base.
          Returns list of dicts with keys: id, title, uri, created_at

          ### get_document(id_or_title) -> str | None
          Get the full text content of a document by ID, title, or URI.
          Returns the document content as a string, or None if not found.

          ### get_docling_document(id_or_title) -> DoclingDocument | None
          Get the structured DoclingDocument object for advanced analysis.
          Returns a DoclingDocument object, or None if not found.
          See "DoclingDocument API" section below for how to use it.

          ### llm(prompt) -> str
          Call an LLM directly with the given prompt. Returns the response as a string.
          Use this for classification, summarization, extraction, or any task where you
          already have the content and just need LLM reasoning.

          ## Pre-loaded Documents Variable

          If documents were pre-loaded for this session, a `documents` variable is available:
          ```python
          # documents is a list of dicts with keys: id, title, uri, content
          for doc in documents:
              print(doc['title'], len(doc['content']))
          ```
          Check if it exists with: `if 'documents' in dir(): ...`

          ## Standard Library Modules
          You can import any Python standard library module.

          ## Strategy Guide

          1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
          2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
          3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
          4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
          5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
          6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
          7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.

          ## DoclingDocument API

          When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.

          ### Properties
          - `doc.texts` - List of all text items (paragraphs, headings, etc.)
          - `doc.tables` - List of all tables
          - `doc.pictures` - List of all pictures/figures
          - `doc.name` - Document name

          ### Methods
          - `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
            Returns tuples of (item, level) where level is nesting depth
          - `doc.export_to_markdown()` - Export entire document as markdown string

          ### Text Item Properties
          - `item.text` - The text content
          - `item.label` - Type: title, paragraph, section_header, list_item, etc. (lowercase enum values)
          - `item.prov` - Provenance (page numbers, bounding boxes)

          ### Table Access
          - `table.data.num_rows`, `table.data.num_cols` - Dimensions
          - `table.data.table_cells` - List of TableCell objects
          - `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`

          ### Example Usage
          ```python
          doc = get_docling_document("My Document")

          # Get all headings
          headings = [t.text for t in doc.texts if "header" in str(t.label)]

          # Iterate with structure
          for item, level in doc.iterate_items():
              print("  " * level + item.text[:50])

          # Extract table data
          for table in doc.tables:
              for cell in table.data.table_cells:
                  print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
          ```

          ## Example Patterns

          ### Counting documents matching a condition
          ```python
          docs = list_documents(limit=100)
          count = 0
          for doc in docs:
              content = get_document(doc['id'])
              if content and 'keyword' in content.lower():
                  count += 1
                  print(f"Found in: {doc['title']}")
          print(f"Total: {count}")
          ```

          ### Aggregating data across documents
          ```python
          import re
          numbers = []
          results = search("financial data", limit=20)
          for r in results:
              matches = re.findall(r'\$([\d,]+)', r['content'])
              for m in matches:
                  numbers.append(int(m.replace(',', '')))
          print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
          ```

          ### Using llm() for classification
          ```python
          # Get document content
          content = get_document("Q1 Report")
          # Use llm() to classify sentiment
          sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
          print(sentiment)
          ```

          ## Workflow

          1. **ALWAYS start by using execute_code** to explore the knowledge base
          2. Run multiple code blocks as needed to gather information
          3. After collecting data, provide your final answer

          ## Output Format

          CRITICAL: Your final response MUST be valid JSON matching this exact schema:
          ```json
          {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
          ```

          - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
          - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.

          Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}

          CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.          
        role: system
      - content: How many tables are in the document? Also tell me how many pictures/figures it contains.
        role: user
      - content: |-
          <think>
          We need to find a document. Likely the user refers to a specific document loaded. Let's list documents.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"docs = list_documents(limit=10)\nprint(docs)\n"}'
            name: execute_code
          id: call_qmo5y6dt
          type: function
      - content: '{"code":"docs = list_documents(limit=10)\nprint(docs)\n","stdout":"[{''id'': ''8b7e395a-b2b4-423d-8c65-53d7ea800da1'',
          ''title'': None, ''uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'',
          ''created_at'': ''2026-02-06 11:23:09.536783''}]\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_qmo5y6dt
      model: gpt-oss
      reasoning_effort: low
      stream: false
      tool_choice: auto
      tools:
      - function:
          description: |-
            <summary>Execute Python code in a Docker-sandboxed environment.

            The code has access to haiku.rag functions (search, list_documents,
            get_document, get_docling_document, llm) and any Python standard
            library module.

            Use print() to output results.</summary>
            <returns>
            <description>Structured result with success status, stdout, and stderr.</description>
            </returns>            
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
          strict: true
        type: function
      - function:
          description: Result from RLM agent execution.
          name: final_result
          parameters:
            additionalProperties: false
            properties:
              answer:
                description: The answer to the user's question
                type: string
              program:
                description: The final consolidated program
                type: string
            required:
            - answer
            - program
            type: object
          strict: true
        type: function
    uri: http://localhost:11434/v1/chat/completions
  response:
    headers:
      content-length:
      - '691'
      content-type:
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    parsed_body:
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        index: 0
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          content: ''
          reasoning: List shows one doc. Let's get docling.
          role: assistant
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              name: execute_code
            id: call_q4w5nsrp
            index: 0
            type: function
      created: 1770373393
      id: chatcmpl-498
      model: gpt-oss
      object: chat.completion
      system_fingerprint: fp_ollama
      usage:
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        prompt_tokens: 1953
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- request:
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      accept:
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      - gzip, deflate, zstd
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      - '9333'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.

          IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.

          CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
          - search("query")  ✓ CORRECT
          - from haiku.rag import search  ✗ WRONG - will fail

          You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):

          ## Available Functions

          ### search(query, limit=10) -> list[dict]
          Search the knowledge base using hybrid search (vector + full-text).
          Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings

          ### list_documents(limit=10, offset=0) -> list[dict]
          List available documents in the knowledge base.
          Returns list of dicts with keys: id, title, uri, created_at

          ### get_document(id_or_title) -> str | None
          Get the full text content of a document by ID, title, or URI.
          Returns the document content as a string, or None if not found.

          ### get_docling_document(id_or_title) -> DoclingDocument | None
          Get the structured DoclingDocument object for advanced analysis.
          Returns a DoclingDocument object, or None if not found.
          See "DoclingDocument API" section below for how to use it.

          ### llm(prompt) -> str
          Call an LLM directly with the given prompt. Returns the response as a string.
          Use this for classification, summarization, extraction, or any task where you
          already have the content and just need LLM reasoning.

          ## Pre-loaded Documents Variable

          If documents were pre-loaded for this session, a `documents` variable is available:
          ```python
          # documents is a list of dicts with keys: id, title, uri, content
          for doc in documents:
              print(doc['title'], len(doc['content']))
          ```
          Check if it exists with: `if 'documents' in dir(): ...`

          ## Standard Library Modules
          You can import any Python standard library module.

          ## Strategy Guide

          1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
          2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
          3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
          4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
          5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
          6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
          7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.

          ## DoclingDocument API

          When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.

          ### Properties
          - `doc.texts` - List of all text items (paragraphs, headings, etc.)
          - `doc.tables` - List of all tables
          - `doc.pictures` - List of all pictures/figures
          - `doc.name` - Document name

          ### Methods
          - `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
            Returns tuples of (item, level) where level is nesting depth
          - `doc.export_to_markdown()` - Export entire document as markdown string

          ### Text Item Properties
          - `item.text` - The text content
          - `item.label` - Type: title, paragraph, section_header, list_item, etc. (lowercase enum values)
          - `item.prov` - Provenance (page numbers, bounding boxes)

          ### Table Access
          - `table.data.num_rows`, `table.data.num_cols` - Dimensions
          - `table.data.table_cells` - List of TableCell objects
          - `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`

          ### Example Usage
          ```python
          doc = get_docling_document("My Document")

          # Get all headings
          headings = [t.text for t in doc.texts if "header" in str(t.label)]

          # Iterate with structure
          for item, level in doc.iterate_items():
              print("  " * level + item.text[:50])

          # Extract table data
          for table in doc.tables:
              for cell in table.data.table_cells:
                  print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
          ```

          ## Example Patterns

          ### Counting documents matching a condition
          ```python
          docs = list_documents(limit=100)
          count = 0
          for doc in docs:
              content = get_document(doc['id'])
              if content and 'keyword' in content.lower():
                  count += 1
                  print(f"Found in: {doc['title']}")
          print(f"Total: {count}")
          ```

          ### Aggregating data across documents
          ```python
          import re
          numbers = []
          results = search("financial data", limit=20)
          for r in results:
              matches = re.findall(r'\$([\d,]+)', r['content'])
              for m in matches:
                  numbers.append(int(m.replace(',', '')))
          print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
          ```

          ### Using llm() for classification
          ```python
          # Get document content
          content = get_document("Q1 Report")
          # Use llm() to classify sentiment
          sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
          print(sentiment)
          ```

          ## Workflow

          1. **ALWAYS start by using execute_code** to explore the knowledge base
          2. Run multiple code blocks as needed to gather information
          3. After collecting data, provide your final answer

          ## Output Format

          CRITICAL: Your final response MUST be valid JSON matching this exact schema:
          ```json
          {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
          ```

          - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
          - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.

          Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}

          CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.          
        role: system
      - content: How many tables are in the document? Also tell me how many pictures/figures it contains.
        role: user
      - content: |-
          <think>
          We need to find a document. Likely the user refers to a specific document loaded. Let's list documents.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"docs = list_documents(limit=10)\nprint(docs)\n"}'
            name: execute_code
          id: call_qmo5y6dt
          type: function
      - content: '{"code":"docs = list_documents(limit=10)\nprint(docs)\n","stdout":"[{''id'': ''8b7e395a-b2b4-423d-8c65-53d7ea800da1'',
          ''title'': None, ''uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'',
          ''created_at'': ''2026-02-06 11:23:09.536783''}]\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_qmo5y6dt
      - content: |-
          <think>
          List shows one doc. Let's get docling.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"doc = get_docling_document(''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'')\nprint(doc.name)\nprint(''tables'',
              len(doc.tables))\nprint(''pictures'', len(doc.pictures))\n"}'
            name: execute_code
          id: call_q4w5nsrp
          type: function
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      model: gpt-oss
      reasoning_effort: low
      stream: false
      tool_choice: auto
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          description: |-
            <summary>Execute Python code in a Docker-sandboxed environment.

            The code has access to haiku.rag functions (search, list_documents,
            get_document, get_docling_document, llm) and any Python standard
            library module.

            Use print() to output results.</summary>
            <returns>
            <description>Structured result with success status, stdout, and stderr.</description>
            </returns>            
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
          strict: true
        type: function
      - function:
          description: Result from RLM agent execution.
          name: final_result
          parameters:
            additionalProperties: false
            properties:
              answer:
                description: The answer to the user's question
                type: string
              program:
                description: The final consolidated program
                type: string
            required:
            - answer
            - program
            type: object
          strict: true
        type: function
    uri: http://localhost:11434/v1/chat/completions
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            main():\n    # The document URL (as known from list_documents)\n    doc_uri = ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf''\n    #
            Load DoclingDocument\n    doc = get_docling_document(doc_uri)\n    if not doc:\n        print(\"Document not found\")\n        return\n    #
            Count tables and pictures\n    table_count = len(doc.tables)\n    picture_count = len(doc.pictures)\n    # Output
            results\n    print(f\"Document: {doc.name}\")\n    print(f\"Tables: {table_count}\")\n    print(f\"Pictures/figures:
            {picture_count}\")\n\nif __name__ == \"__main__\":\n    main()\n"}'
          role: assistant
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version: 1