diff --git a/tests/cassettes/test_client_analyze/TestClientAnalysisIntegration.test_analyze_search_and_extract.yaml b/tests/cassettes/test_client_analyze/TestClientAnalysisIntegration.test_analyze_search_and_extract.yaml index 5c876be7..2858b919 100644 --- a/tests/cassettes/test_client_analyze/TestClientAnalysisIntegration.test_analyze_search_and_extract.yaml +++ b/tests/cassettes/test_client_analyze/TestClientAnalysisIntegration.test_analyze_search_and_extract.yaml @@ -8,7 +8,7 @@ interactions: connection: - keep-alive content-length: - - '10361' + - '3480' content-type: - application/json host: @@ -17,53 +17,35 @@ interactions: parsed_body: encoding_format: base64 input: - - |2- - - 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 - - |2- - - @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 - - |2- - 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. + DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com + Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com + Ahmed S. Nassar IBM Research + Rueschlikon, Switzerland ahn@zurich.ibm.com + Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com + Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com + Figure 1: Four examples of complex page layouts across different document categories - |- - 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/ + KEYWORDS + PDF document conversion, layout segmentation, object-detection, data set, Machine Learning + - |- + ACMReference Format: + Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 + - |- + ABSTRACT + Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. + - |- + ABSTRACT + Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. + - |- + CCS CONCEPTS + · Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ; + Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). + KDD '22, August 14-18, 2022, Washington, DC, USA + © 2022 Copyright held by the owner/author(s). + ACM ISBN 978-1-4503-9385-0/22/08. + https://doi.org/10.1145/3534678.3539043 model: qwen3-embedding:4b uri: http://localhost:11434/v1/embeddings response: @@ -74,56 +56,29 @@ interactions: - chunked parsed_body: data: - - embedding: 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u7ZcuaP8hryc1Y88V72fPNdYRrq3bzo9+McJPZ3gU7tUoMU8Mx6GvATERTzQU8k88KaqO4fGI7ygTUC997qUvVJI9jx8jR28VkADPBa3Q7sPZB686AoHPZsCYT1x0es89Q+8vCXnD73VLq68GJ+CvR/Snjx3jW48pT0RPXuw/bxWk0w80ZrYO1duEDs28xW8x0RmvFy3b7v65bs8WAFMvJahnDw+b0y8bhmBOjWXDrofPrg830coO22OPDsMEPm87+YVvX5sjrzM13k775e4O0CxXL1S7QW8oWg8PUixTrypDTk9ynyHOdNwUrp2JJs82FGsuXJFt7rqESW8dfasvEgQjbzk3cK8TQEHPMW2gzhnIzw8F17Auezq4TiKbG27uwelu1nMLLzm80M8cvjevM8CnbvGj1E8I9PFvMB3yzwym3w88/Ryuk5wKzw0wD89kFDUu5aWQDvAu6c8Q6qvOhJborwifzU8ag8EujBjfTsCsYW8G1oyvPrDqrp8m+87jwP2vC0etbyaeoO72o1IO7/km7tig9G8gL0hPOTRjzoFuDU8sx+wvOXUObyNSSa8TG8rvJ6uAjy3Od87aMNhO23Fary5+6U8W78gu7pNuby0Eri6GqzlPL25OzyirEk8egbyu0YdFTyuFEu8mJxyPEB87zsgAI69HBs7vJZvBL0ECDQ9q5pfPA+TMjzlEa28znh7OTnFnLx4BDU8OW9VPH9xprx7x+S59kPIO5g56jpfjpS8O0PfuoDSETsInnW8++QvvLVbXr1bUQy89dwCvYdG6juZeYm7gH8wPE6nkLuwgo88g3aMOw1ijjyAFAA9f6O9vCQsUbv3eV47JcqaO2nSYLtb2qk8Ee4evLjhjD0c6x085eVHPGRIKLz8gH04952eOmk/r7wjY248LeUHvBB19bq6oYS7ZhPZvDGWVryr9Ja8NK3tO5jpgLvB9yc9Kw+TPPzoMD1ueBu80OIfPPZ3prqE+8a8b92Tun6tK7tzptY79YSsPGqg0DxiQom5rdKPvGXdnjw53pG7fL7Tu+hwnbzUVmA8qBUfPF+znDymHhw8ZDUQPFW8mjxuA5a8k40vvIwKjLz3kge7Aba9vG+P5TwWdQu87QTCN7wb7Lz3sZG8f6ZXu1fDcDzcqT48kDW3vKmkTbzL+BE9rauoPOGuVLvDYZe85oEtvEoMUjxH9iq84mRLu5iPRjxKdsY6XVLVu+VmFLzNH688Fd3gOvD4mbwmmQi8Kk5dOhTIjDx3jcQ7asFvPEM5ajzm8Vq98n56PDWxjrw4/7G8K9DiO7Fgd7sWiDq8QoCkO1Af8LxLM7C807KEvEOKJrw7/3A8LhCPu/cDbLyzFDG9K0mgu0sXx7w1lrm7qAMCuvYZ/LtJeVe7M9cTvVf+sbsf8Bq8NPoovGj5ALyxCxI8zFiavLTLtbzEcUY85lQ1PREWHDyHHoc8Nb5fPBsCVTyiPqK8oZZnPLA5ITxkiww8vQl7PNMdwrwNeTI7hNLKvCW8EbzXLN+8wBdZPInsaD1RoL66KfaoO8zc+Tyqwn48OOStvIFjqzywOtW8Va1kPGtuljz3QZo8U9qMPHg5LTua6BK8Q4TMvHeUk7sYxjs88rGHPOqQLrxc3v47xCSIPO8GILw5BJg7XNuwu1i/lDuWXqg7dek6u2SQB7w6VDo9UY/1uEPi8DvvGgs8qNQxvNrwh7y9m2m8FOl8vXp7n7wb6JY8RyPEvB8jqjxbuYY8NKwJPexW5jyd/eO8FSsxvLiQKT2sflC9TYakOqLUSbxvN7A83z4VvLzIoDzmcqW7nZxku/wVIrxIUxq7809+u7PUQL05GAS9O/CEu82LibsUc7S7ATmuvPD6zTs1d1u8aeqwvKMOh72tRwO8hmcOPaEYmDwCgyw81RqWOoZZDz0vapO78CWuvOkclrySckG7ZY2uO2EBr7wviJO864kXvMdMjTzBEyU9z8kFvAOCIzyFHDg8N2fSPA03kbt3HZq8MWA2u/3h17i6W1K8VHeDvNGwpjt4vIs8E66kPOC+uDup2IQ8AV0jvB20TjxGl2m8zgiIu1WETLoQpLM8N/mTO9wDrTylYRA8pMVou/uFyLwbRxs9dF5VOquv0jx6h9Q8WHY3vBVY9LwmH4G7zKWBvJrlVLxHyZk7PssFvTeLAr2O9G07MA8PukS1Yrwnjpm8zVauu9KamTzWVRG8C+07vUKFVbtrpQs9JC4bvENFe7yjT4M8YHzhvEDeFb2kc+w8kDlQvMvdFLvz8c48myxBufZv0jzbwVy8P6H8vFfyYbwZqrg8Ef+tPP/F5TyH3686Hc6Hux+7Lrzmicu8M8OSPK97Ar13G/w8681JPFZ0Rjv/T8k8oyPlvMKCMbu33R+7EXwPPfnxmTxl8gq9Ia+gvO+FiLwNoGi81rzYvAW5q7tKVp88TuRSPMwLZDwl10+9Smz5PInHM70678G7er6IPKLRabx3H108DRL6vHL9mLvije+7hW6XPHEB1zxkKdG6VZWQPHps5jxNcBe8F5VjOrV1xjykOhc8Qv2ePH+qJbuZYtU7/Ie2PFg9DzypfnE87IsjPJ8jtDybWGw9cVbZPF9ZkTvUlGY8D4SxPHaEAD3DcI27frmevDDk07r6ylQ82R5ou3w6DDyjuWK7DTs3vFpq5LzQNYW7N5/FvIrZ8Dznx1y7OHsauwXHEj1gt1o8d3k2OmXmaLwYamA84YDbPBBiPTt6AzW8IqwKvep4YbwM3Ba9c++gOh9vj7yynKW8PoUNPb+ovDs8nAQ8SMSDPEQmCDs+R0M8ZxHDPGfETD30JTa7uwZvO9j42TxAlwS8hSaWPDFacjzbSpe8RUSFvDaXfzzAcku89Rt7vK7dBT2pZQY9e7KZvCutkzxpVec7yKmRvGd8BbxlYVy8UDI1uw3zqLzwpow78PMYu79qj7xiJRS9VtAJvDzIiLzCFvc7Qb0VO/bpA703oEc8N2vjO8ufnbvTTde8qLSnPIqhlzxvysU8V7x9POqwWbtT7cQ7FCmKPN/oibzQxRc7tlOlvBcgLLxt7ls7HlkjvOLPwTw0+ek7TeD4OwZjjrxMfFq8vzskPKft4jwQnYg7ExuMuxZnkrtIljA7S9NxvKc9sDu86aA76UwAPatIvbuiGMS8qY6ovP5RPTzZDBG8F4f5u3KMD7ytTUu8mTUhvb/pYDtTGaW89EvMPM+++ryhsxq6tEhuPHyskrmldYS8R2jzPCnOYbyS9XK8PBPkul62XziKtDw7kYaXPDBBuTtfuyA8ty4IveglOLz9m/i8iHTMvLEEELwcj3q8WqS9PEWm8bxu5za7PkvBvM6CMbyW9O+8qzLavJ/jornoCWY8QKSevGQLaTzF9Dc9GrSUPDS0hbwFd5O8C0hcvc+5nDvShW87WI6EPI8u2jzRd6k8IyELPOrUc7zjoAA9uPE7u3dmRr07+HC9arHwOsMGFrt4okE7Eau7POcKy7ymSIM8AJe2vOTbmjmNlH08lLbhO5d74zxET4a7i66SPOIpkrxhPg+9zF65PP4Ihbq3a648VCiIvN5rN7weTGo84ZMPvImT5DsJlyk8WC6sPKDlNDrae2c8TdnQvG+vvrzdwhm88ZyrPP4kHjzzfzs71aDiPAxPeDyUzh87GUCyO9gP1LwZ2bC8KltzvEpBFjrmWIk8snLNO9azCLy+WX+7wl+vPMddBzx2JLw8jcGCPL7nljueRsu8z56Au7IclrxoRAG9EyjPOo1jGb0WlWU7ErxavL6vzjwKxhy8k3xSPOUdrjxAPJO7PCWBO9NTRb17osG7dnGmvH5YVLzLXKC8FxEKPGFdIzrQZ6O8q0eauiBkfbtz9QO9sQJZPHFDHbznnME8fy8ivB6UVjzUGam7HCpzPXKKW7yERA28S/aQPJAfLDyaF328xuYxPFz97zxyNmi85zGuu+hdojw8uVI9vqByvLorW7u150C8G23dOYOYYzxAcvm8whcSPCwrgjujHue53GsSPRbXlLvifK88UEEpPEda0bye55E8aLZJOcia67uwAdg72p72OyPjLLuln2q8SDKYu/Xi7Dv3CVu8tKYwvCDiWbtES2G70FuNvAn42DxqhcO8zT8GuoiBsboRPxs855KlPQDLrDt33Dy8G/SwvCBArTwQL5I7Ys+dvHLHkryH7Fc8yJcGvfJYwbvuGbW76eoIvKBjUTwyFcg7ddgSu5Dzy7reSmo8RQcnvKBylTxQoXU8QvDEvMkr+7tkU4O7CtZrvSjOIzu96tk72PdOPOSMmjtG/bw8G53JvKUKvDuDUEo9V/qZuzH6E7yR3TA8EqY6OrxpB71wzxg8oB1Pu+qih7xCqZ28rtmmOxB0BTz10Ce8yhKbOiIJsTxiQum6A6MZPEx9Bzvq2aw7lTu/OwjYTD36RLg7rUbxPI6XgjzSPu+7JnluO5LAYbxIwJQ8dVjbO3KNVbxFOiI97Ie6vCSojrvN6bu8IzKHvM1RIL2K7BU9SeNLvFS0MjwzsGw8OVe1vA8NAT0woda7i52huwd7pDt49Yo95uvyPB86Aj2ew108C5SfPGlGTTxm3jC7gcEwuxhZgrxfxaq6DY+DPMgyE70LasW8uIKqu4xG3LvYa226ogKPvIhbzrzsgAW9Bq4GPZih6DuB9PI8liynPHJcYj2Is3+7M4bxu4IfXrxs41i7oLgevHyDCz2cYe67Bm5cO4/tGj0LvfW8blwePalaHbxkU4g8hTalvB6dY7zztH+8jbKNPIYqs7xQ5FI7Sk0wvF5jzLu+pW68hN4evdYMKj2mr6y8KLatuxp9Ejz6+de73QFpPWpLHT0OMsI8W0TfvG6r4LvSB948LkC8u/y+Er366TG8idSBvDL+fTxTZ+28nbcROlthZbswN9i7u20tvZVAEztedqe89mLFu9RwYLv3LIG7uGKRPK6uKTpaCTe8hGEbvOYWIbzWwgI8A+a7vO0NPjy4FN+7mzlEvLBMRj0St++6Fzpku1UJKTuYbDg7YD5HvMUJ97xqVqg8J6eMPGW7oDwAW8y7ENykt6xxI7ybJ/Y7XEETOwe/Sr1LjO086S6SOVdjPbxskx88fTp1vKjSJrwkK8K7ZMAdPDkKKrw2rlc8zBSdO3uHijoabra8a1SDPAkCuruOfbw7jtbHPJCBxrzgaOC8vPaAPKL0przUlWW85rYOPEcC77tvZC28BvEDPBKnnrysAAo9DNFvOx7Ot7tH6ri7Ia2wvMHsiDuT3Y+8dupsu33mtTvZ9as8YqmvPDVwSTulp6w81MiMvHC6LTymEo47wL+bPLdvmTy4nvk8Qwa/uRT65zxfrx08iwPUvH0zAb0R9fI7/s2yO/DchrzwCny8Jb09vIrnqTxHprA8kg0GPb8RJb0QjJa8LPyQOyYd9juyQRo8FIdQOyRufbxS6s27a3dGPeOJhbuvViI9+FeSO7/IartFZmI60oeNPGfSyTzjrsG8I2J5PJUOkDxlcFm8cSiZPHzPFDvsxaU8L1Aevb4ozru8deu65AsVvf28V7yttku8im8jvDq7Rjl0rIO8H5FwPAknGTzGngQ7RNrTvF7D5jx/amQ8F0cwvDOrML0fLUU8tFchubGbRjy8RcA83PvjPOzgyLxFezA7a3QRPT29XbxLdkw89nlKPb5497sZBum8U9Q+O5Rw47wqZXu63UAvvWJ4PzsYNTY61ESEO/4wTTyUKQS99NvcPFfp37yF+cK8f46+usrYFb3DZle8T873vBVD4bx7dqe7sUcIvQnvurzE7lO8mSivPG7xjLxlf6Q7PimSPIi/CzyeGZm8D6/FPPwj9DxkFxO7iwc+O4jkjrzbVMc8fjWRvKb2q7zqL5y8Z4nBvLn3xry2w/M7jZXEO95OmbwR+zk8gQSyvI7xyrxYghE9D13XOrOOhLpRfy09lAkuvXE3W7pNioE8NIaGPFcCSDw/8n08DvOQvCPcV7zkObu8ZR4ZvXeHojzQyiw80fbTvAUiJT2fS6+61eMFvWZfVDtt+j08LkitvAsQ/Dx/SoE6EvElu5G5DTzLRwi8kCp6PJFkRTwvTGK8kFTJPJufQD2sQYm8NZKNvCL1Er1F/js8zUlCvCgmAT2IGF88b6IRvZuuFz3dS6c74TeYPPR6Gb3YGtE7tI60O08YSTtx/SY8L2svPAoghjuH9IY8abfPPHfx0jynbHi8P9UxPD4l8jru0nY7ZwtLvFgfg7x4boA8VwMRvBTe1DqRWT880PgevBL0Zb2zz4o8CpHEO2NXzzvnOIW88nOuPJqawbwIetI6RIn9OzBfnrySZ5A7B9YvPa84lbytlqO8eFqrPKOF/TvwMFo8FwYiPP3amDxcph09Q/yePDspaDmcEy69+pMjPQWBCzsGvhG83l6svF6Dk70hfK28LtNGvMuIs7zefHC8pQkYvU282DomYCU8sZ67u1L4pzznu4s8JN+ZPNDetbsE8he84UnyvKRfiDyVSUG8WuUsvFZMgzwvrjC83Jzku3ogfbpwc6u83OZ4vNDw5zv1wA489haIPHK3n7z2HWA8758zPcYkEzzCEuU79R0EPAj6TjwV4gS8fxMHvC1qu7qJwrY8ryoJvc1AY7xExGs7spIEOw8ztLzjiuQ8KwvqPBWcDLzg8JM9wIoluxqaobw8/Ak9FYrqumWvnruAOBm9LQbEPASIDr3k7k68zq78u5qpbbzxH6U8FIC8u9ICm7wouVs9/MTXvJ8RxLxz8e68h42mOzfsUTzAvkM7pdiSPAINXDzMLvy8uiYVvDfGJzxdgUq8aX22PCEXEj2cHMa8DwqbuzD8EbtHTlA7TB3ku9RCxLyTabQ88DiMvNnjkztGkUW80BulPKOfgTnIg8K8qSGoPJOMYryxCeu86o46PE3/YDxKVO280hcBvI8oN73zA+Q6loCBPFMYLDy0PUm8+OXivHpBvrwAy407hYI9PI2DyDznZig8k/LqvJk3hTyCMRK7n1SKO4bypblwubs8m0WMvEvMijxIWPs7bP5Auz3oYbw7YcW8ZNMzvBje9ryKmbm8iKswO4pv7LpM+HC86RyAvGjlSTypR9e6LzSivMhETbqip5I6ZJ+4vN6JujpGZOI7kWszPOjL5zyPRfG8rA08vMIG1jxn/Y47LhqAPfiKoLo3RAW9phU9vAMinbx6soO6z1ApOzZi8Ts9jjm9y6ZYuwyuXTuPJcG8AJGTPGShGDzDpMc6Qky+PPG9rDzLP6o8sPkuO4rknjtUMLc7gcx1vNzMvjuvJhG9DRMdPU6LhTr7V7Y6jLD0upW9FD0Kb4s8hBZBvPNEBzxhuAQ9IbyAO3W6cjuLWQC9gXGDOyUlaryVLZO5zHcGPe70ybzIGC+8HJiMPMJgvbxVRYA7lAIyPDNxCD2IMPw8NwyMvHddRT32dSC60kqVPAZKy7yEZt+8xb6UvMD4TD3Bp4u7p7KYvHEThTz9dxK7E2/OO1G/jrzw5Yc5+7Y+vORwDr2adNi7vKgcvMMQhbwRfMI8gKSHvCc17Ly32Oa8nmaavNbgVT2Y6ai8KeqevN9IcbswJ6S81gkcPNbr3bxR1JA7dqc1unZKNjziakS9I3kRvQ67U7zRWQI6BeAqOziZQDzuJyy83Iv0OwSoEbwpcBO77gYlvbyUwbxnsKa8lVtYO4by4DySu3+8z8q7ue15cTmBMzg8a4kTPMN+JLlk+CQ8LXtHO3z+GLzZgu+86lDou0FSQTy1uDc8Ic+VO969aLsx33W7tTrKPPrvfzwPDyK8gmx2PGeWE7xp0fC85EGnPHuEN7tLlCc8UWvLO/ueqLwMrhk9/NW3PIv6hbwY3RK9/u7DvAnK3bv1kiO81ru0vLRIpjwpvSM9EuBvvDv707wwSb+47021PLP6rDqwiFy6v9r7vJf01TuRfzw8MbPHPARBLrwhQuS7sbIlPJ7QsDxGvI28qdGqPFYUCDzDh327VwwivGkbvTwXeBy8N0YPPLlBBTyN+RW9Isu5vHLLr7xfHui8bUHSPC/WybxeIbC8wWhUvEngczwF0Pm7DEFtPJW6yLudI8k5lr1oPATs07wv1wq6ACNmPKp47zy8dsO8kiveu6pDvLyAhc48pOe3vHKjrDyK2YA8TbbuvK6h7bpey7m8nuXUOgh0mDxI0Kc8+3pdPAzerjwoICM8ZV0NPOCDGztIzwk8V1C8vLWhlbxGAXg8v6Ewu/p+xzuazTE94tPtvK5ujTxTfdw8ho/mukJNOrzOnAQ8hX+ROxkNCj2THQa8OS4kujkqJTy5e7u8YIaRvJ0rJT1SFpC7bTvivAvtOjzRJ7q76frmO+Vjm7v6LyA8lybtO4scSj3HWze99N0xPEZbWbpDMtI7M3ibOkZhkTyqJVe8i0VbOo5prrt+M7U7/40RPAK7BLwEf2C7iHcIPAcykzzxyLC7kUloPKEScruQlkE9oc20OrtZ2bsyiCq8lR+tvEIs9ruIMtY8UzAtPd3LKbwq1h+8WW4fPKeRE7zXXJG6ANfTPC+S4zsXDk87Q/sjvHBvMDwlTCg9zzf/POmo37uUJdC8pHbKvDoYpTwwYA68/3x+O4jUFLxxLqi87CjlvHQoiDuhPoq7vCdgvFQcAjzRW+Y7XyyBvKhNEL1JLq27yy9UPN8EHj0yo2S6FpAHPaPeIDzSp7o8YNwJvAMMurq/cZg8MFZbvN2VCDz6fNI8e72MuyIt9zxx8xc6rzfpO6W0sDt+AcW8b4ioPKwfvbtGkAA9qvpWPE/vsbtRiO47mZk9vNcTHz0fPCe9TuX5uki5TL22UKE7MOW1PG6T6zysFOs7DtL4PKgEZbyXDks9bQDqPKj5wDyXa8E8Ab/lvLUdqbnkQmo89SqAPEk5Prsivgg8BaJWPBf/BT1HvaO84ty8POFEVTqEuYo7Ky/KvDFjjTz4gS29hEMDvSMZGz3/sRq8lhlyPH8forwX96m8tRjsO8yhFbz8kPm8u4elO4qojLyWHUA84tZFPcbHt7wHYXG9uQPBvEsj+Dtx1IS82Uu+O5sW2jytnWk8TYgUPO1T7TtL5Kw87/yiPH++1TwxkVe9Jeycu0S12zlOZDa8AZgZPMb4W7mlKuS8PnMiPGcab7y375680H2MvFCC+joPzIe8sLFWvOorGzwB79i8AQLEvLpqWrwu5uk8nozbvEzvWTxM00E8jhqYun2fpDyDOGM7ri3cvPqk1joHzim8SYfSO4No1zyIIZy6+i2IvAVgfzzS5SA8JSSGPI6Pz7z9lDc8OdpDPPzLoLzCqfo6Tg98PK5VabynGeQ56d6UvAZymLzaSRC9PkkwPJQpCDyD7QI4O7WSvLEGLzwtFag85r+ROphP4Twg6uc7q5OuOnf2GTyjqBW9lH+Xu6BVybutTSu8wp+HvBtdkbtIAuS6P1rnu3hrBL3T4ae8qiL3u510Kbxhyya9x4WWvOq5EzoXWRq9OAw1PC6dtzt96Uq8RQcyPWsHkbzC/sI7cKt0uq/7yTz+3bc5uOM3vfVmhrxj3uu7TvUFvdtOgTxrNl88gxb+u34YYDunvhS8xeUVPI/63bzpcz29NkMRPLPD2TrwqZa86tv8vLAL7DsgGoe8XXAGPVQ8M73Mv3E8JX0bvCNKerynceC8b79JO+ZAsDx/FdE8jkdKux3A47xNlzs8cJ34O0hk8DzXgHK8yNK1OxnOgDwn3LI7p7zHPEgg6bySuhY8IFqKPDChwLpPyd4648vdvMPBBb0yfrk8huwCvT/bkTxMhKi7xmDuu3UEX7zuLVo8RP4xvOSezTuNMzK9t2Y/vQv2aboEfnG8RCzRPN2fz7yiiAQ91tcyPKKeHzwrohW8DPjguzIPjjz/Dp28VWTru+hbx7zE1A87Dh3YvLb6DDy6y1s8w99DvJ3qXjrhQCS8arXROVjSZLpLFb+7gPoPPK0D7DsYI7m8eU5TvIMCuLxnJ6+8H4EzPPCeOD0//C28GG6jPDx/Djxa44W886u3vDwui7yYafo7l8udvGpuwrzqPlc8xJsCPS/TtzzP1Ro8EOcOuiT0+DzZCjY8K2gePaFiB7ygXes8k0WTPJf5RzxLHZ88aZvcPNHzljtEJT68OOJLvEjPibuBOUs85IvJu5A91bv+qDw8/lC1vKDtszy8poa8HBG4PFMHtbwKCSu9TheqvFcsuTyvcNm8KjCuPJJb2jzABS67UI2pvICTZTwqzJq7gpHGOl4GEbvT3a075WtNPAcSiDtsmio81qGAOm9E3rwMPdS8OqMwPNsknzzpYMA8oFwXPAXTeruyRSw73hqPO5hnJTs/IUM8equ/uj3h8DwCn5K8Sd6svNDmQ7zpaJG8WQypPNGMbjtFCpI8GXnNPPdMTLpvT6G8eqkUPbf3NTxN9ti8TstSOmvKv7zJJj+8hG2yO9lgyDxINdU7OtZpvHBimjxSe127nRH3PMO5cLyUqkG86lwJvISmoLw+Gji7JHyrPP9oWLuQsrs8xMCyvBSk0LznZ1i8cOb0OU/aYzwMKjG9vWqZvE85rLvkBVY7aSSOOyL9RjxgWaA7IWmyPNjvsLvMZ/47OW1BvHC++LxccyW9m8OIPDwxfLwlDEU7pJrBun1VFjrdLuu8+OQ+PZ1DE73VUPm73DnfOuyibrznJBE8CAScvJNHajsqQxM8lsGLPOaSFTwQpDo9DLriu9yoeDxT8Ky8/tPLvNxm5jw6vRC82eV5uxeSz7yiBru80OcZPK1rp7yJo8a7HO2WvNJgA70mpmo8TxWYvH2mNb3jE1C8tbjju1KEbTzaRTu8JBG+O89oBrzolky8HUyKO2SWXzzl+L48GAjSu/t5YDyqm4y8NMHwutmL/zvhqM28vRayvIltJ7299OE8zz0ePeWPwDw55xi90YxAO2pF3zyCUb+8cwMLPC/n6DpX/II7tM6dPEJRjTxCR0C8iWDMvNM517zrEr48BibBOW+SxLyJoYU8z+o6vUXZ27v2oC+85kGGvN1bAz0WCKQ8g5oQvGBqKrvSX/687mMbPUkECLsZPsG8/hTuPL7c57xZ/GW8hldHuogCKj0Q4qu8jqqLvCIWc7yrI4+83sNUPGS0oDw/lrQ8f1APuOdyJrySy1q8cHDAuzCJZ7wzlc27u/gPvOCh9DysQxg7ywBpu9Em4jpNTTI8zHDIuyM1k7xE1zE6Ol83PN2/lbuqLKs8h2QiPXPL+Ty6Pqm8yAHBOgtZhzxZ0/282HBTvPNwZrxFli27AZSqPLgef7xZhwM7FE/2PONfsbwJXHm8XhF8PCR8Brvpdwe9nTRTvKnUeryHLmU7D1ydtwGwRjxehCO94O3CPEUukLs8QuQ8KAuNPJEek7yYpRw9Qw+aPORLN7xXaEM8sJgDPB4A+bxFO1c8X5ZUvLHHQrxxn0S8XswOvF0PlTlizsa825sHPVz4nTynDCu7CQqSO6gSe7rDVw28Q/utPAJQJrytW5Y8EHEiPPkO7DqR1gc97HEnPN/ZQbz6crW7AfxoOYfN7LrJd9I3aekku/+v4bzAUP08+7+cu9sin7xraxY8vWaSPDm6l7xTC4e7JSycvENNVDwxGrO7ETsZvR6qBL0f6Y875tRsvLgTsjyCmja8bCiZPGnpYzvNkqO7CGHSvO1c5jxARWE75tXwOqPgID0vxyW8GT/Xu8CMr7zF9dc8LxYAvU9GGr2+7lq8lBH9uz48pDw3Uco869UBvDzLojspO7w8i8GjvACqBDwj3B28hyEXPA6MHb03o288A5PFO0fx+rxHcOE77exIvIqs0jw01dG75iUDu+kSEj2XsGu6P0lNPftTmjtjtRg7iAvkOhinnrwu14w8P1bBvDLK8TwWRlg7yM+OvL5/lbs106O8tLkZPdqbdrxC0xK73V9WuvxVmDukgMw7BRgavMaAAT1zORo8zgYBvd60pTvGoXu881Yau+hT+7tWiwA9SMZyvNZQ/rxS2B275Cc+vCAO1zwggsG83E4ePTMV2rwEO5q8NMApvb03T7xuXdS8VTIiPL8+kro95gU8jROXvLYuUbxGJmE6Ct/IO+y5hDtCJMW8dNOuu68Ij7tzCkM8SBpePIahtLuQdPu7hjjnu7s7pDz98386yNogPJY/sjxilgS8gDhLvA9us7w5Pfk73LMlu65lnLz8lTC7D+4cPVrJGDy8rbG834YKPHeVaTpO3sw7jJnOPDBPFrvUpbq7Kc1vPB+9mzwp1BO9eFE5u/jQEr32uXk7j+wfOsSmt7yzACw9goZFvJBO0jvZJ6Y8wZURvcZtpzxWMAe8reMOvLjfDbvPx8U7pyZHvNGaHrwWD4A7NiMcPWQ11jyNupK8qruSuwfBkLsIh6g7sSFGPK1yVDxUimu7za9JvGnf/7xP7z88xrppOwAaEj2Og4k8uPxbO11tkrs7nbY8df22vEC1Vry3dc66p5y3OxMDDDsG7Ts8eSsJPA6P5TuqEbu8NLhIvF8cRbu8A0+7BJK8ul1tvDsEZiS7PQbWvNyMAz0hAVO83RobPE4mUbtuLa872j4dvNYHqbzw+Yc5+pKcvFYSE707Cna8iQb5u5liubxn20Q8tnSzO2D4CbzvU6+8vHaovIKAiDzo99e7ewLVO1VWibwCO5W8M66TPFpOzDwgoWW8IxylPAnIujtFtNW7evBrPHODurxlphi7IYdNPD0KvDw4+uG8AHbCPK3wurxveQM7TJajPJs+gLxRSWY9ifXDOz8fdzzlxz08DZdCPCu4zjuEXvK7yd2NPPnsGD2movG6yeCVu6Uxjjyig/48tNSnO8OIGLyF3Yc86jQPPLdLbryt61Q8x6KMu+LYtzxteZ68zvOMPEtKMTt6/RC7n4BXuehKyLophTA73uzUvM5btDyC9W88AiQ9vEHN7ruslQs5+GZ3vPIddjv27n28zcMkPPZgFDys+c47LkXfOxHQjryPUJg8SicAvCrktby7FCQ9AnpvvLI6Hr0Ld1m8HwpPuwU1WTrmHTI8732vPMAmMrz0WB09PYc1vZ4rYzyurqW7iEBNPF/Wjzw8uPY7CH1+PMPNzbt+S8Y8At/1uyNtmTxKEGu8dqwCPHKNrrvfK2c8Krs3va9sIDz2X+u8qr+cPGPSBTyMiA+8igG1PNFotLtNLZU8gq/Uu1Rr4TwRX5s7VDYKPPKWjzoAcRU802SoPJNsybvKin08sx87PImnhLynJT+8HZyNPIUB/bsabyI9XwvtvDBMmzyWFvO8kjZlvGcosDyfd++8SaUkvT5WprvjORk6g4oTPWSCEDuypzO7WnyzO7XKqbuk8hk8m7GUvKL8ATwpEr67PCwNPP0QAb0G0Uw9Y4MnvMv/+bqeL007v7pHvDIQErv7rKQ8kq2mO8ipvTtdcas81pT1udLBhbyoDrO7ifIQu67SUDu1fRK9e7qROmdx3ztNqI67G3GuPAVpY7rmkJ88t9K/u149ezxM+ks8AQ8LvSoiwbxd3Wq8/nMtvK2PJTrANyY8JVjKvJjMtzuKCDg8RGZUPDby+zwreJc835ThPDkNizxBYeM8xeXdutqyGbz9Nzu6qUH2uoEKAD2ISdG78bWZvDXlEL0+mG+8nSCWvBW9HDyCnji81jm1u/pVu7yDdqs7UTAgvNq2jDu2B6C7G3OZOwfaBz3yR0G8/RYXO7HanTz+krI8hZvAvBBON7wN/W68C0MqPA== index: 2 object: embedding - - embedding: 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 index: 3 object: embedding - - embedding: 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 index: 4 object: embedding - - embedding: ruqaudIQiLxu8TU9NY+1OqJkqrrjpXI9zMp8PfNwWzwZooI7AaQSvEYZ7jyGi4I9vLJwO5cKSr2P1GC9xedkvX17a7xnBWS8HKO6POuHUrr1E1m5OMwIPbIfNDyIRLS7pSulvPcolrxPRqG8F8zbuyQoCzwFU5Q7/v10PFcitrwRlA66F00LO6RHobriUc689YwEvWWiczrUPkS8lUizvISSK7x5OxO8qU1lPEnO4zy3cSk8HcWjOltkADxYo8O8yp4zvDwcOLxCZ0s7b+emO8KfaL0LUmW81QT5PMqfK70Zwgs9aOzeOn/ypbpMbyA80TcgPJ8WurynOso7Vw4lO/Unpbtc0B+9OQwkvJHxtDt55aw7bLYYvCierzw95gC9U5g6u49NZDxnU5k8GEu7vIKNWLxruyu8ZIgzOxja7Tuuecy8Y6LPOyz/zbvVGSA9N1O/PLUk37ys7ag8mH8+PC79Wzy1Iq+70tm9PMzi5LkQbWe6HqtZPPwNvrvE7GY8pnDcO1htGrwOQEg8buzjO7lmh7xg74i8UAg9PbG5I7uaaiU9gjQuvMBsJbzG3H+7i0mFO00VljzY2CS7a0lkPAQcCL3bXgw9Ey47PNUWuzpRrWU9qu75PN0pLDsQx4A8B3eWvJ+aZzwz3cK734m8Otq9zjxtQGq9U0pVvASCMrxnRUU8oCYLPCJwtjvtqbu8ebVXPENmELycK8C8N6y2PHrSjLrskRM5RCOZvCERIDwkPga8QLKdvFNEO7vfSHc73RTLvLluPL2Nnns83JoZPE8ktbsKzI67pAXdO8vKlbxrppK7yl9bPBU1AbyQ6+Q8bCD4u0yxpjyo4p08oVSUPOo9AbwiwLs6ZArdu1O3yDxRT/I7FiojO4/09LrS+6c8QLP0uyAcHrx51T08T3f5uxAjd7u9BbG7A7YbvOfzHbt1+fi8J0+IvN4lZLyuOXM8ZhnDO4+yhT3xPh49a0WsPGpUMTzQnKm8ZLHTuRwDxbyrvDY8IXGOu4bAt7u4aR07Q0oiO59v3TzgBoY7BtBFvBkp4bxdgPs7LtCVvGHEzzzN4jy8gA+CPO3O6LvY5Ua8mp8wvPSuZDpQOfs7fdLHu/hZATyWY/i7M0nrPK/GHTxfj+8740dLPEkGVLvfqqc7bpylvDg737uGLuY8StBJPHg2qbviMDS8pkM2vKqHa7ziMm+8OWmCOTX3y7ofqSy80W4CvPe/zbsAUl49lwkVPWwS5btxoZo8jRdyPPV3W7y7JMg58C+FPCFuzDxfike975AJu8o9sbwoW0K8nyNKOlxaibxsuC28CWjUO5heX7zyfg68LVRkvKnT2DnJCVk8YLCQPOeBgLw1S/a8xpwmvHqZLLzQnei8w6sjvPLOIjsW2HQ77CUWvTx9Ibx0r/W7WNrZu093ETzHT8w8JMsDvTuJ07pn5Bi8TmhgPXGYRbxKxJU8qOUjPMNCfjxFElC8Zq07u49UJLy6jE47F2qFvLAuU7uhUZM7piGcvJ7WtrtBJbG8AHaAPAGGgTx4wyS8JdmBvOIJqLtmHCo84IiBPHOaPLzdmWA7xpbEu8+bYDxkXpM8mRG7OfHVR7y4XpU7shxGvPmwprsjgr67P/jKPGE6WTxWlpA6PhhDPFI/HTy3Mb08VhQ9vS4qbbuNpJo7C3M3u1xkqLkx/+s8fFQEu0SudjtI25I8W2G2u8Fl9byyvNA7wqRPvdD0YjnNnAG8c9BAtVJfFzzmI5883DyKPFzEPDyRrOM7F1dCPKiuUTyJpZi9guclOjhU4Ts2Upq8BkTSu90l1TxmLb47oP/oO0z4w7z1/IA87xq1O3QKzrylKgm82EUhO8FuDjySGnY8hJIWvIgmIjx6BSa9EJ30vCzn/bzauum8NrntPInchru98xW5BIhIvEQe6Dw/Mcy8E70MvGmHhjs9YcA47Hs3u8WgybwNJsy8mOpAvIQajDzEi5m83U64vADtaLxdZ488+uQXPU7oGL1R2528zLWouy/K2jynZ1M8CJ+su5os9TwRyno7GsDiPEejlbz6VTK6m6CAvLK9i7zmzww8GaiavFkCOzygVzS8DgnLO3fsIbz4Vo064M2RvHa53LzxSb08yqniu/kmarvi4489aNfsvJdDmbznJP28A7P1vDmZLLwsxiU9uu7ovOuHh7wUEfs7b+WXu0iOFrylvVE84kyvvNjwVDzrh6W78+2ZvbsSlrwJ3Mc7dagsO/m8X7zGP0I7liHjvP1TTbxJvxA9ZILLvIY2Mrw2XBg9JyS9PKNOVDw3GsK8n8d1vRCilzy8Krw8Ce7dPFKDuTtL6eq7kwXBOwiO+bwJSYK8nleNPKKE+DvPIoU8q83lO6P527xlE6Q8VKcHvM9oOTuJn9y6QFE5PDRSy7vgAxy9kg0MPObmk7wJ1my8f53pu9B+WjwtjF48nSiUvL20FLwiAAy91kmpOinYKr3nmtI8yh7ruwlk0bxyMUa8mBtfOvk0I7wZUcC8KuHKvMzF8Tl2eHs8QCm8vG+t/jwCG1G5aYdevEDLMrrTrnq8I/ScPNDBEDxiPnO8l3f2O+GWibwG3BI9UIhxPEcgGzwXlR48EQmqPB8KODy/7Rw7y+rXulQQAj3lfXC8xIzVvCJHiDv0Wb+7kIu2PJ89DD3s/KY8CLlDPH9NZzy54MC8UPW4vGewEbw3Lpo7t9sBvFEbSLwxCuA8qezHvDhqsTrQFlE8kyr3uiV2oDzmEz26nFvku4EAtjxbA1W8Iw5Su27PgTktjb67CgIYPJwAA73NIN28v4XevAj2gLxf0Do8sXYLPAmRMjxSyAc8x7iHvKqyNDybARo8gpIZuwGfkDw5l668gC2Qu/obazxA0sK8b/SxPBsP8TzKzic82DcBvO8O5blqiQ07DHeHuyRhIz2w5Hg88zcQOoMtBT1e/AC9wXvPPLxOjbu2LVy7EwqGPIKcwrypnz+8phB9PHaqXDrCI7q7VrdCPFbysjtOzUe9ENu8PFGmAD2zt1o8AkqCPChN2re8uZI8Wu3Cu1IjE709lXq8gSmRO3hfC7taXwM8zTwgvE0x4jzs2zE9+U4LPPhIgLxnl2e7X18oPKmkEDz1ga475CyAO2iHxbwL78G81lcePIDcu7x/Q6S8ZQXKPDRmiDz7A6e7ZxMSvV46yzyvMWS8d4IyO6DNBr1i5iE8IP87vXEwML3Rxkq7DF7+PI33erymNkK7sBPcPJpUzLsqhWa6TLIEPQefCz3o1r48iTmwPEnhFzucRaY7hNWhvK6sArsuVYe8yFsVvMp28LxWGqO81jUIvZtdFbymDJs7XfAhPIMnO73zY1a75OTFOmsGMrzOK4g8NN0XveH6r7xyq0Y8kzwIO/HcLDzQB3M6chFGPG8b0Lxy9um8HwDou1tUyjz8xtO7YFFHPEkiwjylcQQ95AO3PCpk0LxTHuQ8p955PI8fD73FAja9IeM3vLAeljx/1jA7C0G5u7O5ODpH0+A8awoAvK07A7w41Dq7VCZYPOfwTjxYQiA8Y2cuPL2KPb33y4s7BGILup8V4rvxBw+8cuByvIK1iDzIAVE8lwPPO7jnMDwyhwk8SCyMPB8qrzu3Fhg8GsTnO2Q/9rzP3gK9GHeCu6z7SbuZ4ci8noG5OygiDT2VJgO9mfehOjzLeLz0TKe8hBcyu6GaKbrBm+M8/9BNPBrwELz0nIE8o/zkPNp0HryELoM6EE6FPN5MtrwrKvi8VgqVvC7akbwc+4W8Ygg3vNzetrylius7DvxkvN+gezxk6ga9Xl6rPCFKkLxO26G8r9wJvW6E0bs2avg8ThDavDgljLwzl9y834e1PGqbDzx0fRq869EhvNazsDxYL7G6R9ANPLUZkzt9hr48lGCyvHsxkDzw0M080MULPaOwPbyoI2E8UCByPO2jlTyzbYq8GcuNu5mk/TsGPQC9L6EpveCI5rvchaM8eIYfvRfs4zuivl07RriXPOkz5DzbzO683ctAuyr8Cz3VduA7sw66PD0bnbzw3h89sNakvK1L77zzzKa7ogQkO1XSNzuiyw66ohfVPJuSvbxzu6E8LMX5vBIrZbvwRnq8V9oLvJ8AwTyOBaa7jgfQvMw16jzHX+u7EmFfvMDsgjyFuxA8u6qLPavFHrxULKm8GYmnvA51Gj2k0ci8ctiHuyQqpjv/KlA6Gi2OutijrLxt0bu6fESfvMLhFzt+4988VT4iPNwmxzu2gkA8wDvnu+lyrDwChvY8aDCsvNZOwzy3WUu8AC8SvANkhjwQBOw7n5mGvIYTiTyqJNo7rZqDu9s6Cb0MDWE8xQL6PHIvnLzqo6s8hhuUu3sWpLvMqzW8lIm7PAXtj7xMOyy9BiyjPK8ZcTzkwm48qXT8O33kfzyUJcu7d4BNuwhAvrwVXHW8OetbO+Awnzy66g89NRGJPKPLIz0BcAa7d2KuPD6QNbydQX880oFbO/OrED3P+Do8ZUVTvMcwljyQqzq8+LG5vEcN4rxnxP88xDmyvAjHIryYSlG8j4lSPMsZgby6IIc8fkepvMvbFT2OHpQ9E4MsPGbcjbzBeJ087CULOjOuEz0+h2S7niEdPGJZRbyKdCo8oTJmPNWSIzvgoUK9+iQOPa2Qj7pXOmM8Ky+RPIPXBr1SNgK9fPjqPK3LcTwkqQE9LO+Qu6vi3zzbuUo8JpLMvASnSz00ZZC8XMfOuxQ+/juSvX48xjgSOsTPtTypsZw8HL+fPBOLErxwUVq7PBNSvYjpezz9qpu8ZPmYPD586jtU3Aq7zJriO2NdLTykVxG8wBEavUKUAj2L5gA8fJHMO9xxcjyK0Q+9JhAkOgrrlTzijje8ALnXvELYn7t1Fdc8voJMvDYwJ73+FCa8JMM4vKbj1TxaS/+8iK7cu0qPDLwhiME7eNhJvDbzYjyNSwU814IyPFjtSrzr53e7VK4bvCxaQbxwXYK8P0FpPAeq5LwlD4G8Aj3oPHTznTy8EAK9JYnMuyR2CrvPasK8Jxh7u0PtwDyyPe08b+jUPId2vrxxmyI9D+VFPLeGODyfiTo7GsVuPD8bczzQ+Jw804ZQPI2PjTviOVg8LCh5vLNQCr05RzS8NhzqvITqcb3CXn28EnKmPDCXZ7zY2QK7mXWXOqfjezySkuE7410xu0XhDjzdmpY75AvwufGMPb1lPY48ys5RvEs4dTyTrn68vOE0OxFp4bwAu4c7WM+WvN3R1rtXfNY7wN35uw/upTxLBwe9wANfvPVg1Lw2+cm85jKeu2baFrxXtvg7BtTAPC9jjDzSyWs88+ftOvqYy7txZ8U87kebPP1C1jjH8hE9gfdNPPnABry0dp+8G0y1OyJP/bxE8uE8dSlSPORrg7y/n9q6gL9qvH+oUjxhDC48RKuHPIQhB7z+I528gl3CvLHqyzxqLke7bNMtu1witbxY+WE8LY2xO1FXNrwdIQ89QK8XvPCyzDs7TXY853VNu0wagzzI5CU8K/EIO8pIvztLJvc78F+1ugvjxjvLoDc85VTzvM1tAbyAnTY933CJvKmGYDypgJw8uSWZPDVpk7oBHp67bIKEukbBTzy6Fgo8ImU8vN+LJDyRHWA8ewBQvLhBmTrZBty7ycsFPfyzvrhLfU48xmA+PKjOrzz9+qq8YaFnu26WYDxO/Fo8Jx+TPAW5ozyYIZe56j5ovHTS/Lxw3mA5YVClu/OKbjy5gt26IjuFPDn+yTwgoRS9HlQZPR83P7wtFGK8qfk3vG62grwkBcO8J2kuvLPZO7z7Kj68ig3tvNFGNzxnGCa8OnZyPNI29jugUxk82OlmPLRCdbxleIE86hbgPNekNTvgihS7QexaPLKC8byvxtQ813yYvI37D707e4c8K9nHuj7ltbvuIya8WvOvOr2uWbxpjzw8FUKuvI1fqDtO2ww9LO4ZPVPe7buTzMg8miVbvMHil7tMO0O8g9y7PCJ6cjvRUCW9RniYvJP6A722cV+8gBwrvDaIhDznnWs8O0YLvd0KBD1gWko9Zv8rPC8CNbt+2kM8/beMPAVQdzybS2O8UzpsO/ceQ7wRNyW8Chq9O9RqHjxWcSc7oSB6PPbA5zwmhMQ8QfY5vD+54DswV4Q6mYMQveZ6EDzv0w4806WfvOzoVD0v0iM8VTUzPJJj0LvEP1i8pYu3vNeJITt28dM8Gc68vIDGF7x3zKw78PUmPPK5oDsXH5q7I1SPPDpvUDyI+p87SGNcvB04NbxDCfw8GZ7hu3kjEzzwE2o8t35tu1WYfLwWbcA8NSwqPCXgDbvj9TQ8QBcHPC554buZFP28pcXDvGcBqbwmEJ47oekMPeJoQr0JErW85cpWvB1hXjzulCu8yTdFvADHTzyj2Wo8rAUQPBht4zumdt68779ZvIG9T7wnwKe8hZqquyv/VL1JXkk8So+Eu65ppLwvHjG8VmAsvBO2ADyvgGs8e5CbvCfvjjx7psu7IOz1PPnVXjwbZQQ9SBO4vCeI7zs4qqI7WyjevKgC1jy5xs47C+lgvIgPP7tKSW87stxUPOJqUrxxtWy7h/KCvBQRsrzOzlo8PpoTPZkASjooJmY7xWqpu3+81jvJI3Y8RNfuPFkTnTkilb08Ny83vGHBzLylwiG8o6hSvMYcA71G0os8e+CFPHxe/7uxYEo8SzqFun77Ebysl487I6AHvdnbQLw5lja9xjEWPISw7rs+Bzy8gG0VOzXSfrzVItA8XGUOvZApmbtwoCU9AdUMvVVSCj3MBSC9pPSuu40XxTwtr408kz62PG2vmbxunSK8dLoRPRSALbxcQAo82lOHPMJ9uDyckgK9lxanOuSWRrwwbZi8H82iOdVWDj2FzMc7aHUSPGv6UrpEGP+6V2z6O29lL7yRc0e8RYwSO1a28bx4Jv+8D5j+O4EUqjwHsyC90gSpu0d3QztdUIA8TM34Oqscr7uZc4K7CtOLvNBZT7uF8MY7pTkKPUv6CbtgEck8xHuDvMofwbkDNIi8WODMu0PohbxvgaO6hvqTu+4KGz1/chw6iTxSPKwoEr0YrI08IHoWPMwbCL2B+Qa8q6FSvDRmiLqp6Y86e/FZvNu+ebxA9AY7buZdvOETlrx33E887FsCveKd1DyS1EY8aToCvPBboTwIQKS8RvV4PBF6wTxzZQk8hGEwPbWYfL25Y665wF5zvWUZo7v+hT46mh0LvAhGBj2priC9je5ovATO8juJi5e8VQLiPH2bcLyVFGA83gYYPRc7rzxfnUW8br5WPLY/z7wCFia91fwqPHFPOztjFm+8VtIOPXCRCLqkqKs8RzCWuzsDszyWQ8c7JjFkPMRLnboxg9i8NeBnOW9tZby2OyM7YWOYPDhiNjxLhhe9xNJKvBBh/buEQgw9DrzaPDIwmblOw7Q7OZnFO92b6jsNCUw8vZcHvYW6XTwr7568FO7Iu+0/y7wUB/68L43JvC2YAj3b7LM87edrPLM1Pj2ut1481CT2vEDOIzy/Xum7Gi/cO6C5pLtj9bM7VwgzPPaHIrylaHY8Kp5QPEpYs7xffou8ZcYyOxxD/Dpx7K+8wTylvHFxIzxU6bK7y5FuPElUK7wEfiU9TCyWvE2knTupfi+9l3EEvezom7n7KJ28+ZtTPJscv7vtxfa8rFqHPArtvTw1YXE8htbhvAZ5nrxGfjC8IlKVu9zNlTzLKNk86nofPEnz9jx38xg99HeCuuzxNrz2WLQ8EyxPPLIWHzxRk868Q85KPUQgr7zGN4w84D0HvA6sBjvIEFW8mG61u4yn7rsVMMY8Yuh5O0TKHz0dpV+82FfNPC8sxjwARs28AngsPdPUJrvGXAE91OVTOx2DW7tYNhy8MHfGvHyD2bsl3R+8jDjjO5GhCTwZggQ7iOj3u1dFsLw6Bkk8fPQjPMEtzbxER6Y84zPUvGVACbwFX4o8m1kdPQrsJT3LAP281icRvMxaqrvTszG75oz/PC9KSbzAeJa8DmwzvIkwZDw2gIW8eMeHPFAdazz5V9e8xLv5vGtsAbyrURm8ym8VPJ6v6ru+/AS9zs/CvKmp0DzpNAS83tt1vAOi6TuJr/87e1eTPGfAuzuUxow7tcYQPDqcCDtAfAK9NWMJPA92YjyDmSm8DWhsvClwszyy7Wy8suE/vWc/3TxCaCa98vjcPPNWv7ycCuY7CuAxvFa8Qj0mzNs7MblhPKn1NDwSi6Q6kpxEPPRMpTqENYc8e8rvu8TKjzsXnR499ByuO6Vw0zwjaYa8FYnwOWKCITyAmNM6v5SgvN3pBD2oaE27a+APvLM0TbzybPA8dWipvACB7jurqIK7Gs8YvYx+TrwuwxU9UsU3PEhtDbzQDAq8QLWqPE+PTz2/zle8Y+SYOqhyejxcFEA8iMvbu4+lqDxMram87dgkPcMfEbwAD8g8+06DPHrnvDsG2YU7+NRRO4rZqDyihDO884yIvMFVlTzYmR88plDnOuDMururt+a8PTqNO4/AKzzyk0o5CbMaPbgEl7zSdk86Tr3pPLmnuDzGRtc7zrfsO3rOFT1uNsy8nvDtu5af/7wNe4E8YNpWPAHITbw1ZgC9VsMAvdUE/jwoE9e8jBsJOYJjTrz56Ou7cQeCvArpjTvyHLM4SOQgu9jWIjwmSBg96k12OwFqJbxGDgU6H2eTPAdXnTxxDrg7BB7Tu9HkHT0a+y89Fc8UvADFILzFbtW7XLjjvKlAeDl/ec68eNSnumPHDz1mg128p4emu/oYgDus9568DdKNvH6RjjmWL7Y8gCgRPe5fQLyq+AU8LD/jOU8AHTxjH0e9PxgAvBQBkTskg0m8uk8APfvCnTwuwJu8sFUCPfE16rtusAY8x5PfPD0npTsarwI9orjbuxDUwrycr6A8vp9zO38x6Dt6GB+9k5cKvFrH+js82ae8q+bSPHxHC7u7Yr05ZaySvDzrlDpr//+7Pq8WvE39hjvWRBO8XAEEPSdggrtnk467GbZquXaAczv4hl+7Oo8SvB9J8LyVQ6s7GQPvPFugebwLNwm91MbbPLN8jzxUdw28jGhQPCh0Dz0FLBm8/+6Ku4VZCz057RQ8vyqkPHz1WjoT/ta8/f+VPE4G3DzA9zO9XCYaOKT1rLuQYSi8zP54PI9pFLtNDTG9iT+EvLykyTuOPY289IFqPFoHSLormj29GMAvO8xwW7x41tw8a40Ivbnt0rkdrWU8IsksuQiUFj3WFQk9bRY7vOBJ2DxD4Se8GDgtOxSmhzv/sZy8Ao9fvG06w7s9Ayi5bBGpPD+RPDzINQW8N3wdPLHqerxSRAo9DMkKvKYDuLxEQau87HnLuubp2DxQam+7e868PMqynLsOx+k6lIWkO2qYnjxfk8A8yefPu7TCWDyOcp08uxtIu94kIzttkwI8M36cPNbe27y/+n47SSy1O+KUGLudhK68g7QIPYWmlLuoDbw87Dc3PJU2Cb2MjxO9ueqPu7MZQbwXzu68zkmDPG9G5jxOiZs8wyNrPTeSlrtTEFK8a3gxvKtfxzxRGcS8qGrLvAlqdbwQ7Zi6AdLTvEJ7bjzoVRc94vxEOrabgTxRyv+88mQgPDZedDr4Kgu84ZbxuxkEVLuLHsO8mmvZvOhMELw0YCC9BQsTO8wa07wyyc08uG/ZunHNArzAfoe8LmhjvMpwkjyLPaW8BTnJvIYIN7ytefO7mQnFvJWeUrxToCG8l8aPPBu64Dx/Y50896uKPEkbDL3CJws88X9WPJugdjyf6p28ikmtvMH5o7yGzHc8VO9nvK2aQj12T6M8qI/tuuC66ztRc8e8D89IvL2cZTw54R69RuiTvPktTzoHCCe8lzhpvLNv/DnIzmw8KrloPLPKW7xauym7I3WWvIY67TzrjaY7aVecvFEpAb1d0oe7qr8tvISNITyEvjY8j3ugvBRUlzzneYE8K6FgvIJIfbvY8fk7zei3vDuB7DyGXeG8/gqiuKvznbzWBZW86Sc9PVZLkDzpKSG73vpuvFhaTry5NbC8UQwvvXWM4zoswA28OdDku/sq2rxY/z8728YQPDBgLT2DtpE8pNC3PN4wFjuvNgI9sgQ6PdQ8Mjw+lCw8h/w9PHT12Lxg9Yq6mWvYPJw1tzmfDQa9SWljPPC3dryj7rs8fS5lPNj/G7y07tw7LttuPBlMzTy80jS8oEwDPLU8D7y22Qe9xCclvRlyEj1zX8U7SFytPD8HG7uCd4k7S4C4vGHUBD2Vnae7YSMQO7iANDx/kBI8/pREvKTChDy9P8Q7QEDLO8i9D70gLlW4EVCUuqi/wjvGchc9ezkkO6Yf1Twe+/Y3esQauojssTwwSCu8dIpXvByWFzvoHq28GmnavI1ocDsroiO8Up2IPOXZzDuCLgg9YMqBPM8mWLtJ76G8jj9IPeOEvTswGOg7Xc/2u92e1rwMbzY8YwOtuy6rKrxZz/Y7zg8fPItSSzupCRi8b9bLO8/Gmby8eMo6fJmhvBgribzkWgG81tIjPa6/kTzcbhU8rxsSvaLp6LytW6W8LtXXPLj7U7xcBV29Gi/XvM78Hrz/qW48QNWIvA+ZhrwMChy7AF6su/vm4rs8bfs6T2mCPAthHj1i3ia9ZV/huyonhbu21ow854Equ11sYzsWTFO8yKqQPK23NL3yJek8zkBxPF4Gk7s6LLg7xolDvazDkrsVE/k8/BChOvT9Tz2glho922o5vPN9lLyJfoK87FR9vDa2hLwFyya7PE1sO5AfHD3d90I8PZp1PN5CpbzBGuS8DB+WvAR6xjuO+nY8EfqOtzTO1ryPv1a8yrlUuV/Nnzw08Si9pSapvNwBO72LzjG9kjOrunP3gjsB7UK8vf64vOqOrjxPnK48KQ/Lu47rUbwOfEq8uC8xvYssEry5/eM7a0G3O+brxrzojRe8TgC8vBfOf7z0xqy8261aPUSJkzx2Iy89PrElO1NurDy6tVy8KeUyuyUO1bv87sA8J4K7PCNsZ7u7aSQ9T2OIvJq2urtCug+8bZCCvPmGkzyhW/S7aBslPM4vpbxBmEM73k0TPeB+Gjyi4RE7ZPNBvO+sQzyJYvO8yYD+O7N+lTzUMwe7Ee9LvEXj6jkSSas8mrl1O5ahjTxsyqQ7isWKO/+Ka7uTvK689KpXPNUdxryabl68PmZ0vEGOyrz5oAe9/XQevV6Hf7stDh29F2yavI+SJr0oZd28g+uevLyMizwTDKE827QdPZtUhzxuPxG8qhxgvW1rvDouxxE8DF0cvNKOAbvKDK0825bhO5OCo7xSUAu7wTK8PC9Bn7wB+7m8l+ivPDR0gryFr3M6lrCzvJ/jH73a3Z07EB2Vu1bSPT1Bp2q9C7XXPDTTMjztKDk9gQk6O1AYTrpgUtI8ye29ur0FNbw1AmC7xdkuvMy8ETwIl3y8aUOkuaUvejtqQJO7Nw0DvPw66LtVuoq8uyntPFC+zzu474m79p7FPD4PC7wj1p28XW8bPTcVLDyembU7CwBePMkUCjwI0tI8elvdPHkcarzv0ry7VNi2PNYMGLyg1k08XwNxugcdD70wB6M8w59UuwPWDLzilyq8sNOruxZJYjt3Xtm8kMPTO/89yzsJc1A8d1UHvO8hQLzpKYu8SVKovBCiLjwNNvG88gqwvM3F/zudrBM8oymLvAl+vTyM6VE4tvW4PKer1rxtTZy8VtU7u8Xfcry7skK8NScNOQc64bvMKlq8Hc++uw9jCT1U/hg8wMmCPHrxjzxETgw8u4govWLbAT36HHw8YhYRuvkiEL3zQ/+7jGT1u551V7yWJ3g8nw4kvCMPPTyhvM08ZA3duxFzJzxJZQ69RLvru6LcEbzsMga80fcfO49khrx0naE8YFRFvCRn0rt80xo97vgBvZgtiLtKXAE89dUfOgW547sYcom7dHYRvGz0IzxcLWW71yBiPEEzvTwziO48KsmfvHK1gTvUIKm8UMwVPalsH7wx3FE9j1u3vMxQjLzO/vw7uyxHPNnjLj2D9Fa80l7YuXtWBb2FWNO89m5AvH1h6zxmsD88Yg3MvOm/mDyhlBo7GAtZvLexs7wSfEk8Y8QvPJRrxbsIQ5W8o13ivMSbBz1+NhA9LBSMPNnWijz5yjU83NCCPEqL9TzZHMC7A5CUPBezsTvuZdu8MCSSOxYIrbuiQPi75RToO/Ptv7sVy3M8ZnzgPOCFl7urSy08HRdjPGMEurxFgTI8svHvPManuru1xZg74BRDvAYf97mqWbU7/ykGuglXTbxJywy75I4tPBTl5jznL9E8t/+pOzzwTbtarUK8b9FcPD7f0bxlWLa82sLAPO7SpjzzeWk7TsKwO3UrB7x8uqS5igkaPb2vMDxTy2G9Idiwu4v0VbzQVRA9hnCtvLbLNrxK28o8tZ71vKHniryvy5Y7E/k/PLCvlzwzqiK7ayUnvcmznDxIwbC7HeIZPMVoszs9wOK74m9QvOX6zLtLMEq8g6qAPNx5t7zrcpc7nSYVvXXd9bt/7Ps70dqTOnyK5DrlQDq8ATIOOy02EDwg4lS8e54oPJ5crLuboxG7zOl2PFK+E72EtAS7+ScnvMrM+blpu+m83WeJO27PgDvtLJ68XmxbujrjOrxCvTu9KePcPJnSlzxIJk88KAipvHddojwWT9y8wGOFPKLPyzzj/no7ztURO91+07xEGnG88ioqvNwq5rvl1So7OzYwPCXzvLt4bwu9FNQKPd57T71V1L68N3MYPbv8MrytkiA9iSdzPE1/Az1OUkQ8F90uu2/PLDp7hza8qIoFPAZ8z7tOqEO9ePNxvJvvhzz/kKc8AYbvPNeSV7zy3HW6gjHZPP6SgjscG+A753eLO2q2RTzTWxG83B5SO00oSjx46Us7uWnWvBLqzLy084C8Vjgju5ZmiTuXbTc89RiGO1WuvbuAMju5IuC6u8ZdvDuwptu8MYN5vCmWKTzsXsg6e2DgvGdu7rxUhS881k59vBRFDz2jevM8XcVcvBiE2bzj1VO8F9aGPAocErx+K8O8MJFMPPMAVzyvOlo8XOADuwthLzw3lzk5Gg2mu4kSYTvLH1e7PCcEPeQ0irwsUXc8mbTTu0yEEDxXmaS8voF8O5l/Mb2hssY8Evt6vBY4IzyIglS7e13nubsaWDqQS868x+SIO7Bf/rx140k8E3axvO8yUzxCc748Dp81PFLQ1zpVztw88VaOOxJ6C72/h408DgTVOzeCB70eXD28lz6FPJDsET17wZA87LvHOnUdY7xon9Y7TCiBu+hGBj2HbjC8/NLYvO/P5LnskhA81Xj0PErMhjpWqUO8EBTBvMggULzsfNw7Y4Xpux3C17xSHqC8UEHOu9kI67ycYvs8dB96vB9cRjy3c4S8olhXvBUumjxdcpC7drqQPO6SRT3gsB07DnmtvJesTLz24ai8nHfavGaKYbyF4Ow7q+RfvAt94LvcbDU8UdHiPLFWbjtqOKW8ZMUovZQOMzwQiDg8zsBLvK6pPrtOtwe9GHfevA1TNTxiq5K6KeMdPPBdCDyusKW7XH2gvFzCpbtg8yI9q8iXvH7M07vVXv47gAzBvAA2jbsxD1Q8t5uLO2+CYjx9rRM89xa+upY8JLwwHIK8GQzOunhhEjwfF2y7AJuoO3sjADuRO9a8htf9OPdlsLsmw1C8b7EqvKGfDLy8pMM7r3CaPHflMDuFgcy5qpVSO6+RI7wiZ3E8TVVgPA== 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 index: 5 object: embedding - - embedding: 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 - 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 + prompt_tokens: 842 + total_tokens: 842 status: code: 200 message: OK @@ -136,7 +91,7 @@ interactions: connection: - keep-alive content-length: - - '8789' + - '9754' content-type: - application/json host: @@ -173,9 +128,15 @@ interactions: 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`. + Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer. - 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. + Chunk IDs come from two places: + - The `chunk_id` field on `search` / `await search(...)` results + - The `chunk_ids` field on `items.jsonl` rows (when you ground via direct file reads) + + Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs. + + Every answer that uses search or file-read evidence must be backed by `cite`. ## Document Filesystem (inside execute_code) @@ -189,7 +150,7 @@ interactions: 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`. + `{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 and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora. ### Reading files Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported). @@ -220,27 +181,29 @@ interactions: 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" + Structured document items. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file. + + Each row carries: + - `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results + - `label`: item type — one of `"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. + - `chunk_ids`: chunks that contain this item — pass to `cite()` to ground an answer that read this item directly + - `heading_level`: H-level for `section_header` rows; `0` on non-header rows ### 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. + Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `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. + Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in `items.jsonl`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index. ## 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. + 4. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents. + 5. 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. @@ -321,14 +284,15 @@ interactions: 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. + Accepts chunk_ids from search results AND from direct file reads + (items.jsonl, toc.json). Verbatim copies only — chunk_ids that + don't exist in the database trigger a retry. name: cite parameters: additionalProperties: false properties: chunk_ids: - description: List of chunk_id values from search results. + description: List of chunk_id values from search results or VFS reads. items: type: string type: array @@ -340,641 +304,3759 @@ interactions: response: body: string: |+ - data: {"id":"chatcmpl-260","object":"chat.completion.chunk","created":1779176073,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"We"},"finish_reason":null}]} + data: {"id":"chatcmpl-121","object":"chat.completion.chunk","created":1779702452,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"We"},"finish_reason":null}]} - data: 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{"id":"chatcmpl-121","object":"chat.completion.chunk","created":1779702502,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","tool_calls":[{"id":"call_qd0d5ug0","index":0,"type":"function","function":{"name":"execute_code","arguments":"{\"code\":\"from pathlib import Path\\nimport json\\nlabel_to_chunk = {}\\nfor doc_dir in Path('/documents').iterdir():\\n items_file = doc_dir / 'items.jsonl'\\n if not items_file.exists():\\n continue\\n for line in items_file.read_text().splitlines():\\n if not line.strip():\\n continue\\n try:\\n item = json.loads(line)\\n except Exception as e:\\n continue\\n label = item.get('label')\\n chunk_ids = item.get('chunk_ids')\\n if label and chunk_ids:\\n # take first chunk id\\n chunk_id = chunk_ids[0]\\n if label not in label_to_chunk:\\n label_to_chunk[label] = chunk_id\\n# Print mapping\\nprint(json.dumps(label_to_chunk, indent=2))\\n\"}"}}]},"finish_reason":"tool_calls"}]} + + data: {"id":"chatcmpl-121","object":"chat.completion.chunk","created":1779702502,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[],"usage":{"prompt_tokens":2124,"completion_tokens":2081,"total_tokens":4205}} data: [DONE] @@ -995,47 +4077,7 @@ interactions: connection: - keep-alive content-length: - - '92' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - encoding_format: base64 - input: - - document element types - model: qwen3-embedding:4b - uri: http://localhost:11434/v1/embeddings - response: - headers: - content-type: - - application/json - transfer-encoding: - - chunked - parsed_body: - data: - - embedding: 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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' + - '19401' content-type: - application/json host: @@ -1072,9 +4114,15 @@ interactions: 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`. + Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer. - 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. + Chunk IDs come from two places: + - The `chunk_id` field on `search` / `await search(...)` results + - The `chunk_ids` field on `items.jsonl` rows (when you ground via direct file reads) + + Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs. + + Every answer that uses search or file-read evidence must be backed by `cite`. ## Document Filesystem (inside execute_code) @@ -1088,7 +4136,7 @@ interactions: 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`. + `{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 and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora. ### Reading files Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported). @@ -1119,27 +4167,29 @@ interactions: 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" + Structured document items. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file. + + Each row carries: + - `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results + - `label`: item type — one of `"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. + - `chunk_ids`: chunks that contain this item — pass to `cite()` to ground an answer that read this item directly + - `heading_level`: H-level for `section_header` rows; `0` on non-header rows ### 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. + Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `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. + Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in `items.jsonl`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index. ## 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. + 4. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents. + 5. 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. @@ -1165,60 +4215,114 @@ interactions: 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 list all different document element types mentioned. Likely the document element types are the labels defined in items.jsonl: "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote". But there might be other types in other documents. The question is to search for content about document element types or labels. So we need to search the knowledge base for references to "document element types" or "labels" or "element types" or "document element types" etc. Then gather all distinct types mentioned. - 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 produce a list of all different document element types mentioned. We need to cite the chunk IDs that support each type. We need to gather all types across the corpus. The items.jsonl labels are a set of known types. But there might be other types like "section_header" etc. The question might be expecting the list of types from the items.jsonl. But the question says "Search for content about document element types or labels." That might refer to the items.jsonl labels. So we need to search for "label" or "document element types" across the knowledge base. But we need to list all different document element types mentioned. So we need to find all distinct labels across all documents. That might be the same set as the items.jsonl label values. But there might be other labels like "annotation" etc. We need to check. - 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 should search for "label" or "document element types" or "element type" or "document element type" or "document element types" or "document element label" etc. But we need to find all types. We can search for "label:" or "label" or "type:" or "element type" etc. But we need to be careful to capture all distinct types. - We can use the search tool. Let's search for "document element types" first. + We can use search with query "label" maybe. But that might return many results. We can also search for "label:" or "label" but that might return many. We can search for "label" and "document" maybe. But we can also search for "label" and "items.jsonl" but we don't have that. + + Alternatively, we can read all items.jsonl files across all documents and extract the label field. That would give us all types. That might be more reliable. But the question says "Search for content about document element types or labels." So we might need to search the knowledge base for references to "document element types" or "labels" and then list them. But we can also read items.jsonl to get the labels. But the question might be expecting the list of types from items.jsonl. But we need to provide citations. We can cite the chunk IDs that contain the labels. But if we read items.jsonl, we can get the labels but we need to cite the chunk IDs that contain those labels. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide citations for each type. We can gather chunk IDs for each type. But we might need to gather many chunk IDs. But we can just cite a few chunk IDs that mention each type. But we need to ensure we cite at least one chunk ID that contains the type. We can gather all types and then cite a representative chunk ID for each type. But we need to ensure we cite the chunk IDs that mention the type. We can use search to find chunk IDs that contain the type names. But we might need to gather all types. + + We can use execute_code to list all items.jsonl across all documents and extract the label values. Then we can produce a list of unique labels. Then we can search for each label to get chunk IDs that mention them. But we can also use the items.jsonl chunk IDs themselves. But we need to cite chunk IDs that ground the answer. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide a list of chunk IDs. But we might need to provide chunk IDs for each type. But we can just provide a single chunk ID that contains each type. But we need to ensure that the chunk ID indeed contains the type. We can search for each type to get a chunk ID. But we can also use the items.jsonl chunk IDs. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we can use them. + + We need to produce a final answer: a list of all different document element types mentioned. We need to cite chunk IDs that support each type. We need to call cite before writing the final answer. We need to gather chunk IDs for each type. + + We need to decide how to gather the types. We can use execute_code to read all items.jsonl across all documents and extract unique labels. Then we can produce a list of labels. Then we can search for each label to get chunk IDs. But we might also use the items.jsonl chunk IDs directly. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we need to confirm. + + We can do the following: + + 1. Use execute_code to list all documents and read items.jsonl for each document. For each item, collect the label. Build a set of unique labels. + + 2. Then for each label, we can search for that label to get chunk IDs. But we might also use the items.jsonl chunk IDs. But we might need to use search to get chunk IDs that mention the label. But we can also use the items.jsonl chunk IDs. But we need to ensure we have chunk IDs that mention the label. We can use the items.jsonl chunk IDs. But we need to confirm that the items.jsonl chunk IDs are indeed chunk IDs that we can cite. The items.jsonl chunk IDs are the chunk IDs for each item. But we need to confirm that the items.jsonl chunk IDs are the same as the chunk IDs we can cite. The items.jsonl chunk IDs are part of the items.jsonl rows. They are used for cross-referencing. They are chunk IDs that we can cite. So we can use them. + + Thus we can gather a mapping from label to a sample chunk ID that contains that label. Then we can call cite with the list of chunk IDs for all labels. Then we can produce the answer. + + We need to be careful: The items.jsonl chunk IDs are unique across the entire corpus. They are used for cross-referencing. So we can gather them. + + We need to produce the answer: "section_header, text, table, list_item, caption, formula, picture, code, footnote" maybe. But there might be other labels like "annotation" or "definition" etc. But we need to check. + + We need to search for all labels. Let's do that. + + We can use execute_code to list all documents and read items.jsonl. Let's do that. + + We'll write code to: + + - import Path, json + - iterate over /documents + - for each doc, read items.jsonl + - parse each line as JSON + - collect label + - also collect chunk_id for each label + - produce a mapping from label to list of chunk_ids + - then produce a set of labels + - then produce a mapping from label to a sample chunk_id + + We need to output the mapping. We'll use print(). + + But we need to be careful: The items.jsonl might be large. But we can handle it. + + We need to ensure we use Path.read_text() and splitlines. + + We'll do: + + ``` + from pathlib import Path + import json + + label_to_chunk = {} + for doc_dir in Path('/documents').iterdir(): + items_file = doc_dir / 'items.jsonl' + if not items_file.exists(): + continue + for line in items_file.read_text().splitlines(): + if not line.strip(): + continue + item = json.loads(line) + label = item.get('label') + chunk_id = item.get('chunk_ids', [None])[0] if item.get('chunk_ids') else None + if label and chunk_id: + if label not in label_to_chunk: + label_to_chunk[label] = chunk_id + ``` + + But we need to handle chunk_ids: It might be a list of chunk IDs. We can take the first chunk ID. + + But we need to confirm that each item has chunk_ids. It might have multiple chunk_ids. But we can pick the first. + + Then we can produce a list of labels and chunk IDs. + + We need to output them in a format that we can parse. + + We can print a JSON mapping or just print each label and chunk id. + + We need to capture the output. + + We need to call execute_code with this code. + + Let's do that. role: assistant tool_calls: - function: - arguments: '{"query":"document element types","limit":10}' - name: search - id: call_539fpdo9 + arguments: '{"code":"from pathlib import Path\nimport json\nlabel_to_chunk = {}\nfor doc_dir in Path(''/documents'').iterdir():\n items_file + = doc_dir / ''items.jsonl''\n if not items_file.exists():\n continue\n for line in items_file.read_text().splitlines():\n if + not line.strip():\n continue\n try:\n item = json.loads(line)\n except Exception + as e:\n continue\n label = item.get(''label'')\n chunk_ids = item.get(''chunk_ids'')\n if + label and chunk_ids:\n # take first chunk id\n chunk_id = chunk_ids[0]\n if + label not in label_to_chunk:\n label_to_chunk[label] = chunk_id\n# Print mapping\nprint(json.dumps(label_to_chunk, + indent=2))\n"}' + name: execute_code + id: call_qd0d5ug0 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/ + - content: | + { + "text": "713678f9-980e-47ed-9e1c-e6b533231d57", + "caption": "713678f9-980e-47ed-9e1c-e6b533231d57", + "footnote": "73a2443c-3c25-4fbb-a6d4-0dec371ef104" + } role: tool - tool_call_id: call_539fpdo9 + tool_call_id: call_qd0d5ug0 model: gpt-oss reasoning_effort: high stream: true @@ -1278,14 +4382,15 @@ interactions: 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. + Accepts chunk_ids from search results AND from direct file reads + (items.jsonl, toc.json). Verbatim copies only — chunk_ids that + don't exist in the database trigger a retry. name: cite parameters: additionalProperties: false properties: chunk_ids: - description: List of chunk_id values from search results. + description: List of chunk_id values from search results or VFS reads. items: type: string type: array @@ -1297,433 +4402,143 @@ interactions: response: body: string: |+ - data: {"id":"chatcmpl-550","object":"chat.completion.chunk","created":1779176151,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"The"},"finish_reason":null}]} + data: {"id":"chatcmpl-291","object":"chat.completion.chunk","created":1779702503,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"The"},"finish_reason":null}]} - data: {"id":"chatcmpl-550","object":"chat.completion.chunk","created":1779176151,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" search"},"finish_reason":null}]} + data: {"id":"chatcmpl-291","object":"chat.completion.chunk","created":1779702503,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" output"},"finish_reason":null}]} - 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- data: {"id":"chatcmpl-550","object":"chat.completion.chunk","created":1779176156,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[],"usage":{"prompt_tokens":4158,"completion_tokens":240,"total_tokens":4398}} + data: {"id":"chatcmpl-291","object":"chat.completion.chunk","created":1779702506,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[],"usage":{"prompt_tokens":4304,"completion_tokens":119,"total_tokens":4423}} data: [DONE] @@ -1744,7 +4559,4122 @@ interactions: connection: - keep-alive content-length: - - '81' + - '20121' + 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. + + Chunk IDs come from two places: + - The `chunk_id` field on `search` / `await search(...)` results + - The `chunk_ids` field on `items.jsonl` rows (when you ground via direct file reads) + + Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs. + + Every answer that uses search or file-read evidence must be backed by `cite`. + + ## 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 and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora. + + ### 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. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file. + + Each row carries: + - `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results + - `label`: item type — one of `"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 + - `chunk_ids`: chunks that contain this item — pass to `cite()` to ground an answer that read this item directly + - `heading_level`: H-level for `section_header` rows; `0` on non-header rows + + ### toc.json + Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `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`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index. + + ## 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. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents. + 5. 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 list all different document element types mentioned. Likely the document element types are the labels defined in items.jsonl: "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote". But there might be other types in other documents. The question is to search for content about document element types or labels. So we need to search the knowledge base for references to "document element types" or "labels" or "element types" or "document element types" etc. Then gather all distinct types mentioned. + + We need to produce a list of all different document element types mentioned. We need to cite the chunk IDs that support each type. We need to gather all types across the corpus. The items.jsonl labels are a set of known types. But there might be other types like "section_header" etc. The question might be expecting the list of types from the items.jsonl. But the question says "Search for content about document element types or labels." That might refer to the items.jsonl labels. So we need to search for "label" or "document element types" across the knowledge base. But we need to list all different document element types mentioned. So we need to find all distinct labels across all documents. That might be the same set as the items.jsonl label values. But there might be other labels like "annotation" etc. We need to check. + + We should search for "label" or "document element types" or "element type" or "document element type" or "document element types" or "document element label" etc. But we need to find all types. We can search for "label:" or "label" or "type:" or "element type" etc. But we need to be careful to capture all distinct types. + + We can use search with query "label" maybe. But that might return many results. We can also search for "label:" or "label" but that might return many. We can search for "label" and "document" maybe. But we can also search for "label" and "items.jsonl" but we don't have that. + + Alternatively, we can read all items.jsonl files across all documents and extract the label field. That would give us all types. That might be more reliable. But the question says "Search for content about document element types or labels." So we might need to search the knowledge base for references to "document element types" or "labels" and then list them. But we can also read items.jsonl to get the labels. But the question might be expecting the list of types from items.jsonl. But we need to provide citations. We can cite the chunk IDs that contain the labels. But if we read items.jsonl, we can get the labels but we need to cite the chunk IDs that contain those labels. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide citations for each type. We can gather chunk IDs for each type. But we might need to gather many chunk IDs. But we can just cite a few chunk IDs that mention each type. But we need to ensure we cite at least one chunk ID that contains the type. We can gather all types and then cite a representative chunk ID for each type. But we need to ensure we cite the chunk IDs that mention the type. We can use search to find chunk IDs that contain the type names. But we might need to gather all types. + + We can use execute_code to list all items.jsonl across all documents and extract the label values. Then we can produce a list of unique labels. Then we can search for each label to get chunk IDs that mention them. But we can also use the items.jsonl chunk IDs themselves. But we need to cite chunk IDs that ground the answer. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide a list of chunk IDs. But we might need to provide chunk IDs for each type. But we can just provide a single chunk ID that contains each type. But we need to ensure that the chunk ID indeed contains the type. We can search for each type to get a chunk ID. But we can also use the items.jsonl chunk IDs. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we can use them. + + We need to produce a final answer: a list of all different document element types mentioned. We need to cite chunk IDs that support each type. We need to call cite before writing the final answer. We need to gather chunk IDs for each type. + + We need to decide how to gather the types. We can use execute_code to read all items.jsonl across all documents and extract unique labels. Then we can produce a list of labels. Then we can search for each label to get chunk IDs. But we might also use the items.jsonl chunk IDs directly. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we need to confirm. + + We can do the following: + + 1. Use execute_code to list all documents and read items.jsonl for each document. For each item, collect the label. Build a set of unique labels. + + 2. Then for each label, we can search for that label to get chunk IDs. But we might also use the items.jsonl chunk IDs. But we might need to use search to get chunk IDs that mention the label. But we can also use the items.jsonl chunk IDs. But we need to ensure we have chunk IDs that mention the label. We can use the items.jsonl chunk IDs. But we need to confirm that the items.jsonl chunk IDs are indeed chunk IDs that we can cite. The items.jsonl chunk IDs are the chunk IDs for each item. But we need to confirm that the items.jsonl chunk IDs are the same as the chunk IDs we can cite. The items.jsonl chunk IDs are part of the items.jsonl rows. They are used for cross-referencing. They are chunk IDs that we can cite. So we can use them. + + Thus we can gather a mapping from label to a sample chunk ID that contains that label. Then we can call cite with the list of chunk IDs for all labels. Then we can produce the answer. + + We need to be careful: The items.jsonl chunk IDs are unique across the entire corpus. They are used for cross-referencing. So we can gather them. + + We need to produce the answer: "section_header, text, table, list_item, caption, formula, picture, code, footnote" maybe. But there might be other labels like "annotation" or "definition" etc. But we need to check. + + We need to search for all labels. Let's do that. + + We can use execute_code to list all documents and read items.jsonl. Let's do that. + + We'll write code to: + + - import Path, json + - iterate over /documents + - for each doc, read items.jsonl + - parse each line as JSON + - collect label + - also collect chunk_id for each label + - produce a mapping from label to list of chunk_ids + - then produce a set of labels + - then produce a mapping from label to a sample chunk_id + + We need to output the mapping. We'll use print(). + + But we need to be careful: The items.jsonl might be large. But we can handle it. + + We need to ensure we use Path.read_text() and splitlines. + + We'll do: + + ``` + from pathlib import Path + import json + + label_to_chunk = {} + for doc_dir in Path('/documents').iterdir(): + items_file = doc_dir / 'items.jsonl' + if not items_file.exists(): + continue + for line in items_file.read_text().splitlines(): + if not line.strip(): + continue + item = json.loads(line) + label = item.get('label') + chunk_id = item.get('chunk_ids', [None])[0] if item.get('chunk_ids') else None + if label and chunk_id: + if label not in label_to_chunk: + label_to_chunk[label] = chunk_id + ``` + + But we need to handle chunk_ids: It might be a list of chunk IDs. We can take the first chunk ID. + + But we need to confirm that each item has chunk_ids. It might have multiple chunk_ids. But we can pick the first. + + Then we can produce a list of labels and chunk IDs. + + We need to output them in a format that we can parse. + + We can print a JSON mapping or just print each label and chunk id. + + We need to capture the output. + + We need to call execute_code with this code. + + Let's do that. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nlabel_to_chunk = {}\nfor doc_dir in Path(''/documents'').iterdir():\n items_file + = doc_dir / ''items.jsonl''\n if not items_file.exists():\n continue\n for line in items_file.read_text().splitlines():\n if + not line.strip():\n continue\n try:\n item = json.loads(line)\n except Exception + as e:\n continue\n label = item.get(''label'')\n chunk_ids = item.get(''chunk_ids'')\n if + label and chunk_ids:\n # take first chunk id\n chunk_id = chunk_ids[0]\n if + label not in label_to_chunk:\n label_to_chunk[label] = chunk_id\n# Print mapping\nprint(json.dumps(label_to_chunk, + indent=2))\n"}' + name: execute_code + id: call_qd0d5ug0 + type: function + - content: | + { + "text": "713678f9-980e-47ed-9e1c-e6b533231d57", + "caption": "713678f9-980e-47ed-9e1c-e6b533231d57", + "footnote": "73a2443c-3c25-4fbb-a6d4-0dec371ef104" + } + role: tool + tool_call_id: call_qd0d5ug0 + - content: null + reasoning: |- + The output shows only three labels: "text", "caption", "footnote". That seems incomplete. Maybe the items.jsonl files only contain these labels? That seems unlikely. Maybe the items.jsonl files are not present or empty. Let's check the structure. + + We need to inspect the /documents directory. Let's list the directories. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\n# List directories\nprint([d.name for d in Path(''/documents'').iterdir()])\n"}' + name: execute_code + id: call_b60xwvbx + type: function + - content: | + ['40fa427f-7d9c-499f-9985-ce7f51d24db4'] + role: tool + tool_call_id: call_b60xwvbx + 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. + + Accepts chunk_ids from search results AND from direct file reads + (items.jsonl, toc.json). Verbatim copies only — chunk_ids that + don't exist in the database trigger a retry. + name: cite + parameters: + additionalProperties: false + properties: + chunk_ids: + description: List of chunk_id values from search results or VFS reads. + 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-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"Only"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" one"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" document"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" directory"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":":"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" '"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"40"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"fa"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"427"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"f"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"7"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"d"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"9"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"c"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"499"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"f"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"998"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"5"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-ce"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"7"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"f"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"51"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"d"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"24"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"db"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"4"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"'."},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Let's"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" inspect"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" its"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" contents"},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702507,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702510,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","tool_calls":[{"id":"call_rt6l2me5","index":0,"type":"function","function":{"name":"execute_code","arguments":"{\"code\":\"from pathlib import Path\\nimport json\\npath = Path('/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4')\\nprint('Files:', [f.name for f in path.iterdir()])\\nprint('metadata:', json.loads((path/'metadata.json').read_text()))\\n\"}"}}]},"finish_reason":"tool_calls"}]} + + data: {"id":"chatcmpl-416","object":"chat.completion.chunk","created":1779702510,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[],"usage":{"prompt_tokens":4459,"completion_tokens":132,"total_tokens":4591}} + + data: [DONE] + + headers: + content-type: + - text/event-stream + transfer-encoding: + - chunked + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '20984' + 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. + + Chunk IDs come from two places: + - The `chunk_id` field on `search` / `await search(...)` results + - The `chunk_ids` field on `items.jsonl` rows (when you ground via direct file reads) + + Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs. + + Every answer that uses search or file-read evidence must be backed by `cite`. + + ## 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 and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora. + + ### 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. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file. + + Each row carries: + - `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results + - `label`: item type — one of `"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 + - `chunk_ids`: chunks that contain this item — pass to `cite()` to ground an answer that read this item directly + - `heading_level`: H-level for `section_header` rows; `0` on non-header rows + + ### toc.json + Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `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`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index. + + ## 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. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents. + 5. 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 list all different document element types mentioned. Likely the document element types are the labels defined in items.jsonl: "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote". But there might be other types in other documents. The question is to search for content about document element types or labels. So we need to search the knowledge base for references to "document element types" or "labels" or "element types" or "document element types" etc. Then gather all distinct types mentioned. + + We need to produce a list of all different document element types mentioned. We need to cite the chunk IDs that support each type. We need to gather all types across the corpus. The items.jsonl labels are a set of known types. But there might be other types like "section_header" etc. The question might be expecting the list of types from the items.jsonl. But the question says "Search for content about document element types or labels." That might refer to the items.jsonl labels. So we need to search for "label" or "document element types" across the knowledge base. But we need to list all different document element types mentioned. So we need to find all distinct labels across all documents. That might be the same set as the items.jsonl label values. But there might be other labels like "annotation" etc. We need to check. + + We should search for "label" or "document element types" or "element type" or "document element type" or "document element types" or "document element label" etc. But we need to find all types. We can search for "label:" or "label" or "type:" or "element type" etc. But we need to be careful to capture all distinct types. + + We can use search with query "label" maybe. But that might return many results. We can also search for "label:" or "label" but that might return many. We can search for "label" and "document" maybe. But we can also search for "label" and "items.jsonl" but we don't have that. + + Alternatively, we can read all items.jsonl files across all documents and extract the label field. That would give us all types. That might be more reliable. But the question says "Search for content about document element types or labels." So we might need to search the knowledge base for references to "document element types" or "labels" and then list them. But we can also read items.jsonl to get the labels. But the question might be expecting the list of types from items.jsonl. But we need to provide citations. We can cite the chunk IDs that contain the labels. But if we read items.jsonl, we can get the labels but we need to cite the chunk IDs that contain those labels. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide citations for each type. We can gather chunk IDs for each type. But we might need to gather many chunk IDs. But we can just cite a few chunk IDs that mention each type. But we need to ensure we cite at least one chunk ID that contains the type. We can gather all types and then cite a representative chunk ID for each type. But we need to ensure we cite the chunk IDs that mention the type. We can use search to find chunk IDs that contain the type names. But we might need to gather all types. + + We can use execute_code to list all items.jsonl across all documents and extract the label values. Then we can produce a list of unique labels. Then we can search for each label to get chunk IDs that mention them. But we can also use the items.jsonl chunk IDs themselves. But we need to cite chunk IDs that ground the answer. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide a list of chunk IDs. But we might need to provide chunk IDs for each type. But we can just provide a single chunk ID that contains each type. But we need to ensure that the chunk ID indeed contains the type. We can search for each type to get a chunk ID. But we can also use the items.jsonl chunk IDs. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we can use them. + + We need to produce a final answer: a list of all different document element types mentioned. We need to cite chunk IDs that support each type. We need to call cite before writing the final answer. We need to gather chunk IDs for each type. + + We need to decide how to gather the types. We can use execute_code to read all items.jsonl across all documents and extract unique labels. Then we can produce a list of labels. Then we can search for each label to get chunk IDs. But we might also use the items.jsonl chunk IDs directly. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we need to confirm. + + We can do the following: + + 1. Use execute_code to list all documents and read items.jsonl for each document. For each item, collect the label. Build a set of unique labels. + + 2. Then for each label, we can search for that label to get chunk IDs. But we might also use the items.jsonl chunk IDs. But we might need to use search to get chunk IDs that mention the label. But we can also use the items.jsonl chunk IDs. But we need to ensure we have chunk IDs that mention the label. We can use the items.jsonl chunk IDs. But we need to confirm that the items.jsonl chunk IDs are indeed chunk IDs that we can cite. The items.jsonl chunk IDs are the chunk IDs for each item. But we need to confirm that the items.jsonl chunk IDs are the same as the chunk IDs we can cite. The items.jsonl chunk IDs are part of the items.jsonl rows. They are used for cross-referencing. They are chunk IDs that we can cite. So we can use them. + + Thus we can gather a mapping from label to a sample chunk ID that contains that label. Then we can call cite with the list of chunk IDs for all labels. Then we can produce the answer. + + We need to be careful: The items.jsonl chunk IDs are unique across the entire corpus. They are used for cross-referencing. So we can gather them. + + We need to produce the answer: "section_header, text, table, list_item, caption, formula, picture, code, footnote" maybe. But there might be other labels like "annotation" or "definition" etc. But we need to check. + + We need to search for all labels. Let's do that. + + We can use execute_code to list all documents and read items.jsonl. Let's do that. + + We'll write code to: + + - import Path, json + - iterate over /documents + - for each doc, read items.jsonl + - parse each line as JSON + - collect label + - also collect chunk_id for each label + - produce a mapping from label to list of chunk_ids + - then produce a set of labels + - then produce a mapping from label to a sample chunk_id + + We need to output the mapping. We'll use print(). + + But we need to be careful: The items.jsonl might be large. But we can handle it. + + We need to ensure we use Path.read_text() and splitlines. + + We'll do: + + ``` + from pathlib import Path + import json + + label_to_chunk = {} + for doc_dir in Path('/documents').iterdir(): + items_file = doc_dir / 'items.jsonl' + if not items_file.exists(): + continue + for line in items_file.read_text().splitlines(): + if not line.strip(): + continue + item = json.loads(line) + label = item.get('label') + chunk_id = item.get('chunk_ids', [None])[0] if item.get('chunk_ids') else None + if label and chunk_id: + if label not in label_to_chunk: + label_to_chunk[label] = chunk_id + ``` + + But we need to handle chunk_ids: It might be a list of chunk IDs. We can take the first chunk ID. + + But we need to confirm that each item has chunk_ids. It might have multiple chunk_ids. But we can pick the first. + + Then we can produce a list of labels and chunk IDs. + + We need to output them in a format that we can parse. + + We can print a JSON mapping or just print each label and chunk id. + + We need to capture the output. + + We need to call execute_code with this code. + + Let's do that. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nlabel_to_chunk = {}\nfor doc_dir in Path(''/documents'').iterdir():\n items_file + = doc_dir / ''items.jsonl''\n if not items_file.exists():\n continue\n for line in items_file.read_text().splitlines():\n if + not line.strip():\n continue\n try:\n item = json.loads(line)\n except Exception + as e:\n continue\n label = item.get(''label'')\n chunk_ids = item.get(''chunk_ids'')\n if + label and chunk_ids:\n # take first chunk id\n chunk_id = chunk_ids[0]\n if + label not in label_to_chunk:\n label_to_chunk[label] = chunk_id\n# Print mapping\nprint(json.dumps(label_to_chunk, + indent=2))\n"}' + name: execute_code + id: call_qd0d5ug0 + type: function + - content: | + { + "text": "713678f9-980e-47ed-9e1c-e6b533231d57", + "caption": "713678f9-980e-47ed-9e1c-e6b533231d57", + "footnote": "73a2443c-3c25-4fbb-a6d4-0dec371ef104" + } + role: tool + tool_call_id: call_qd0d5ug0 + - content: null + reasoning: |- + The output shows only three labels: "text", "caption", "footnote". That seems incomplete. Maybe the items.jsonl files only contain these labels? That seems unlikely. Maybe the items.jsonl files are not present or empty. Let's check the structure. + + We need to inspect the /documents directory. Let's list the directories. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\n# List directories\nprint([d.name for d in Path(''/documents'').iterdir()])\n"}' + name: execute_code + id: call_b60xwvbx + type: function + - content: | + ['40fa427f-7d9c-499f-9985-ce7f51d24db4'] + role: tool + tool_call_id: call_b60xwvbx + - content: null + reasoning: 'Only one document directory: ''40fa427f-7d9c-499f-9985-ce7f51d24db4''. Let''s inspect its contents.' + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\npath = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4'')\nprint(''Files:'', + [f.name for f in path.iterdir()])\nprint(''metadata:'', json.loads((path/''metadata.json'').read_text()))\n"}' + name: execute_code + id: call_rt6l2me5 + type: function + - content: | + Files: ['metadata.json', 'content.txt', 'items.jsonl', 'toc.json'] + metadata: {'id': '40fa427f-7d9c-499f-9985-ce7f51d24db4', 'title': None, 'uri': 'file:///private/var/folders/4k/n24fyvf11117vpdnpngx290r0000gn/T/pytest-of-ggozad/pytest-106/doclaynet0/page0.pdf', 'created_at': '2026-05-25 12:47:13.296904'} + role: tool + tool_call_id: call_rt6l2me5 + 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. + + Accepts chunk_ids from search results AND from direct file reads + (items.jsonl, toc.json). Verbatim copies only — chunk_ids that + don't exist in the database trigger a retry. + name: cite + parameters: + additionalProperties: false + properties: + chunk_ids: + description: List of chunk_id values from search results or VFS reads. + 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-603","object":"chat.completion.chunk","created":1779702510,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"The"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702510,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" metadata"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702510,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" shows"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702510,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" title"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702510,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" None"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702510,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" The"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" file"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" is"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" a"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" PDF"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" The"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" items"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":".json"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"l"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" might"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" be"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" large"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Let's"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" inspect"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" first"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" few"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" lines"},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702511,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702514,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","tool_calls":[{"id":"call_nvweqb8s","index":0,"type":"function","function":{"name":"execute_code","arguments":"{\"code\":\"from pathlib import Path\\nimport json\\nitems_file = Path('/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl')\\nprint('Size:', items_file.stat().st_size)\\nprint('First 20 lines:')\\nfor i, line in enumerate(items_file.read_text().splitlines()[:20]):\\n print(i, line)\\n\"}"}}]},"finish_reason":"tool_calls"}]} + + data: {"id":"chatcmpl-603","object":"chat.completion.chunk","created":1779702514,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[],"usage":{"prompt_tokens":4725,"completion_tokens":142,"total_tokens":4867}} + + data: [DONE] + + headers: + content-type: + - text/event-stream + transfer-encoding: + - chunked + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '28390' + 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. + + Chunk IDs come from two places: + - The `chunk_id` field on `search` / `await search(...)` results + - The `chunk_ids` field on `items.jsonl` rows (when you ground via direct file reads) + + Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs. + + Every answer that uses search or file-read evidence must be backed by `cite`. + + ## 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 and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora. + + ### 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. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file. + + Each row carries: + - `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results + - `label`: item type — one of `"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 + - `chunk_ids`: chunks that contain this item — pass to `cite()` to ground an answer that read this item directly + - `heading_level`: H-level for `section_header` rows; `0` on non-header rows + + ### toc.json + Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `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`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index. + + ## 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. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents. + 5. 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 list all different document element types mentioned. Likely the document element types are the labels defined in items.jsonl: "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote". But there might be other types in other documents. The question is to search for content about document element types or labels. So we need to search the knowledge base for references to "document element types" or "labels" or "element types" or "document element types" etc. Then gather all distinct types mentioned. + + We need to produce a list of all different document element types mentioned. We need to cite the chunk IDs that support each type. We need to gather all types across the corpus. The items.jsonl labels are a set of known types. But there might be other types like "section_header" etc. The question might be expecting the list of types from the items.jsonl. But the question says "Search for content about document element types or labels." That might refer to the items.jsonl labels. So we need to search for "label" or "document element types" across the knowledge base. But we need to list all different document element types mentioned. So we need to find all distinct labels across all documents. That might be the same set as the items.jsonl label values. But there might be other labels like "annotation" etc. We need to check. + + We should search for "label" or "document element types" or "element type" or "document element type" or "document element types" or "document element label" etc. But we need to find all types. We can search for "label:" or "label" or "type:" or "element type" etc. But we need to be careful to capture all distinct types. + + We can use search with query "label" maybe. But that might return many results. We can also search for "label:" or "label" but that might return many. We can search for "label" and "document" maybe. But we can also search for "label" and "items.jsonl" but we don't have that. + + Alternatively, we can read all items.jsonl files across all documents and extract the label field. That would give us all types. That might be more reliable. But the question says "Search for content about document element types or labels." So we might need to search the knowledge base for references to "document element types" or "labels" and then list them. But we can also read items.jsonl to get the labels. But the question might be expecting the list of types from items.jsonl. But we need to provide citations. We can cite the chunk IDs that contain the labels. But if we read items.jsonl, we can get the labels but we need to cite the chunk IDs that contain those labels. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide citations for each type. We can gather chunk IDs for each type. But we might need to gather many chunk IDs. But we can just cite a few chunk IDs that mention each type. But we need to ensure we cite at least one chunk ID that contains the type. We can gather all types and then cite a representative chunk ID for each type. But we need to ensure we cite the chunk IDs that mention the type. We can use search to find chunk IDs that contain the type names. But we might need to gather all types. + + We can use execute_code to list all items.jsonl across all documents and extract the label values. Then we can produce a list of unique labels. Then we can search for each label to get chunk IDs that mention them. But we can also use the items.jsonl chunk IDs themselves. But we need to cite chunk IDs that ground the answer. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide a list of chunk IDs. But we might need to provide chunk IDs for each type. But we can just provide a single chunk ID that contains each type. But we need to ensure that the chunk ID indeed contains the type. We can search for each type to get a chunk ID. But we can also use the items.jsonl chunk IDs. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we can use them. + + We need to produce a final answer: a list of all different document element types mentioned. We need to cite chunk IDs that support each type. We need to call cite before writing the final answer. We need to gather chunk IDs for each type. + + We need to decide how to gather the types. We can use execute_code to read all items.jsonl across all documents and extract unique labels. Then we can produce a list of labels. Then we can search for each label to get chunk IDs. But we might also use the items.jsonl chunk IDs directly. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we need to confirm. + + We can do the following: + + 1. Use execute_code to list all documents and read items.jsonl for each document. For each item, collect the label. Build a set of unique labels. + + 2. Then for each label, we can search for that label to get chunk IDs. But we might also use the items.jsonl chunk IDs. But we might need to use search to get chunk IDs that mention the label. But we can also use the items.jsonl chunk IDs. But we need to ensure we have chunk IDs that mention the label. We can use the items.jsonl chunk IDs. But we need to confirm that the items.jsonl chunk IDs are indeed chunk IDs that we can cite. The items.jsonl chunk IDs are the chunk IDs for each item. But we need to confirm that the items.jsonl chunk IDs are the same as the chunk IDs we can cite. The items.jsonl chunk IDs are part of the items.jsonl rows. They are used for cross-referencing. They are chunk IDs that we can cite. So we can use them. + + Thus we can gather a mapping from label to a sample chunk ID that contains that label. Then we can call cite with the list of chunk IDs for all labels. Then we can produce the answer. + + We need to be careful: The items.jsonl chunk IDs are unique across the entire corpus. They are used for cross-referencing. So we can gather them. + + We need to produce the answer: "section_header, text, table, list_item, caption, formula, picture, code, footnote" maybe. But there might be other labels like "annotation" or "definition" etc. But we need to check. + + We need to search for all labels. Let's do that. + + We can use execute_code to list all documents and read items.jsonl. Let's do that. + + We'll write code to: + + - import Path, json + - iterate over /documents + - for each doc, read items.jsonl + - parse each line as JSON + - collect label + - also collect chunk_id for each label + - produce a mapping from label to list of chunk_ids + - then produce a set of labels + - then produce a mapping from label to a sample chunk_id + + We need to output the mapping. We'll use print(). + + But we need to be careful: The items.jsonl might be large. But we can handle it. + + We need to ensure we use Path.read_text() and splitlines. + + We'll do: + + ``` + from pathlib import Path + import json + + label_to_chunk = {} + for doc_dir in Path('/documents').iterdir(): + items_file = doc_dir / 'items.jsonl' + if not items_file.exists(): + continue + for line in items_file.read_text().splitlines(): + if not line.strip(): + continue + item = json.loads(line) + label = item.get('label') + chunk_id = item.get('chunk_ids', [None])[0] if item.get('chunk_ids') else None + if label and chunk_id: + if label not in label_to_chunk: + label_to_chunk[label] = chunk_id + ``` + + But we need to handle chunk_ids: It might be a list of chunk IDs. We can take the first chunk ID. + + But we need to confirm that each item has chunk_ids. It might have multiple chunk_ids. But we can pick the first. + + Then we can produce a list of labels and chunk IDs. + + We need to output them in a format that we can parse. + + We can print a JSON mapping or just print each label and chunk id. + + We need to capture the output. + + We need to call execute_code with this code. + + Let's do that. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nlabel_to_chunk = {}\nfor doc_dir in Path(''/documents'').iterdir():\n items_file + = doc_dir / ''items.jsonl''\n if not items_file.exists():\n continue\n for line in items_file.read_text().splitlines():\n if + not line.strip():\n continue\n try:\n item = json.loads(line)\n except Exception + as e:\n continue\n label = item.get(''label'')\n chunk_ids = item.get(''chunk_ids'')\n if + label and chunk_ids:\n # take first chunk id\n chunk_id = chunk_ids[0]\n if + label not in label_to_chunk:\n label_to_chunk[label] = chunk_id\n# Print mapping\nprint(json.dumps(label_to_chunk, + indent=2))\n"}' + name: execute_code + id: call_qd0d5ug0 + type: function + - content: | + { + "text": "713678f9-980e-47ed-9e1c-e6b533231d57", + "caption": "713678f9-980e-47ed-9e1c-e6b533231d57", + "footnote": "73a2443c-3c25-4fbb-a6d4-0dec371ef104" + } + role: tool + tool_call_id: call_qd0d5ug0 + - content: null + reasoning: |- + The output shows only three labels: "text", "caption", "footnote". That seems incomplete. Maybe the items.jsonl files only contain these labels? That seems unlikely. Maybe the items.jsonl files are not present or empty. Let's check the structure. + + We need to inspect the /documents directory. Let's list the directories. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\n# List directories\nprint([d.name for d in Path(''/documents'').iterdir()])\n"}' + name: execute_code + id: call_b60xwvbx + type: function + - content: | + ['40fa427f-7d9c-499f-9985-ce7f51d24db4'] + role: tool + tool_call_id: call_b60xwvbx + - content: null + reasoning: 'Only one document directory: ''40fa427f-7d9c-499f-9985-ce7f51d24db4''. Let''s inspect its contents.' + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\npath = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4'')\nprint(''Files:'', + [f.name for f in path.iterdir()])\nprint(''metadata:'', json.loads((path/''metadata.json'').read_text()))\n"}' + name: execute_code + id: call_rt6l2me5 + type: function + - content: | + Files: ['metadata.json', 'content.txt', 'items.jsonl', 'toc.json'] + metadata: {'id': '40fa427f-7d9c-499f-9985-ce7f51d24db4', 'title': None, 'uri': 'file:///private/var/folders/4k/n24fyvf11117vpdnpngx290r0000gn/T/pytest-of-ggozad/pytest-106/doclaynet0/page0.pdf', 'created_at': '2026-05-25 12:47:13.296904'} + role: tool + tool_call_id: call_rt6l2me5 + - content: null + reasoning: The metadata shows title None. The file is a PDF. The items.jsonl might be large. Let's inspect the first + few lines. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nitems_file = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl'')\nprint(''Size:'', + items_file.stat().st_size)\nprint(''First 20 lines:'')\nfor i, line in enumerate(items_file.read_text().splitlines()[:20]):\n print(i, + line)\n"}' + name: execute_code + id: call_nvweqb8s + type: function + - content: | + Size: 6660 + First 20 lines: + 0 {"self_ref": "#/texts/1", "label": "section_header", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 1 {"self_ref": "#/texts/2", "label": "text", "text": "Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 2 {"self_ref": "#/texts/3", "label": "text", "text": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 3 {"self_ref": "#/texts/4", "label": "text", "text": "Ahmed S. Nassar IBM Research", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 4 {"self_ref": "#/texts/5", "label": "text", "text": "Rueschlikon, Switzerland ahn@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 5 {"self_ref": "#/texts/6", "label": "text", "text": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 6 {"self_ref": "#/texts/7", "label": "text", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 7 {"self_ref": "#/pictures/0", "label": "picture", "text": "Figure 1: Four examples of complex page layouts across different document categories", "page_numbers": [1], "heading_level": 0, "chunk_ids": []} + 8 {"self_ref": "#/texts/8", "label": "caption", "text": "Figure 1: Four examples of complex page layouts across different document categories", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 9 {"self_ref": "#/texts/312", "label": "section_header", "text": "KEYWORDS", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 10 {"self_ref": "#/texts/313", "label": "text", "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["92e8dd8b-171e-4933-a0f1-16f5ce6ac545"]} + 11 {"self_ref": "#/texts/314", "label": "section_header", "text": "ACMReference Format:", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 12 {"self_ref": "#/texts/315", "label": "text", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["93ad7de1-8b78-4585-9865-01058d67019d"]} + 13 {"self_ref": "#/texts/316", "label": "section_header", "text": "ABSTRACT", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 14 {"self_ref": "#/texts/317", "label": "text", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["c0d1d300-462b-445b-8421-6a09606a2c9b", "912809c6-deb1-4a1f-91e8-ef3eadfeb852"]} + 15 {"self_ref": "#/texts/318", "label": "section_header", "text": "CCS CONCEPTS", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 16 {"self_ref": "#/texts/319", "label": "text", "text": "· Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ;", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 17 {"self_ref": "#/texts/320", "label": "text", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 18 {"self_ref": "#/texts/321", "label": "text", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 19 {"self_ref": "#/texts/322", "label": "footnote", "text": "© 2022 Copyright held by the owner/author(s).", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + role: tool + tool_call_id: call_nvweqb8s + 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. + + Accepts chunk_ids from search results AND from direct file reads + (items.jsonl, toc.json). Verbatim copies only — chunk_ids that + don't exist in the database trigger a retry. + name: cite + parameters: + additionalProperties: false + properties: + chunk_ids: + description: List of chunk_id values from search results or VFS reads. + 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-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"Scrolling"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" further"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" might"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" reveal"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" more"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" labels"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Let's"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" inspect"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" rest"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" of"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" file"},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702518,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702523,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","tool_calls":[{"id":"call_c2sddzkn","index":0,"type":"function","function":{"name":"execute_code","arguments":"{\"code\":\"from pathlib import Path\\nimport json\\nitems_file = Path('/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl')\\nlines = items_file.read_text().splitlines()\\nprint('Total lines:', len(lines))\\n# Find unique labels\\nlabels = set()\\nfor line in lines:\\n if not line.strip():\\n continue\\n item = json.loads(line)\\n labels.add(item.get('label'))\\nprint('Unique labels:', labels)\\n# Show some lines with each label\\nfor label in labels:\\n print('---', label)\\n for line in lines:\\n item = json.loads(line)\\n if item.get('label') == label:\\n print(item)\\n break\\n\"}"}}]},"finish_reason":"tool_calls"}]} + + data: {"id":"chatcmpl-191","object":"chat.completion.chunk","created":1779702523,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[],"usage":{"prompt_tokens":6806,"completion_tokens":212,"total_tokens":7018}} + + data: [DONE] + + headers: + content-type: + - text/event-stream + transfer-encoding: + - chunked + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '30576' + 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. + + Chunk IDs come from two places: + - The `chunk_id` field on `search` / `await search(...)` results + - The `chunk_ids` field on `items.jsonl` rows (when you ground via direct file reads) + + Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs. + + Every answer that uses search or file-read evidence must be backed by `cite`. + + ## 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 and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora. + + ### 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. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file. + + Each row carries: + - `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results + - `label`: item type — one of `"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 + - `chunk_ids`: chunks that contain this item — pass to `cite()` to ground an answer that read this item directly + - `heading_level`: H-level for `section_header` rows; `0` on non-header rows + + ### toc.json + Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `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`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index. + + ## 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. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents. + 5. 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 list all different document element types mentioned. Likely the document element types are the labels defined in items.jsonl: "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote". But there might be other types in other documents. The question is to search for content about document element types or labels. So we need to search the knowledge base for references to "document element types" or "labels" or "element types" or "document element types" etc. Then gather all distinct types mentioned. + + We need to produce a list of all different document element types mentioned. We need to cite the chunk IDs that support each type. We need to gather all types across the corpus. The items.jsonl labels are a set of known types. But there might be other types like "section_header" etc. The question might be expecting the list of types from the items.jsonl. But the question says "Search for content about document element types or labels." That might refer to the items.jsonl labels. So we need to search for "label" or "document element types" across the knowledge base. But we need to list all different document element types mentioned. So we need to find all distinct labels across all documents. That might be the same set as the items.jsonl label values. But there might be other labels like "annotation" etc. We need to check. + + We should search for "label" or "document element types" or "element type" or "document element type" or "document element types" or "document element label" etc. But we need to find all types. We can search for "label:" or "label" or "type:" or "element type" etc. But we need to be careful to capture all distinct types. + + We can use search with query "label" maybe. But that might return many results. We can also search for "label:" or "label" but that might return many. We can search for "label" and "document" maybe. But we can also search for "label" and "items.jsonl" but we don't have that. + + Alternatively, we can read all items.jsonl files across all documents and extract the label field. That would give us all types. That might be more reliable. But the question says "Search for content about document element types or labels." So we might need to search the knowledge base for references to "document element types" or "labels" and then list them. But we can also read items.jsonl to get the labels. But the question might be expecting the list of types from items.jsonl. But we need to provide citations. We can cite the chunk IDs that contain the labels. But if we read items.jsonl, we can get the labels but we need to cite the chunk IDs that contain those labels. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide citations for each type. We can gather chunk IDs for each type. But we might need to gather many chunk IDs. But we can just cite a few chunk IDs that mention each type. But we need to ensure we cite at least one chunk ID that contains the type. We can gather all types and then cite a representative chunk ID for each type. But we need to ensure we cite the chunk IDs that mention the type. We can use search to find chunk IDs that contain the type names. But we might need to gather all types. + + We can use execute_code to list all items.jsonl across all documents and extract the label values. Then we can produce a list of unique labels. Then we can search for each label to get chunk IDs that mention them. But we can also use the items.jsonl chunk IDs themselves. But we need to cite chunk IDs that ground the answer. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide a list of chunk IDs. But we might need to provide chunk IDs for each type. But we can just provide a single chunk ID that contains each type. But we need to ensure that the chunk ID indeed contains the type. We can search for each type to get a chunk ID. But we can also use the items.jsonl chunk IDs. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we can use them. + + We need to produce a final answer: a list of all different document element types mentioned. We need to cite chunk IDs that support each type. We need to call cite before writing the final answer. We need to gather chunk IDs for each type. + + We need to decide how to gather the types. We can use execute_code to read all items.jsonl across all documents and extract unique labels. Then we can produce a list of labels. Then we can search for each label to get chunk IDs. But we might also use the items.jsonl chunk IDs directly. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we need to confirm. + + We can do the following: + + 1. Use execute_code to list all documents and read items.jsonl for each document. For each item, collect the label. Build a set of unique labels. + + 2. Then for each label, we can search for that label to get chunk IDs. But we might also use the items.jsonl chunk IDs. But we might need to use search to get chunk IDs that mention the label. But we can also use the items.jsonl chunk IDs. But we need to ensure we have chunk IDs that mention the label. We can use the items.jsonl chunk IDs. But we need to confirm that the items.jsonl chunk IDs are indeed chunk IDs that we can cite. The items.jsonl chunk IDs are the chunk IDs for each item. But we need to confirm that the items.jsonl chunk IDs are the same as the chunk IDs we can cite. The items.jsonl chunk IDs are part of the items.jsonl rows. They are used for cross-referencing. They are chunk IDs that we can cite. So we can use them. + + Thus we can gather a mapping from label to a sample chunk ID that contains that label. Then we can call cite with the list of chunk IDs for all labels. Then we can produce the answer. + + We need to be careful: The items.jsonl chunk IDs are unique across the entire corpus. They are used for cross-referencing. So we can gather them. + + We need to produce the answer: "section_header, text, table, list_item, caption, formula, picture, code, footnote" maybe. But there might be other labels like "annotation" or "definition" etc. But we need to check. + + We need to search for all labels. Let's do that. + + We can use execute_code to list all documents and read items.jsonl. Let's do that. + + We'll write code to: + + - import Path, json + - iterate over /documents + - for each doc, read items.jsonl + - parse each line as JSON + - collect label + - also collect chunk_id for each label + - produce a mapping from label to list of chunk_ids + - then produce a set of labels + - then produce a mapping from label to a sample chunk_id + + We need to output the mapping. We'll use print(). + + But we need to be careful: The items.jsonl might be large. But we can handle it. + + We need to ensure we use Path.read_text() and splitlines. + + We'll do: + + ``` + from pathlib import Path + import json + + label_to_chunk = {} + for doc_dir in Path('/documents').iterdir(): + items_file = doc_dir / 'items.jsonl' + if not items_file.exists(): + continue + for line in items_file.read_text().splitlines(): + if not line.strip(): + continue + item = json.loads(line) + label = item.get('label') + chunk_id = item.get('chunk_ids', [None])[0] if item.get('chunk_ids') else None + if label and chunk_id: + if label not in label_to_chunk: + label_to_chunk[label] = chunk_id + ``` + + But we need to handle chunk_ids: It might be a list of chunk IDs. We can take the first chunk ID. + + But we need to confirm that each item has chunk_ids. It might have multiple chunk_ids. But we can pick the first. + + Then we can produce a list of labels and chunk IDs. + + We need to output them in a format that we can parse. + + We can print a JSON mapping or just print each label and chunk id. + + We need to capture the output. + + We need to call execute_code with this code. + + Let's do that. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nlabel_to_chunk = {}\nfor doc_dir in Path(''/documents'').iterdir():\n items_file + = doc_dir / ''items.jsonl''\n if not items_file.exists():\n continue\n for line in items_file.read_text().splitlines():\n if + not line.strip():\n continue\n try:\n item = json.loads(line)\n except Exception + as e:\n continue\n label = item.get(''label'')\n chunk_ids = item.get(''chunk_ids'')\n if + label and chunk_ids:\n # take first chunk id\n chunk_id = chunk_ids[0]\n if + label not in label_to_chunk:\n label_to_chunk[label] = chunk_id\n# Print mapping\nprint(json.dumps(label_to_chunk, + indent=2))\n"}' + name: execute_code + id: call_qd0d5ug0 + type: function + - content: | + { + "text": "713678f9-980e-47ed-9e1c-e6b533231d57", + "caption": "713678f9-980e-47ed-9e1c-e6b533231d57", + "footnote": "73a2443c-3c25-4fbb-a6d4-0dec371ef104" + } + role: tool + tool_call_id: call_qd0d5ug0 + - content: null + reasoning: |- + The output shows only three labels: "text", "caption", "footnote". That seems incomplete. Maybe the items.jsonl files only contain these labels? That seems unlikely. Maybe the items.jsonl files are not present or empty. Let's check the structure. + + We need to inspect the /documents directory. Let's list the directories. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\n# List directories\nprint([d.name for d in Path(''/documents'').iterdir()])\n"}' + name: execute_code + id: call_b60xwvbx + type: function + - content: | + ['40fa427f-7d9c-499f-9985-ce7f51d24db4'] + role: tool + tool_call_id: call_b60xwvbx + - content: null + reasoning: 'Only one document directory: ''40fa427f-7d9c-499f-9985-ce7f51d24db4''. Let''s inspect its contents.' + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\npath = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4'')\nprint(''Files:'', + [f.name for f in path.iterdir()])\nprint(''metadata:'', json.loads((path/''metadata.json'').read_text()))\n"}' + name: execute_code + id: call_rt6l2me5 + type: function + - content: | + Files: ['metadata.json', 'content.txt', 'items.jsonl', 'toc.json'] + metadata: {'id': '40fa427f-7d9c-499f-9985-ce7f51d24db4', 'title': None, 'uri': 'file:///private/var/folders/4k/n24fyvf11117vpdnpngx290r0000gn/T/pytest-of-ggozad/pytest-106/doclaynet0/page0.pdf', 'created_at': '2026-05-25 12:47:13.296904'} + role: tool + tool_call_id: call_rt6l2me5 + - content: null + reasoning: The metadata shows title None. The file is a PDF. The items.jsonl might be large. Let's inspect the first + few lines. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nitems_file = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl'')\nprint(''Size:'', + items_file.stat().st_size)\nprint(''First 20 lines:'')\nfor i, line in enumerate(items_file.read_text().splitlines()[:20]):\n print(i, + line)\n"}' + name: execute_code + id: call_nvweqb8s + type: function + - content: | + Size: 6660 + First 20 lines: + 0 {"self_ref": "#/texts/1", "label": "section_header", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 1 {"self_ref": "#/texts/2", "label": "text", "text": "Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 2 {"self_ref": "#/texts/3", "label": "text", "text": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 3 {"self_ref": "#/texts/4", "label": "text", "text": "Ahmed S. Nassar IBM Research", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 4 {"self_ref": "#/texts/5", "label": "text", "text": "Rueschlikon, Switzerland ahn@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 5 {"self_ref": "#/texts/6", "label": "text", "text": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 6 {"self_ref": "#/texts/7", "label": "text", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 7 {"self_ref": "#/pictures/0", "label": "picture", "text": "Figure 1: Four examples of complex page layouts across different document categories", "page_numbers": [1], "heading_level": 0, "chunk_ids": []} + 8 {"self_ref": "#/texts/8", "label": "caption", "text": "Figure 1: Four examples of complex page layouts across different document categories", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 9 {"self_ref": "#/texts/312", "label": "section_header", "text": "KEYWORDS", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 10 {"self_ref": "#/texts/313", "label": "text", "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["92e8dd8b-171e-4933-a0f1-16f5ce6ac545"]} + 11 {"self_ref": "#/texts/314", "label": "section_header", "text": "ACMReference Format:", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 12 {"self_ref": "#/texts/315", "label": "text", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["93ad7de1-8b78-4585-9865-01058d67019d"]} + 13 {"self_ref": "#/texts/316", "label": "section_header", "text": "ABSTRACT", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 14 {"self_ref": "#/texts/317", "label": "text", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["c0d1d300-462b-445b-8421-6a09606a2c9b", "912809c6-deb1-4a1f-91e8-ef3eadfeb852"]} + 15 {"self_ref": "#/texts/318", "label": "section_header", "text": "CCS CONCEPTS", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 16 {"self_ref": "#/texts/319", "label": "text", "text": "· Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ;", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 17 {"self_ref": "#/texts/320", "label": "text", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 18 {"self_ref": "#/texts/321", "label": "text", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 19 {"self_ref": "#/texts/322", "label": "footnote", "text": "© 2022 Copyright held by the owner/author(s).", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + role: tool + tool_call_id: call_nvweqb8s + - content: null + reasoning: Scrolling further might reveal more labels. Let's inspect the rest of the file. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nitems_file = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl'')\nlines + = items_file.read_text().splitlines()\nprint(''Total lines:'', len(lines))\n# Find unique labels\nlabels = set()\nfor + line in lines:\n if not line.strip():\n continue\n item = json.loads(line)\n labels.add(item.get(''label''))\nprint(''Unique + labels:'', labels)\n# Show some lines with each label\nfor label in labels:\n print(''---'', label)\n for + line in lines:\n item = json.loads(line)\n if item.get(''label'') == label:\n print(item)\n break\n"}' + name: execute_code + id: call_c2sddzkn + type: function + - content: | + Total lines: 22 + Unique labels: {'section_header', 'text', 'picture', 'caption', 'footnote'} + --- section_header + {'self_ref': '#/texts/1', 'label': 'section_header', 'text': 'DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis', 'page_numbers': [1], 'heading_level': 1, 'chunk_ids': []} + --- text + {'self_ref': '#/texts/2', 'label': 'text', 'text': 'Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['713678f9-980e-47ed-9e1c-e6b533231d57']} + --- picture + {'self_ref': '#/pictures/0', 'label': 'picture', 'text': 'Figure 1: Four examples of complex page layouts across different document categories', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': []} + --- caption + {'self_ref': '#/texts/8', 'label': 'caption', 'text': 'Figure 1: Four examples of complex page layouts across different document categories', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['713678f9-980e-47ed-9e1c-e6b533231d57']} + --- footnote + {'self_ref': '#/texts/322', 'label': 'footnote', 'text': '© 2022 Copyright held by the owner/author(s).', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['73a2443c-3c25-4fbb-a6d4-0dec371ef104']} + role: tool + tool_call_id: call_c2sddzkn + 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. + + Accepts chunk_ids from search results AND from direct file reads + (items.jsonl, toc.json). Verbatim copies only — chunk_ids that + don't exist in the database trigger a retry. + name: cite + parameters: + additionalProperties: false + properties: + chunk_ids: + description: List of chunk_id values from search results or VFS reads. + 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-497","object":"chat.completion.chunk","created":1779702524,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"So"},"finish_reason":null}]} + + data: {"id":"chatcmpl-497","object":"chat.completion.chunk","created":1779702524,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]} + + data: 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accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '77' content-type: - application/json host: @@ -1753,7 +8683,7 @@ interactions: parsed_body: encoding_format: base64 input: - - class label + - picture model: qwen3-embedding:4b uri: http://localhost:11434/v1/embeddings response: @@ -1764,7 +8694,881 @@ interactions: - chunked parsed_body: data: - - embedding: 5fpZttg2F72W/gc9gFRQPHfKpbnphIQ9U4J8PZ2UmjzIMMM8xpNcvAye1DyX+7087GorO2YFH70znu87xpwxvRyTtzyYoCu8oq7pPGsllLvltrC8eomTvJgfrTwBqzc9E94KvEvtQzyGna28qZyRvWFRGD3JTas8RwihO6DNTL2GVNg80xJIvNVGpTsZooi8Gzw9u8Mr4LuuOva8Yhv5vBHyED0VxAK9hXvHPNcFk7m7nOA83D8DvGie17rwTgE9iGpmvNxr5LyKr1A8xPImPDDR1LwYRcu8IaShPFVfPT0E0MI8avySuzaG6rvtYRW8G7/Wu6qztzpZ7EK9OYxrvLbItLsEd/y8iyu/vHwnZb2HGl48/9psuy5pC70oqBc9bUGMvMgYXzyawcm8Ok8RvTz7N7zCKmY8a7kfPFgwMD05WWO8xKqKvFGvmjtcuRc8QXfPPMzBj7wgpUM9TtvAO4ClqrzI3s07Uy/qOmLcezxvBLE4bQrkPMIi+7lvW1o8589ZvPKDiLw264i8u8Pkuu4ClTtHEs46y+R5PIQEeLw9aKg72DG2vPCR4byTGym8ctVxO4WzgDydYAQ8VUVTvDTOe7xXkCS8GXosvBcwmLwE3T07x7YGPYviSTwF0cm8yTD0u+iaoDxGMB28vNusO4mK0bpIHy67qU6XvMwJBbx0u626kGYcPI7Gljx2oKm8tCDlO7MH5LwN7wQ9ts2IOxql97spaoY8eoHVuwWB6DtTfqy8gfQRPL7VwLvXIYo8voK5vP5tJ72RDvW7oX6tuzIbFjwY5XY6+4dhPIVpPLx5MYS8NbY9OjUtF7vz2188S/GevPCQmTx0/ie8wP9cPFWau7uNONY8HTgnvMtDBz3skIk8T/GtPIby9bseFhS8tmixus6odzwFvk08CArbvHy4NLxREwQ8yNUhvf41NLzDgmU7m4sMvbpOmbyieeA8Zyc4vLrMGT3Wb0k9y28TPNWZHDx2PAK8v8TTOzOThLx2CmI8k/A4u2JTAr0kKmq8HONGvHJRCLxdxoK8w6C+u+u+0bwDH1+87ce2u37qpDy5QFM7fmEOPMqWp7yvk5+6B2RpvL7aoTtepau6ePuLvGJWDTzSpb681dFbuzmKUDsF/ky8CA4BvRiwjrsbvZE8Dq2ZvLSEMLzTAY88Tv68PPUPirv3xVw7VfwYvI2h0Tv0ZSe9xbLmujiaTroPvUi7afKSPEZpc7zcFpc83CIovDSRirwB6um7bGHgOqnjrDwWnCK8lsN1u7V4fLu/KKO8ErEzPCTKpLzojYY88+HpO9BCVLxTAv28KKCeOXWB+7zCgWa8pC/JvJFDebypsLE7Kn8aPRLvNrxMlKY8IwkEu7CPmLxPUhK9EyTzOpKaPLsGeas7vMw2PEGmV7w8ZEK8YpDVu0DiujxA3uY8BKPhu58sNLuZhlU8xSAYPXPcSLwR/EK8Q4OCO5oPnzxQTvK8g58KPOkDDzxPM7A8abTrPFmEkrsFzjg8FKIuveF2IDxLx2U8syJSvLI90Dt5+4y6Wz9wu8zIuTuh8aI8nNfPOaV+uzxrJoy8jTF0vF3lsbkJBt47htIgvLMDSrwCXZ+84bhDvGCDsDxxJI27dBJaPKZPZ7wlVxk84IiqvHJchbrERks8Hzxbu/5geDyttem8yXMmOiCmgzoHGyk8uFw1vRDXJLz9fT08EArgvJfztbxY5g684e15vX5cVb2RzZK8jke8vBYSmjyvqg89E9aIPIkHJT1RahE8Ig2jPNuq5jwSc7W89YbuvJHsITz2V4O7OAQ7u07jRD1wCdo8RwhkPLQoybxRGcs88BdrukfpLrwt0m69/pC0O843qrq8Z0K8ZbWBvDjTW7zXCbw7iQuwvCwuoLxk9Mi8JiIePAjbsTxJl7y8yC6OvDtEWTykk++83bG1vG1cQDwBSSg8+PlGPK6oB73JL7s72s1qvO/7jTz4qD08WAXOvBAlhbw3Nfm87kM4PTHbmTs3RdS6nJpevMOZfLuWbMi8Y/PQO1AvBzxLrLU812nMu9dpJ7wnjwc9nU6uvIYAyLteaZQ6Y/J+vK7pZLx4iAE7payIuJDq7jyRtyM8lOmqOxnrzbxG2kK7GXQkO10BBT38ouI7qQEdvK49ezy/PjG9SJHcvHCKlrxJn2g6WAAHvTpDVLxgyBA909dVPASquLz//wI689EGPKWYizwbyyq9ND3uOtPzKbvRGTA8k0CVO8OXibzSLzE8RPifOzSxCb23m3o8Ytzju4ofhjtNTac6jqalvAbeWjwkotm8/ZCdOqSHYrx6H7E8UvXyPJ+OSz0Spsc8c167vHTMOTz+HTe8ufCUu6nIRLyOZRU9dTCBu/uwvDs0etM7cZ4hvSs8h7yVK5o8natuO0gqgTzdrrK8dpjNvObPpbw31wE9GTPzu0s2groVza87R5M4ugL+ObzxVAa9r5FJu+7r7b0mQ448lLYCPS6/Z7zqX2q77WTmvJfWlrwTFsy81q4IvGW7HD3B+JC8z28gvMXigrxbyzm8Gg2NPMuR/Tyx73Q8dMD+OxUYUjy0NOs8gRWFvINsILwmqsI87Z9JuxxZzDzkeQU8zMTePKsY+juL6dC8dWD6O0OdFj2ABxs7edWZvLvmhjwtmcU8nPnrOvQGmDuayh69JO+Xu4NeiDurxcK70mNhPNj7/zzrEiM7vsMsvQM0Kz0WxXk8DUI0vL1zlTvhKek87cukPGu0ajwCcJg8Yd7jvFeohzyJYwu8+/axuxCrmrx+n7E8ld2KPPAynrwfvow7ZdG5PBD93LzugQq9FVXHOw5yBzwSpNi8bTbRu19+sDo+xaY8OrCSPI8/hLxZmiw81twFveJDSDwL2ZI7uO1LO5GTjzzWFGa6gzCdPEDcp7nS+/I8K+VDO9SUkTzCbYa8TK/pPMhyALtdFzm9MfBOu6s0YTxrgrm7c46GPD/uBbyh5Gc8HtIBPdEcjDx6nDM9LXtJO52UEjyBzgW9Z1LgOp87UbqFcMU7ajNBuilbsjqItKO8RphnuoRJDbw47gg8CcU3vDptW7viuU48HBzZOz1DYbw/C5y7PXfCvDGaGL2EqyO9r6bJuvTY+Dukwu85njl4vJSNQ7zDJXO80L6MvCB8wzyndqU8G9alPLNlwDxbXtS8zUWkPIWlwTz84vS6Rql7vDPPBb0IsMi8lfvru2+S/btBPJq7izTNPKQeuLuoSQa8E+TWPNFWCjzAyKe7MGmDvBVanDw9qBS9VCk0uyRWyjo5+wi6lx8yO4uqJTxBGeI7ZO8gvWaybrx4RMO6DHNOvE4OGDtQiBG9O73Xu83c97y3wmm8mIQnuk1Ihr3pMmC76R6zOl75A7z7j3C8kSTbvLtz/TvZwi49sGU+vBCEjLzqYUc6V+ksvZev8zzEuMw70zczPFYmNLvjh5Q8zcK+PDul3zsNzSO6ZVg7vRCc8rxqgA85eBMcPDSXELvX4Mq7jEmnPNvH/rqm0ZQ8gAXkPDg8b7yMLes8l0GXujyg/TtRhL87ng4jvOMin7zAQv27bssRPGVnYr1AN/W71nYdvcGbsTywb2Y88TixvJGtpLy10s07tGMUvAUz2rseQD48zRj5vPoW0jurjE08CItPPE6lgrzzlxM9r2YHPJpHtTyUNwi8+C78u3w4tjsTHhe8J+WZvAsxwTvWEAM8drY0PC2ZCr1AGvk8ZYIqPcA2RDxNytc8osm4PHp9hTsIT7q7a6TTvH6fpDx27pm7/uM1PH9+77pqqre8t99mPVKcBzsiP7S7hBGmPP+tuTz4Z648yhTVu9ZFTLzEbXq8ENw0vJ5+UryWz4O8mR4nuprrXDzEE/e89ns+PUoXuTw3BSW9s6vIu0GmkLwy0Vs88ODmPO6vRzxD1o68J7NWPZXal7uCAFC8fzZOvG6rGDwfNpm8zNDxuxRP0jvpHl08E28QvcGI1zvMRCk8A7XvuwctXTxYujo9vX4sPCHHIDwwvMG8blLfvMSz9jyb4Yq7i8VmvCdZtDyFFFM85JjdOuOytzpwTT080dgFu08LXrsqHYk89aMKPc8DYzscbsu70Z8OO306hrw/TdC8NWxBvMNQET293Ai8PEavO93zWjz/3048466Ru9KUJzxxE7o7TXcoPAICSLy2eAW9LP5BvGJaXzwF3ki8jhSOPMiuurx6wlg7CLzzO+J/y7x4Vdm8+rSfvOF1GL3TTPu6RXQ9vTIPmDy+FOK77KiQvGvXDLy2C2Y8fc4puz6lwzzdc6a7rpZFvQGxo7uRfp65M4iIOjriLzrnNBE9Ah+VvJGJmTuHcwi8uK2jPLMsEbts/bY8BmKqO09ty7wj3Ko5rYLxOplLmDzab7m8sgcJPRQ6qLwII7g8O38NPTgYtjyLZv25U9ZXvD+jnbzT27E7Zo1Du4PbSz05VSu7sobZu+tUuzxdJVk8T+GAPBYYuDvqb008ruiYPHa6EztPmC08eJ3nu+Rqp7xB4By9bX4dPJiEiboudQc9oyP9vPREwbtvhD45Cg2AvH3CDDwLAlc7+myLO7mMeruUIY49jLyHuwAgtLvCfiE95cgtPfhPJT10yuI7qKeHu3Y1q7xX6hw8rXC8vDbb/rzKieG8xHyjumKw47y6dI07O1SgvLqAfLsl93+9PESSPLs5+TwKl2E8S6sjO9nKXT20oAA8YFBBvNJIfDzbOVG8q84lvPBk8Ty+z6+7YSQUPfqHVDzjsjm846MJO7zdijw6A6k7O0vDPLJ3Erxy7Oe8RkfDPM4zJz3mCIY816dVPFZ+9Lt7AJi8VcfrvIKYLD1eiyK8KLqEulJntztRz1+8eB+QPHb89DwEd7E8QQ6KvLf6rLubMpw7tyhcPJxxPbx8NAK9OYTdPNl1KTxPhju972a6OnJawzsooec8Z6XlvGzlpjvRp4w8IPcOvHVm0ryy8i69DugGPTteyzqlzxi88xhXvKL0+rxfrxO9oJHivH6USjuyll28AWTZPDTQuDq7o+e813LZu4fCKbz0Hmw8M89nu91iQDxYEzq8gAmBPFpUKj3JQty6vFv5OysejjtLIXy7PEZOO7DMSTwwsm+7w5CEu+XTKb1ljX08Aj1IvKxQzjsh+RE965kXvNvtaTxteQs8wZ09OwiYkTviW/a7a0yMPMMi7jx6XAo79sR1OxTaJbzZqYI8SW1KvPAcajuEDQw8hp3su7GbMbwJnmm89YgsPPk7a7z+ygU9b5cUPRkRETvDao68ITaYvKkzFjwnO4+72MCYO0MxNb3lu4k7gIJLvIW/OTxhNsy8+/+JvOctKDpDZ907yv27u3xY1DzwSB897deGu7QDlDxL1Qq8ai/CO+6K/bzT29M8ron2PG553LxABp08i3pnvNhC/jx+iBS8qfGGPOYdPr3d9v856uMTPBsehzsWtzs7Vs9/vLfSejxUp8s71gBwu9ktdbyTPZo8OY3YuvUMl7ufqx489IcovIO3RTy4/p+8D8EXPA1fkTuUFXe7PdCYPGSfvjuLopg8FrvNuznihDxmdQ88IpNDPLFgWLzOOrU83SM/vGF9Gr3gmZm6gqkJPFjlRDxQUeG7xZrTu17m97vNvSK8D7mmOQTtpLxIv3o7Nkf/O4+qnDxQ27q7tEiZOnpGXLzEc6G8zK4XO4w55TyKARi9YRIjvCpXAj0+RaG775QEPF+43rx+6ce8W3ArvYHEQjw1qiI8HtnpvKD3mDwoWwK94KCuO29En7yx3hC7ssqvvGOEMrwRwVa8ePtdvF0/DLuRrR69qsG8vMCdEjy/xUa8eNWdvK8APzsG54m7mTWiPOe3Aj2zapI7sOnOPCHTND21OJC8bCafvEJUVrxwC389so2/vGo2MLzwvz27T2Jvuz2RhDqo0d86Lo+GuydW07w02vO7bRzPvK4uFLwZL7Y844/MPJT8QDwY61s8JNgCvff6Cj1fNVu8WjZ7PNKw7bt19Aa9m6GXvG6b9LyvVLa7cwFqvMQcorzNRt87bUjavFPH2jxHBys8uAF/PAI07DxZMlS8JDecvAjR0ju2SQC9bVCsPPaBMj1fTVc8zSQVPfm0ATwMRfm8JWdfvOafPDtj4H06GFqlvEk8kzu+KQs8dRWEvDX7XTx7XTc9DaxJuxGjTT1Y+Ow7oCiWupWMeLwvSqq77/QKPD9U+TyAjwy80N+MvN57yzz52YQ84BbcvNWWqTykO4C7jYnBOw8UFDoFiwi6lV6pvI5qmLy/oMW7/TQbPHcgibyUIkm7/jGYuz6017yUdhU9D5YFPUQZoLzDTky7bUQzPb/WvDsFp/U75PsCvTPuQDurkYu8ZO41PO6kY7tTI9e8Atq+PGVWbTyhkwK8k5oZugfRFrygxEE9hvjAuye5ZrsChgK9C5OaO4by4rwzRx08YMKou3t5prz3LCq83R8JPL4/zLx5Vo88KHULvUIVR7sS0iw8wSF6uny2C7q95pq8MR0wPZ1f+LpBKwy7fQNpvN8Lbjx0rmS80CCZO5dNAzx+gdO7CZodPYfR17sJtue7eX0QvJHBhjtdJgS9sSHPvMOvZryvj3Q8A/OtPANjALxFqMs7R+zoPBGkhjw9hpE8xKk6PPXmpbwlypA7Bb3fO6cmJbvIaJi56zFJOlk3cTwRiDa7eMwAPHVjI7z1/DA94MajPLQC1rvSZ5I8VrKNO+4gB72zYFu97CgXOd6CV7v66VM7Gpt7u5j/JbzyGaw8XyNbvDWRGjxNWhQ9L5BIvFWXj7ow9b+83PajO5szsTzmX4I7yGBjPONfi7sUSrY7WP+Pu98bJD10g7C6a7WsvFRhYD1amXu8nPvcvKUiLbywGgI7jNRHPO1lpbwywVY8JyljvMhXjTwoyJG7/983uxvsjbxYHuO8yD7KPDiaaLuJMmC9RqyEPGxI0Tyun9m8HWgePJ7GEr1o4pu7MyE1uyPnFrzz6bk8fFdFvGUXhryfcAM91gnoO7irFjxjmjy7RXbwvBowOjw3e427Yt//vL0Cp7s+ZMa75R2KO79f6rzZ4mS74+crPSAnFr1g7Ly80UqTvESyrLzWqGi8GRSWO757bDzFJYQ7JRHmu3qFx7lfV5a7RPDau2M99DtolrI7EdMRPKhpvbwvO/481252OgrwET0Vrl27nUMIvBj4Aj3VmkY6zNm8PCrUrby4HSi9dC3CvIMoAb2tjB88jMpFPOaO0bt2NgS9jrM8uEcBATx4/he9OlYFvI2HurthXHq7ELTrPNq6jrzMKHw8VX+pPAKmKDwaYMO8Zb4+OwVDrDyI96Y7gIuEPKk3Qrxj/3u8LQOEOqwDyjwP4oO6QkfNvKFRgDwr63e744tpPJ2D4rzWFpC8lRmrPJPH6Tx+SAU8l766PFkCDbwWWA+8RUkiuo9qfTpleMg8Qjg8PMJXlzzfG8U8+rvgvAczajy/AaS8tVAlPJyBQLyhxkY8KT4MvVJy/Dz/isc88QnUvNJphjx3nZ68Ko3hvAQQi7syoxC88J+zulokM70rXck8cqGGPFsmarsUv9U8tBC3PG3pQb1IOxW9Op45vITWvDw8NJu8yM8GvexPmbze1V+8C/emPBCp27vfCxO8MPIDvKkhUz2ZsQQ81P6uu5K+3ryYvYE5xTHevASZIT3a2jM8E4qdvKX/QjyETd86pSgKvQiuM7wGX1y8Utk7PFr8VDzHOss60MICPVr86Tx93TQ7JsW6PNdT8LsgbYI8p+5AvNrVTrxkql28IB/FOyigvroCNcw805HIO3hAaLuofh+7H9fLO95Dxzu/8yA8lJVMPCFd27vXspK7WH2Su5YWhrxNFMw7J8HmPNoS1rsjMia8ygBXPUwqlbzKk6q8JQbYvOJ1J7tzjsu8X1IAvfbgGD3H+Zw82JpUvHNX3buF2h08H5EPPaPK8Lz0WiY6JNWyvCdNorpNd1C8/SgRu2Qtwjygzdy7hknGO0OqRLwiOA28CEevPGNvDDzK7I27kuoDu3PisDvFnfG8smgxPdBwrDtdRd+7rd+6u91th7z2X2u9g992PG0nszxo4Ky8Q0AUvOyj9brxbXq8Fxx0PP7cNLsN00s9mszeunDWm7yfxjS8wlv2OaoTBT2fDBG9qufMPNpjKLs3M6A8MW8XvAMTYDuUq5e6JryJOl/mWrpp9D68eN4DPfwHibp04EM85HAcPNb62bys5b07qOiHvC3n8LwzBc68oTXlOtUAdLy4CxA80HL6vPTEG7zVQgQ9AcZ3O1GqrjxE2626rGTcuyEzLzwHEVs7VwPpvCLOjzs04p08C0RCPFGkVbx8nqy8F/0TvQqp3jw9zYg8pXwFvbA/gTxscEI8jRjMugM2SjwLySK8I73hO9kRNT2Dub28CLZluyWN17vMalw89cp5u+mqGzxloi68fBNsvFL+Nbyk3yQ7aRUAPLUH7zwfdpq8vtuTPO3ckbsy3687aWgnPHYxEzuftvE7Ew7rvORkAjyNP5+8vfw/PMx4MzttD108LnGgPE12WburL0a8JORGvJ56kTxnlZQ8WSgsPMM3DTyw65O7OXe7O+L33Dwqr/k8tyOeuiZcabzxYLe86Ikmu6oSDD0KJiO9pLXku8eGPrt4wqK8NnvjvCttnruiMgA9tTKBPKI2vTv5duM8RKJfO8PnZbor7vg7uAOQPARswjyTVRQ8z/T3PNRIqDpoUFy8R0CbvMGUMTy5o5I875MBPMh5PDzpsGQ8EY+MO5DHTT04+N27nMdNvKp0TjvJiC+91uKiu8vrwbxL8uY88by5u83fEr0USS86m/eBvMXP4jxSxlu7FmmbvKL+TLvTXW48w54lum0MmTzozoE6zM5sPK+2srn9h3A8NSZqPA5HnbuRKpQ8fMrRu5mGpTwQNmm881QvPHcWvbyZU+O7quwSPV5R2Dtf4HG8nE2pPFNVYbyxPXS7c4UzvK9Vi7yWy7470fQdvVjFhzzQHQk9WnYwPP1yfrwhrKa8CTNjPHAL6ryy2mO7ZibkvPndZb3MJZC8x1SRPMH6gLyhI+C8wRy8PFCnzTzEf5w7OnYTvJnucroLcH67r9uBvG9eQj0VxPM7uNTbO+nA9TymFOm8Fg6TvC2CFbwa54q8HVWvPEPWKzwuuZa8zQR+uxAKzLyb1US85+I4vIkyHDsEAOU8SaYkOqjdJTodYte8j4cOvfgtCzsS3h09zLq8vMq1jLyUlVm89Hc5PJ+HZzz11hk8IIDlvLroU7z7Lyu7g7NGPEbwDT0Bjqc5kWkpu6AerTwc2Q6826kpPHoE/DxFNBu8R3TJu0bF4bz0EIo7qktwu+csCrx2u8U4h0t2O0HvODyJEO87nzfsuzyaBLoaxSy8Y1SPO9veGT1I7x89UlKyPEcjxTyiBlC8ry4yO2CSaDuJw5a8QWbJPCU3fzxIjq27weyqO8s9PzwMXMg7PMrWvMZvH70wKBO8HFGcuz1Sp7z07/m8WjdYPD8OULyHaLG8LbnPvNmO4DxB+Qs8EWbzPNP6HT1NZ+m8ssuuO45leroTYjy9LRCuvOddKrxk5de8FzIrvCZaSTwfh0Q89X5+PG5F/zlfnZq7KOeqOxjfmjs/jQq9t34EPbtoHz0RNbK8m0b3u6j17jy4IQ28d6ixPDS5nbwRGac8v9IvvGZN77yXdaq7Zs4HPGBNwTzLXRE8oSWSvLLo27zjjlk7JmQfvNFLNT338ZU8Jm8yvBDl4Tv+Vim8f4amO1fNUryKrrI7BpKYOzrZg7v6bi87oF2yvJ+GB72moSc7IGJHvBTgRjwThU68uxxIPIFHOL02T0k8H4CmvNEjxrxYXZC8lUA3vV4zgbtgzTq9RNCCPAaNOr0eKCU912+RvER8EzszkaG8cCOxvOZs+jywEHC8Kdk5uwmdjLzvbzW8JXGLu5Wxw7kqWN47L+eQPEQdBryO/4I7el+KPAuOPztNG0e8SxbkPAP9OzzFwXi7EF7+u2BCSb05AZW8GvVoPBkIUzyWkTu6jtTSu4TJMLxCtHq8afdzOvmaizof0cq7p9Cnu2HB5rzAUt+87gvwPMKEijuYtV8795VOPCiEr7u+v5i8gErvOoUCJzzPYGY8fHzzPImWTDyTCKs86e4lPUACeDyW/cI5yPwSOrJUzTyAd2E83F0TvBFPAjtWbAe6aUohPQ0R4ztUCuS8EqBjPLN/47xeRfi8fyI4vQ45sToVvH88G+eQvE1HGjyAD568kQoIvXIhWDtYyhk8opaGvLsUrjyPf6K7PdA3PIXSsTy2kJg8dc4JPfsVmTyUAs68bpmPvJI+rTy/reQ7fRujuywWrbpv0/w8vw2QPI+tQ7zjyLI8Xd4AvTuJZDpwmNO7CpP8N+HPDjurVMA7KyQlPL5R0DxDuoi6VDRHPHlEnLuqw8m8uT1TO9+Fi7yKZGo8S9HGPEZPX7xZnMa8igKlOwzDsjpDpBw7ZeMavBlUCr1gKsq8iflwPPajfjvcppC8L2odvO+ba7wUM/08VhNYPAA52bsHxhG7xriJvEpQWb0oeUK8xLzBuwb8szwwZj28i8wlPH6Br7xhMWq7eduROxI9Or0BfKa8k/WnPBOEnDzEBBQ80Y3bPE5237xkzQy9H+59O5TwSrxMaPO7t85gO91DkDwMHXC81Mfdu6b+krvlWKa861SrPJV5grvx8pe8nPELPC9SDTtHzYI83oAwvF+ejTzSscE8SBR1vFMTELwmhx48MclXPBOmFz3jOrU72IuyvAXsOL0fMQE6EL4du52TE728zT28CLCBvIzPiL1oe1w8w8OZvAV+XbzgE0S81dZAPC7jmTwoV1a8szg7vWLOijyl82G8F6SFvF/DADsZNyA72dAxvLiOWTw7rwi8VImQuZdPwTuAsfq7BpUXPa0etry7Ecw8fKmHPPI/ErrSG0a9cCxHuvuuQTzZKeU7cadNPLgMlTx4J7q75fXKPD8f8bvBZQm9CzrsO27gQjyRMjQ8HfmBPCb4Nrw2Fxc87FSgOwjIx7s3rKy8Qmc7uJBGvjyWM428FO0vPJfcobzD2A29AkzhPBwADTu5yGK8UKGBvLkIwbxzcZS8VR0BvAITAT1smQS86H0WvU3o7zuBRIG8uhsWPPpmyryG1G87iyBuOxiwoLyLpI68xSG4vITpEDwTmp48M70dPGzBCz0iTDm8HuD4u+r2nruVA547U9uVvK/2Gr1RDCI7kV1MvGjNp7zLDua762g0PAPCBT2NG1E8zLuGO9N2vTylrlK8Bp3iOxvyrTvnZma8APWMO0kzF7oBo0E8EKb0PPu7MrywndY7NJnBPCBN+byTBsG83jHSuFrldzxTHRQ9JN60O80uXDyR/dG7P5qmu3aW1DzSCS88muLwvA8xmrxl/R874ywmvKZ38btdYMC8PhOKOw1XFryksmO8td6KvJixJbxtxum76oGpu4ktA7w3sU88UmHRPAfL6TwLnPM8jTYYPEQm3zv+aBG8XDW6PJJ4jbzAKeO8kt22OlCAQTxDLg29avkrPf2UhTxoKdi6sXakuxo3tLwyAJM5xT3yO+iM7ryeNxg9tSzoOyv6SL18uc+78flHPO/n0Ly5ERg7zakKvcW56TnUXrS7UYAEvUC3t7xy6zq8GBSUu7bnKDwRtye9d9uiu+B0ErpA0N46EVgRvWEAlrzem8Q7oOYQPXTyALsurhK8LLEKPApM2rylnLQ8owQDvfBOO7yilGG8WFcVO0TTKD1TOOM8/ni3PO8nFr1dmhs9N5cNvccBbTtm3L48CTwwPLJyErx9qNq8QVIvOiKbP72UHx+871JBvLwMpjyIsAS9qYOHO4rBHj3dRri8j9ycPJvuA7kiYpG8SR+Mu9eS87wNFdk7kGaSvChK8jzDP+48eTS1O2eL67sY7xq8YNWLPBJtXLxaIC080Ka3vKCqojyadK+7FhmKPGnSuzsyZ9A8L5mVPFcxBbyOxoY8eLF6PPklfDsfdaI8FUx4PH/7s7xcqTW8C68ivL3vUTyD+Ci8WgHSvEVQ97wHgEs8R+V3vKv5pLz/0H27UMfDO1s7QTxEtoe8KBkBvbqtmLxKvYM8RfYxPAezATz+1AI8NlKRvLk7ajwAx6a7k1rqO7MHVTz09WW7ZeWpOzV5qTuMWDu771A9O/V4lDyVkt28vNIcuyvP/7wqFAe6sv67PB4BbLymKA0862lUPffAyznJbV28sBa7OrUASDyUdY08WAq5PJBn/bxJWDA8L9xKvDN55LyVQYa8I05SvLmdmryW5nM6oxiEPKsfe7sqfN88yGNDPIrCsDpZI387CTXbvCvy1DyWD5G8oIUoO5Nd1zwpoDk8rYyXOhj7gDxmewS9D9DAO2k43jwmYYK8Vy33O5FBC71bIm08bbuHO84mBjxs++W8Aj7wvJPz5ry48um8S+J1PJVBED1qeaq78vEKvGlsXrr4eYc8V9s0PJdJjbtKXQS9Wy+YvF4ff7uOo6o62N4ovfV3tTzI1+O8shAJve6p8bp38fM8k8UvvMZHzDw1hTK7k/VBOl6ZkDwdBjy7C+YHvRgLtzxN1Vy8/daRO/LtuTy3lqU7k3gMvNpGjLzaT+O8cQNcvHd3lTyxI4g8SrH0PO6+YzxZmgy8a1H9u7ywaTzxYIa8W3yTPJSrCrs3fH28+6qPOjxz1jzpUEo8/SV/PH6UnrwOo7q8Es/XOogNq7x02l876Elru1BZjTyfLoq8rPhIPXElFrz42i67lDinulwsDrvMvMA8xGswPGeFuTuNAx09MEMPPPvgFz2e15M7mi0NvVxbGD3AFKG8hLZ4u5DEH7zoWJY8fI2nPE/Zi7xPmpI8PyMgPdy1iTyuI7s8x9fTPJk5qjsNng89iqKiPEDt2ztrg/k7pqEeOwv/D7x1e9W7bXpKvAos77ufmtc8q6W4vGLg/jt7tuG73R/lvE4UXzzeXw+8gSSZukB9Cj3gIpg8bLiKu35OmTxEz1O8O6rhO9838zwiTbc8XpkDvDO/x7wEPrC7uZ4ivIxQ5blgXa+7cD7JOoXfwbyf/xE8cn3KvI5L2jsLIKC8xawPPLo6abzys4I8G44rPV523zwQHpg7DyVevF6bmDsQyZg8DSCLu+3ugrysZk88vYm0O1Y8irtH3aK7jQl0PDL1WbmS/Hc71gXwO02vATuavfc8dPChvCcjCzw39wQ8LtKWPFz2+7rbzYw8zR3TPA4CR7yeW4271Q1hPBp6EjoVcVm8HqedO26YfbwTqU07ciVGvL191zsWolO8IGlMvJFroLsf1RG9IAsMvJH90DxhohW8gBnlPAb+oTpb5Le75D60ujL8iLxVmpm80t0+vAHu7Lsoj+i8g6gDPQy0wrxpABM7BF/0uqNMFbzHj5W6pta2O88QU7wUTxc8VoEDPTVlszfQneo8dFUhvI/+ZTzREfC7yzVZvBzqt7xgbce7x2h2PNPjvzyM7G07KA+duyPcmTysLgq8mTzEvEx7tTzttzA8HXIRvQ9FbbvJOaO83GrYPEtf6rsvrGS7fng8vCjZvTs6kqK7WdPDOzidIDxzpQw8X0rhu+XPVzz+UH28kvrBPKsBC7yFdZK8+49Yu7G6ALwEN9E7F4pPuxlRxLxCPaQ8VlnDOvGJDDwqWCk9cCJ9u7WyhjohM7+81xwPvTVAmLvHDvO6KXGYu4H3czpre3i7k+qxvPECRLvvHF07tbI3PFu3i7zO5Xs7a+TwuQ== 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 2 + total_tokens: 2 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '39568' + 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. + + Chunk IDs come from two places: + - The `chunk_id` field on `search` / `await search(...)` results + - The `chunk_ids` field on `items.jsonl` rows (when you ground via direct file reads) + + Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs. + + Every answer that uses search or file-read evidence must be backed by `cite`. + + ## 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 and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora. + + ### 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. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file. + + Each row carries: + - `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results + - `label`: item type — one of `"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 + - `chunk_ids`: chunks that contain this item — pass to `cite()` to ground an answer that read this item directly + - `heading_level`: H-level for `section_header` rows; `0` on non-header rows + + ### toc.json + Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `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`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index. + + ## 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. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents. + 5. 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 list all different document element types mentioned. Likely the document element types are the labels defined in items.jsonl: "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote". But there might be other types in other documents. The question is to search for content about document element types or labels. So we need to search the knowledge base for references to "document element types" or "labels" or "element types" or "document element types" etc. Then gather all distinct types mentioned. + + We need to produce a list of all different document element types mentioned. We need to cite the chunk IDs that support each type. We need to gather all types across the corpus. The items.jsonl labels are a set of known types. But there might be other types like "section_header" etc. The question might be expecting the list of types from the items.jsonl. But the question says "Search for content about document element types or labels." That might refer to the items.jsonl labels. So we need to search for "label" or "document element types" across the knowledge base. But we need to list all different document element types mentioned. So we need to find all distinct labels across all documents. That might be the same set as the items.jsonl label values. But there might be other labels like "annotation" etc. We need to check. + + We should search for "label" or "document element types" or "element type" or "document element type" or "document element types" or "document element label" etc. But we need to find all types. We can search for "label:" or "label" or "type:" or "element type" etc. But we need to be careful to capture all distinct types. + + We can use search with query "label" maybe. But that might return many results. We can also search for "label:" or "label" but that might return many. We can search for "label" and "document" maybe. But we can also search for "label" and "items.jsonl" but we don't have that. + + Alternatively, we can read all items.jsonl files across all documents and extract the label field. That would give us all types. That might be more reliable. But the question says "Search for content about document element types or labels." So we might need to search the knowledge base for references to "document element types" or "labels" and then list them. But we can also read items.jsonl to get the labels. But the question might be expecting the list of types from items.jsonl. But we need to provide citations. We can cite the chunk IDs that contain the labels. But if we read items.jsonl, we can get the labels but we need to cite the chunk IDs that contain those labels. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide citations for each type. We can gather chunk IDs for each type. But we might need to gather many chunk IDs. But we can just cite a few chunk IDs that mention each type. But we need to ensure we cite at least one chunk ID that contains the type. We can gather all types and then cite a representative chunk ID for each type. But we need to ensure we cite the chunk IDs that mention the type. We can use search to find chunk IDs that contain the type names. But we might need to gather all types. + + We can use execute_code to list all items.jsonl across all documents and extract the label values. Then we can produce a list of unique labels. Then we can search for each label to get chunk IDs that mention them. But we can also use the items.jsonl chunk IDs themselves. But we need to cite chunk IDs that ground the answer. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide a list of chunk IDs. But we might need to provide chunk IDs for each type. But we can just provide a single chunk ID that contains each type. But we need to ensure that the chunk ID indeed contains the type. We can search for each type to get a chunk ID. But we can also use the items.jsonl chunk IDs. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we can use them. + + We need to produce a final answer: a list of all different document element types mentioned. We need to cite chunk IDs that support each type. We need to call cite before writing the final answer. We need to gather chunk IDs for each type. + + We need to decide how to gather the types. We can use execute_code to read all items.jsonl across all documents and extract unique labels. Then we can produce a list of labels. Then we can search for each label to get chunk IDs. But we might also use the items.jsonl chunk IDs directly. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we need to confirm. + + We can do the following: + + 1. Use execute_code to list all documents and read items.jsonl for each document. For each item, collect the label. Build a set of unique labels. + + 2. Then for each label, we can search for that label to get chunk IDs. But we might also use the items.jsonl chunk IDs. But we might need to use search to get chunk IDs that mention the label. But we can also use the items.jsonl chunk IDs. But we need to ensure we have chunk IDs that mention the label. We can use the items.jsonl chunk IDs. But we need to confirm that the items.jsonl chunk IDs are indeed chunk IDs that we can cite. The items.jsonl chunk IDs are the chunk IDs for each item. But we need to confirm that the items.jsonl chunk IDs are the same as the chunk IDs we can cite. The items.jsonl chunk IDs are part of the items.jsonl rows. They are used for cross-referencing. They are chunk IDs that we can cite. So we can use them. + + Thus we can gather a mapping from label to a sample chunk ID that contains that label. Then we can call cite with the list of chunk IDs for all labels. Then we can produce the answer. + + We need to be careful: The items.jsonl chunk IDs are unique across the entire corpus. They are used for cross-referencing. So we can gather them. + + We need to produce the answer: "section_header, text, table, list_item, caption, formula, picture, code, footnote" maybe. But there might be other labels like "annotation" or "definition" etc. But we need to check. + + We need to search for all labels. Let's do that. + + We can use execute_code to list all documents and read items.jsonl. Let's do that. + + We'll write code to: + + - import Path, json + - iterate over /documents + - for each doc, read items.jsonl + - parse each line as JSON + - collect label + - also collect chunk_id for each label + - produce a mapping from label to list of chunk_ids + - then produce a set of labels + - then produce a mapping from label to a sample chunk_id + + We need to output the mapping. We'll use print(). + + But we need to be careful: The items.jsonl might be large. But we can handle it. + + We need to ensure we use Path.read_text() and splitlines. + + We'll do: + + ``` + from pathlib import Path + import json + + label_to_chunk = {} + for doc_dir in Path('/documents').iterdir(): + items_file = doc_dir / 'items.jsonl' + if not items_file.exists(): + continue + for line in items_file.read_text().splitlines(): + if not line.strip(): + continue + item = json.loads(line) + label = item.get('label') + chunk_id = item.get('chunk_ids', [None])[0] if item.get('chunk_ids') else None + if label and chunk_id: + if label not in label_to_chunk: + label_to_chunk[label] = chunk_id + ``` + + But we need to handle chunk_ids: It might be a list of chunk IDs. We can take the first chunk ID. + + But we need to confirm that each item has chunk_ids. It might have multiple chunk_ids. But we can pick the first. + + Then we can produce a list of labels and chunk IDs. + + We need to output them in a format that we can parse. + + We can print a JSON mapping or just print each label and chunk id. + + We need to capture the output. + + We need to call execute_code with this code. + + Let's do that. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nlabel_to_chunk = {}\nfor doc_dir in Path(''/documents'').iterdir():\n items_file + = doc_dir / ''items.jsonl''\n if not items_file.exists():\n continue\n for line in items_file.read_text().splitlines():\n if + not line.strip():\n continue\n try:\n item = json.loads(line)\n except Exception + as e:\n continue\n label = item.get(''label'')\n chunk_ids = item.get(''chunk_ids'')\n if + label and chunk_ids:\n # take first chunk id\n chunk_id = chunk_ids[0]\n if + label not in label_to_chunk:\n label_to_chunk[label] = chunk_id\n# Print mapping\nprint(json.dumps(label_to_chunk, + indent=2))\n"}' + name: execute_code + id: call_qd0d5ug0 + type: function + - content: | + { + "text": "713678f9-980e-47ed-9e1c-e6b533231d57", + "caption": "713678f9-980e-47ed-9e1c-e6b533231d57", + "footnote": "73a2443c-3c25-4fbb-a6d4-0dec371ef104" + } + role: tool + tool_call_id: call_qd0d5ug0 + - content: null + reasoning: |- + The output shows only three labels: "text", "caption", "footnote". That seems incomplete. Maybe the items.jsonl files only contain these labels? That seems unlikely. Maybe the items.jsonl files are not present or empty. Let's check the structure. + + We need to inspect the /documents directory. Let's list the directories. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\n# List directories\nprint([d.name for d in Path(''/documents'').iterdir()])\n"}' + name: execute_code + id: call_b60xwvbx + type: function + - content: | + ['40fa427f-7d9c-499f-9985-ce7f51d24db4'] + role: tool + tool_call_id: call_b60xwvbx + - content: null + reasoning: 'Only one document directory: ''40fa427f-7d9c-499f-9985-ce7f51d24db4''. Let''s inspect its contents.' + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\npath = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4'')\nprint(''Files:'', + [f.name for f in path.iterdir()])\nprint(''metadata:'', json.loads((path/''metadata.json'').read_text()))\n"}' + name: execute_code + id: call_rt6l2me5 + type: function + - content: | + Files: ['metadata.json', 'content.txt', 'items.jsonl', 'toc.json'] + metadata: {'id': '40fa427f-7d9c-499f-9985-ce7f51d24db4', 'title': None, 'uri': 'file:///private/var/folders/4k/n24fyvf11117vpdnpngx290r0000gn/T/pytest-of-ggozad/pytest-106/doclaynet0/page0.pdf', 'created_at': '2026-05-25 12:47:13.296904'} + role: tool + tool_call_id: call_rt6l2me5 + - content: null + reasoning: The metadata shows title None. The file is a PDF. The items.jsonl might be large. Let's inspect the first + few lines. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nitems_file = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl'')\nprint(''Size:'', + items_file.stat().st_size)\nprint(''First 20 lines:'')\nfor i, line in enumerate(items_file.read_text().splitlines()[:20]):\n print(i, + line)\n"}' + name: execute_code + id: call_nvweqb8s + type: function + - content: | + Size: 6660 + First 20 lines: + 0 {"self_ref": "#/texts/1", "label": "section_header", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 1 {"self_ref": "#/texts/2", "label": "text", "text": "Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 2 {"self_ref": "#/texts/3", "label": "text", "text": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 3 {"self_ref": "#/texts/4", "label": "text", "text": "Ahmed S. Nassar IBM Research", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 4 {"self_ref": "#/texts/5", "label": "text", "text": "Rueschlikon, Switzerland ahn@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 5 {"self_ref": "#/texts/6", "label": "text", "text": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 6 {"self_ref": "#/texts/7", "label": "text", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 7 {"self_ref": "#/pictures/0", "label": "picture", "text": "Figure 1: Four examples of complex page layouts across different document categories", "page_numbers": [1], "heading_level": 0, "chunk_ids": []} + 8 {"self_ref": "#/texts/8", "label": "caption", "text": "Figure 1: Four examples of complex page layouts across different document categories", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 9 {"self_ref": "#/texts/312", "label": "section_header", "text": "KEYWORDS", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 10 {"self_ref": "#/texts/313", "label": "text", "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["92e8dd8b-171e-4933-a0f1-16f5ce6ac545"]} + 11 {"self_ref": "#/texts/314", "label": "section_header", "text": "ACMReference Format:", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 12 {"self_ref": "#/texts/315", "label": "text", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["93ad7de1-8b78-4585-9865-01058d67019d"]} + 13 {"self_ref": "#/texts/316", "label": "section_header", "text": "ABSTRACT", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 14 {"self_ref": "#/texts/317", "label": "text", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["c0d1d300-462b-445b-8421-6a09606a2c9b", "912809c6-deb1-4a1f-91e8-ef3eadfeb852"]} + 15 {"self_ref": "#/texts/318", "label": "section_header", "text": "CCS CONCEPTS", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 16 {"self_ref": "#/texts/319", "label": "text", "text": "· Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ;", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 17 {"self_ref": "#/texts/320", "label": "text", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 18 {"self_ref": "#/texts/321", "label": "text", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 19 {"self_ref": "#/texts/322", "label": "footnote", "text": "© 2022 Copyright held by the owner/author(s).", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + role: tool + tool_call_id: call_nvweqb8s + - content: null + reasoning: Scrolling further might reveal more labels. Let's inspect the rest of the file. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nitems_file = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl'')\nlines + = items_file.read_text().splitlines()\nprint(''Total lines:'', len(lines))\n# Find unique labels\nlabels = set()\nfor + line in lines:\n if not line.strip():\n continue\n item = json.loads(line)\n labels.add(item.get(''label''))\nprint(''Unique + labels:'', labels)\n# Show some lines with each label\nfor label in labels:\n print(''---'', label)\n for + line in lines:\n item = json.loads(line)\n if item.get(''label'') == label:\n print(item)\n break\n"}' + name: execute_code + id: call_c2sddzkn + type: function + - content: | + Total lines: 22 + Unique labels: {'section_header', 'text', 'picture', 'caption', 'footnote'} + --- section_header + {'self_ref': '#/texts/1', 'label': 'section_header', 'text': 'DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis', 'page_numbers': [1], 'heading_level': 1, 'chunk_ids': []} + --- text + {'self_ref': '#/texts/2', 'label': 'text', 'text': 'Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['713678f9-980e-47ed-9e1c-e6b533231d57']} + --- picture + {'self_ref': '#/pictures/0', 'label': 'picture', 'text': 'Figure 1: Four examples of complex page layouts across different document categories', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': []} + --- caption + {'self_ref': '#/texts/8', 'label': 'caption', 'text': 'Figure 1: Four examples of complex page layouts across different document categories', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['713678f9-980e-47ed-9e1c-e6b533231d57']} + --- footnote + {'self_ref': '#/texts/322', 'label': 'footnote', 'text': '© 2022 Copyright held by the owner/author(s).', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['73a2443c-3c25-4fbb-a6d4-0dec371ef104']} + role: tool + tool_call_id: call_c2sddzkn + - content: null + reasoning: |- + So the only labels present in this document are: section_header, text, picture, caption, footnote. There are no other labels like list_item, table, formula, code, etc. So maybe the corpus only contains this one document. But the question might refer to "document element types or labels" across the knowledge base. The knowledge base might contain only this one document. But the question might be generic. But we need to answer based on the knowledge base. So we should list the types present in the items.jsonl of this document. + + Thus the answer: section_header, text, picture, caption, footnote. + + We need to cite chunk IDs that support each type. We can use the chunk IDs from the items.jsonl. For example: + + - section_header: chunk_ids empty? The items for section_header have chunk_ids empty. But we can still cite the chunk ID? Actually chunk_ids empty means no chunk ID. But we can still cite the item itself? But we need to provide chunk IDs. The items.jsonl chunk_ids for section_header are empty. But we need to provide chunk IDs that ground the answer. We can use the chunk IDs from other items that mention the type. But we need to provide chunk IDs that mention the type. But we can use the chunk IDs from the items that have chunk_ids. For example: + + - For "text": chunk_id "713678f9-980e-47ed-9e1c-e6b533231d57" appears in many text items. + + - For "picture": chunk_ids empty. But we can still cite the item itself? But we need chunk IDs. But we can use the chunk ID from the caption item? But the caption item has chunk_id "713678f9-980e-47ed-9e1c-e6b533231d57". But that is a caption, not a picture. But we can still cite that chunk ID to support the presence of a picture? But the picture item has chunk_ids empty. But we can still cite the chunk ID from the caption item to support the presence of a picture? But the caption item is a separate label. But we might need to cite a chunk ID that mentions the picture label. But we don't have one. But we can still cite the chunk ID from the picture item? But the picture item has chunk_ids empty. But we can still cite the chunk ID from the caption item? But that might not be correct. We need to provide chunk IDs that ground the answer. The answer is "section_header, text, picture, caption, footnote". We need to provide citations for each type. But we can provide a single chunk ID that covers multiple types? But we need to provide chunk IDs that ground each type. But we can provide a chunk ID that contains the text of the item that has the label. For example, we can provide the chunk ID "713678f9-980e-47ed-9e1c-e6b533231d57" for the text items. But we also need a chunk ID for the picture item. But the picture item has no chunk ID. But we can still cite the chunk ID from the caption item? But that might not be correct. But we can also cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But that might be okay because the caption item is associated with the picture. But the question is about "document element types or labels". So we need to list the types. We can cite chunk IDs that mention each type. For the picture type, we can cite the chunk ID from the caption item? But that might not be ideal. But we can also cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But maybe we can cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But that might be okay. But we need to ensure we provide a chunk ID that is present in the items.jsonl. The caption item has chunk_id "713678f9-980e-47ed-9e1c-e6b533231d57". That chunk ID appears in many text items. But we can still cite it to support the presence of a caption. But for picture, we might need a chunk ID that mentions the picture label. But we don't have a chunk ID. But we can still cite the chunk ID from the caption item? But that might not mention the picture label. But the caption item has label "caption". But we need to support the picture type. But we can still cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But that might not mention the picture label. But we can still cite the chunk ID from the picture item? But it's empty. But we might need to find a chunk ID that mentions the picture label. But we can search for "picture" in the items.jsonl. But the picture item has no chunk_id. But we can search for "picture" in the content.txt. But that might produce a chunk ID. But the content.txt might have chunk IDs. But we can search for "picture" in the content.txt. But we can use search to find a chunk ID that contains the word "picture". But we need to ensure that the chunk ID is from the content.txt. But we can use search to find the chunk ID that contains the word "picture". But we need to confirm that the chunk ID is present. But we can do that. + + Let's search for "picture" in the knowledge base. + role: assistant + tool_calls: + - function: + arguments: '{"query":"picture","limit":10}' + name: search + id: call_1atkyf5a + type: function + - content: |- + [93ad7de1-8b78-4585-9865-01058d67019d] [rank 1 of 1] + Source: ACMReference Format: > KEYWORDS > CCS CONCEPTS > ABSTRACT > DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + Type: picture + Content: + DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + + Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com + + Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com + + Ahmed S. Nassar IBM Research + + Rueschlikon, Switzerland ahn@zurich.ibm.com + + Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com + + Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com + + Figure 1: Four examples of complex page layouts across different document categories + + Figure 1: Four examples of complex page layouts across different document categories + + KEYWORDS + + PDF document conversion, layout segmentation, object-detection, data set, Machine Learning + + ACMReference Format: + + Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 + + ABSTRACT + + Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. + + CCS CONCEPTS + + · Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ; + + Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). + + KDD '22, August 14-18, 2022, Washington, DC, USA + + ACM ISBN 978-1-4503-9385-0/22/08. + + https://doi.org/10.1145/3534678.3539043 + role: tool + tool_call_id: call_1atkyf5a + 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. + + Accepts chunk_ids from search results AND from direct file reads + (items.jsonl, toc.json). Verbatim copies only — chunk_ids that + don't exist in the database trigger a retry. + name: cite + parameters: + additionalProperties: false + properties: + chunk_ids: + description: List of chunk_id values from search results or VFS reads. + 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-460","object":"chat.completion.chunk","created":1779702559,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"The"},"finish_reason":null}]} + + data: {"id":"chatcmpl-460","object":"chat.completion.chunk","created":1779702559,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" search"},"finish_reason":null}]} + + data: 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rp9luDmZSLwQVyS8qs8RPBf0CDgDlPA8dXimPcYckrrs6NM84QCEPBY1B72vbpa6gWEhusYDAL2bYVo7DJqcvbEUSTwIEAO9YBWYPaLqvLvDtIW8B0HNOypkOT0tJTo77oGTPNU6TjrdZfK8AHOYvdXSAzymJMe7SpUgvB2AT70Xjmk9Um+jPFIkSzsnDT68UZ6Cu/lbGLzOlgy9u1iruxXtZjweKRO9nvysu5BLnTvOWbq79wVuPIEKQTxrT4E8PsZGvMk/dLyna8q6D+V3usY4GL1hyMm8lA8SOw3zWrxoecQ8OMJbu3FjOrw++Dw7cMPGu8OYLjzVxUe9IJA8vDZrPLtOxwC999CCvLparbyJiJ88DodNPJYOG71ojMQ8bLBDvPWE7TtXlye9BY3evPWvEryDI6o8Jf3pucvlkDyOSGg84oBdPFsCuDzLU1g8BXqrPBSJtTvJQJs8ABa7OB+8Er3kWHW6aeqXPBcnaTr1TTC7+FoJPLONijvQ0wm8unElvGd9WrzgMaW7teMhPAj/0LpS6nC8LctVvK2Xj7wwIBq9dxwQvbOV5rydGm88HvsEOyjGgTzhLGY6SqE1vGwSoLyFbeO8xUFevK73d7zyeNy7vSW+PJdoSjv1UA483qvvu+F0SjziHry66vGrPAwNITpmqaq8fbGxuxHIS7yfTm68vGVrPIoL9bmsGpG8xS/+OryfIbywbSA9kQ1NvC8Q5LvyDw88wQUmO4fusTxXOn28yEeCvN/MHbwZUak8h0zuvPzOWb18YLK6/jvnO25EqDzw1qg7F2lMPEA88rzUGbO6Ey2HPEhKLDwdats81dZsO58yhzztzcw7ZOeCPBF91LzgqxM96/iMvIhXFz3TdzI8bYYiO4FPKbzdHD487ommvDYSObtaXB47neZivKfbZTlIQza7pVrOvLYtxrzGUYS8jEahvBuvIbxgjm87mambO203xDyqfSM9A9EqO4dbaTy+MvS79XJwvFpOELvSnYQ8vUyyPGqEsbzIkIa8T2O3upGM/DsSv607GNqAvPbKSLwAumC8ZSHavJ6aSTxb+4M6VQ+7PGxHID1nFIy8g1UgPMjMmTu2w4k6uqFSuxWeijz9S9q8Ka8hPKPYjTxb3Q+7desiuyzO9rtGNaA8/ZfUvCCi27sqVJk8+m2BPdRPHjwTjDc6jxPnOyt2nzv7QSe9v+UivGp8+7szRqS7iilJO7wagLvL/AQ9m8VlPIcVnbwJ3z+81iJ2u8BYXbtbRhw7oNK8uvAb9LpyrIy8WlP9PD5qU7zM5e27BG+4PKYsJLw+55S8rgpAO7UFk7py7yK4IpuNvBmmRLxTh7E7d1e1PO5JrbxL+yM7Bj82O9i4FrxlgtQ5K/brO5qiezzfo+A8a4iAPIrFHrwKHjK8YHYNPNKoUju30gY7dkXuue5rBzquOM86ZpcwPb05vrzb+YC87Ikmu/bAWjy+kzG8f23au3KGIzxBmK089dIBPUZl5btMzY08HIG1vJ7SvrtLRk+8yn++PGJePbxRwEs8gnrIu8ipC7zM9Ow8cFjfu5K7ajz/LgS7T4ewO25qC730O7G7+S0kvDrsx7uCgoC8f7Leuzd8NTxTc0u7KdGOvDsjBLwrjG27pthtu3uuEbvcI9M8if8zvRLw+DqevJm8mwwhO3DER7t6Ike8sAyQvLdsArxFNnA73r39vPpK7bwN8Cy8CKOrvV1WMb3spnm831W0u5zV2TwYXDA9jpNZu0O+pDyNsIc8MbkOvE368jzaSt27dxWZvEBdnrvYEAm9RhOuOdv5MD1MD6s86QeFPLfbCb02XCC8xpepuoBjCrzvXIC9YAA+uj+SdDzDvdS7+t2IvF6Z67yvG/G81pUkvVZ3Er04W9C6RoIcvAyShzsp5Km8MaMtPCq8RT27QSG8HHOdvLHn6Lrr9oo8beCuO84Gr7y0MRs7AQebvKEMNz12AMK5g1fsvJwGqDpYX4E8rdZNPHetLbyKbtG6pXQmvEGy4rs5Riq8vXOAukOWiTyAp5Y8IId7vJmUubw1uJE8pCGGvKvfMDwYNYy8en7tO43OJj1oFpE6rZ/HvG8kZLxZBUq7r0r0O/NzVzwkpfo7cS8qPO3aTTxBRzg9O3sGvexOhDzgcIi8zMl+vEtzrLtK85S71OdXvLRltLzKPj0995XkuQA50bzZbg89pqxfuy1GujvMT5K8W2P9vI2cNzz5GHE8kQQPvfU8TzvFhku7ZZ4JPHg5pbzOvyE7HJPAudfCmbtcYfs6bJXNu03dLDxyd6+8H+25u/jIpzzylEI9J4VBu6BxRz39nGM7jf+yPITu1bubGqu7WueSu7N+07wxtA07HDDxvMPcADmODPo8HAsCvcZqLrz/Ij48u2T+upPVlbyWbIC5PFtcvEjrqryFUxg8zYelusWeLzzak7m7Qnaxuw8KBL12HdG80EwKO53kub31M2q6bxCWPC8Y37xvo5W8JZQCvQck0DudysQ7aL41POb27TyLfgC8R4mQvJJyYLw16+Y7U1DZO/lnZTzPFoK8p3SnOx6ViTz2ItU7unlLPeKQ9Txa3yg8AI2UPCh++juYzPo6QT6JOxvxETwfGCQ9xWefOmWXrDzoMIy6y53aunjzcLyU2fU80ZM7PLrTijuR75W8Kyh0POypIjwawAq9cODPPIJdFLwtGC06obfNvKEpjjw8bfo78ATwvBfFPTzOtxg8sjjIPE49lDsn+eO8B9wXvQR79zxMLvS7ahFFvaZDXDwzMrU8+GNuvOdRurxgBTk8mQAGPUzg5Ly088y8EvVWPBxXMTz4+LC8g41PO6tEkTxr2HU8PthVubpCvDvgxbc8Up/IuzOnpDtNJ7Y7OEMPPdRaDDwrRUa87S6hvF1NJzx7AgU9c1fHu0wfrTxQBa68L94gvMoM6ruOfRS9UMbLPL4RPjzXlde8WbT7PKvK8bxAkzO8xD0LPa+3JDwNJKw8BYbCupBQdLtYx5e8dRigO8l2ijwOJMA8luvuuyyUo7yxe4O8m0gpvV7m2LuW2Cu6wRSRvApoVjyq9bK4IvzdPFhOmDsGs5W8sFZKu4SYY7yETxq9cO2Nu9qWsDx83Mg7OzZ1O+5TxLxL9M67qt9mPKfUEL37yKg7j0APPW8YczzKQwK9h56vuiKnpzw7yQW7NJ9iPIfIf7x4vwE8I4FlvEoEVbx1v3K8SgyXOwgUQr2QL768dxGHu/vE2DsHLw88WDfAu2nNmjyZ66m8KYXdvBX65zvhu9q8dwy7u0FYqDwnBxQ8xManOrWCjbxvBdi8yPyavFGcq7yked27ON91vJRlEr2r8jK7MPN1vNPZYL1x0NS6sCWavE4/qzt/tgO9T/F0PK3tGbzFAhU9R5wlPG14dTu5+pC8piBMveYEBD3msAG5OaNEvS/lhzyopw09g9tBPQzbODxk4hk8rNnpvJUA6bwgY8y8xTYQO95xyTydXRI8LU0sPIxDnDuaZmw8ncCgvBxP7bso76o8F4fIPEVUXDzxa8E8qiC6vGpg6rplW+07/S/Iux3ZGb0yQiE6ctfpvK3u3ryJEUs85Az6PAQEiTvNhaw8CPDCvIS4jruE0Cg8NOC+vC0fAbsysO+7a9D0u+3UnLz7RFI8nlOEO+mL5zuxAVm8jacnPKjHc7uvXKQ8YFcJvEY3hTw6xQs8eQatOyUX97zrogc96lPgPCerFztSKBM67AqWPF6rHT3K0BC8pq9+O82K9zuSLH08CbCuO/oLnzwtFJo8E0gNPZJimDy8MD884zftu4nX3zyj2968sohCvaeahrzW12e8i1mgu1UIebzV3uC8W8c6PLPeELzpihG86Y8qPRrHwjuM0eK8wHeKvE93sLwM41g8bXrWOxz1+bp9Po88YLXNPO6z7DwCrAw8+4QBvGF6uTzzAjO9//h1PGlJczlv0K87wEDPvB7/Q7xrsB+8Ss3GvFkxET0BZL88yr+bu1bEPrzl1vq8CAqVvIbKojzvyi28rIASO8zFGTxeiGc8qFAHvXsxOrzpHqS7uLFNPA+hDDx6syA8nuH0PFtabrxMWn87mNK5PNziPrvvTLq8if09O7nYmrw1E767qs2SPOeKujyzzui7laaUPA3z4rskiwU7O99PPUHYirwp17G80hC6u9b3tTzTZ3w8T0jfPNVHEr2x1008DKeFPFjE6Lw1N1w8VQtGvJrBAb0Ar6u7ocTcvG3T4jva9w87V6yxvA1dczzLB+g8006OO9t6ezyeL648Dvf5vDsxODzs4hW8IYkbvEqWAjpGhRs9n+Efu4P66Dt86ou8Q5E3PFEgI7yNigm8wemQPK9AHb0dcYK8OW9FvCraMbtyQUK8bjLKO0HceDyH1Bi7pw6ru1zJuzwHWJw8XA75On9jqLx/oQS8YkZTvCW71zwanvW7OYARu3F7fDzdaBi8nWorusmJGrxB0xC738Xsuyx0GTw4DZ881ZcOvXVJm7w2AsS8N0wWvLPFKzwAFd88jTwYvTcjmby3b7Y8VWplPGy3SLwtmTy8ezXSPEu0yDymKoE93IC0OtrKTDwXOzg8yO9UN8DROD1F4ha7eW/9O2BZybv70qQ85JhFvOo91bxpq/a8Z95nOTNJsrxMLbm7enHvO1sjZjth42K9kuuNO8sd5zzuhD+5RFEIvA0gRjymeSI8OUR7vOQQcbxgdZy7c2dFPFZsFj2DDLG89AgDPfpALj27Nae7lzHBu/9khbykHo886XX4vAHwYru/2Fu8YhdWPHOMwzzE6wg9IonMPHRNKrumPa+7tq8GvQYiHT0Q2rG8waXIPDX3xDyInF+86EUhulW+FjywPYw8ZwlevD1icryYdfM7gQ0Pu5yUNLxeozm9BxTzu0L5ELwlCzG9miyzPCVFK7wARSO8Ud9zvdX+NrycFQg9c0G7vJrANztycVC8LvZ6PacPh7yOCyi8E8B7PNUqNL0cYxo8a0ycu10PhzwX5du8upQQvP8JjrwfcUS9OvP0PDrj0TwQEbY8I8wfu2dNQ7y4ZKW84Ny2PCDrUDy3Sgs7gDNCu8oaxDwrx987GcJgPAaYNjvjjEQ86MwPvK3GF7vfQeM6X0UjvB0MZbsVRvu7+h/Qu9GLRTz/g4q7V7RAvFIxELykmfy8Q22Nu39u6jzsK4a8huidvJY6uLxCA5o89qqlPOB4ODxopSs8CCYEujsJ27z5ygO75zzaPGf/qTyM6QY9AqgqPWq0ejsZH5O8dY6JPKeOSrycs7C8MpnyPLFi27zP4wY8LXtgvMLLYjzsTDq91z6gNvAZdjuRoRC6NpOoPK0MQDxeMh49/URJvDtXZzxT1HQ8C6yZPJk2trw4Mvo7meAxPFic+bwLB548VSGGvOIqvzyTwRc8GNjhPNJ+AL19RGk80VZYu/1/jruiwI68ADCdvJCAkjzaeAo6O+Z7PAJgiLsJ26673OrKOyD5KjtRPmg8TLF8PKuvszzivgC9oMp+u5ddNDy0jRs8PbmdOx7MAD3HyBS4GgaYvADWDzzVGsC6otucPEpIjrs62Ms8Wi6cPGD4vrsJagG8J05VvHR99byR81q6q+6qPE/whzsYTtk7ddTMO58wDbt2wVO8Vyz1PKoRBz3vGH+80RCtPI7yuLz56wE8YGeYvAHymbnv4ug7/tXvO2b1FD2C4m682ZxsPCmEhryVmuC7XjzovIcZwDu4Hy685+q2OgdPBT1bNpG8V2jLPCmFwDulcia7sxozPK5vgbve2628YR8UO7qSjbwz9uq8g19Bvdz25jxoqvE5sXtgvNGrujuIaoK83jfzPMKk3zxlC2m8gmXbPKfaeDywVIe8Z55MvYgVDb011Fo9ZyfwuQN8vrwSkZ27RTvAuzZ+LbtW+ls7MrO6ud+HQL3QKBC7ZeqcvBL/27zIKhC7pEsKPUM3wLuX+II8kCZrvL4ABjvx6YE7MdAJPf6SvLve5P+8/MpbvH+2g7qHA+45O2+CO3XdQzyLxs48PziBu/fmrTmggpQ8aZyhPEztxDzOia45eJdMvEKwlTmqkUG8pUwEPYz0PT1Tlr+8+BtNPeIiAbteNMW8P9kzu7CgUTylI308LGeEvKwdULlwBY278y2puwvQOTu8DlU9KQ2CvEqpaj3aqVM8IPmWPJDtwbzkIoA86m2yu0rgKTwmaLi7x9m8O35EjTyWLdU8+78BvZFaRT1xXmi7njWPOzmttLv+xxg8KyvXu/NdK7sGF+q8PCQTPXmjejzO8Da9VPvYvH6sA73Ki8Y8uy8wPKXMbryCfQ48IWm/PHe9AjzBheC7+xsgvKQnrToEwgu8M6zLOz6EcztcXO67yxYWvLcmmztRQlU7CJ2SPNS61rtuSiM9GMl7vH8YKDyrYFG9ox5uOxUKybwEQ0U8ucD7u2yMEb3f0wK74ILOO4esE704j++7TMmrvI/zBLyfyk48vu1JO3D3mzztNx88y0VRPYzLyDosWRc8sEgku+XsIT11Ft6866zHvGczBz3PCoM741JdPKqkqjuR1AS9itwbuyfYH7qlI+a8KdDou9rWP7yMQ6q74kKoPN6i9TvsmfQ79tUGPVrUujyUFmU8vnAxPZWeGL0bXck87SNwvCVGHrxLJos8VlVLu31gKL1gndY7n/BMPF6ryDylHa88inLxPARVNbzzALQ7DC7PO1+4t7ygH9W8GjERvDg9Sb2XmJe8Tq1VPEnPz7sAGmY8NoO5u1HPYjvsidM8pjmzvHrJdjvmtsy8OA0PvHKDqjwRcx86EYtlPCGnzrov78c8j06KOwHJizwG9AE9feSWvG4GLD3s1Ou8jGsivLbsKL1cM4C88HO1PAJoxbyB5BC8knakPFgFBTuCwwa7ozFTPAJXtzrIxca8g5//PMZEjbqtG+G87wvwPFoTNLzyUzW8XsnIvMQSuzyl3ni83TUmPFTqEryzPkW8AdgCOxxMcbrnr3i86elTPEuyjztppbW6i27UvPRYIj30FY88BSmvvEU0LzwXb2c7g67wvH9elbtizKU8A321POoyRbzXt/e8LbafvMZG/zupraw7r/YhPBGCKby/Dba8xNerO9z/Bj2nxQw7qY0PvGeKAb0aqzM89XIwPKRRS7y0ANq6M89su8PBizy4Dl88zgOSOxWeJryf17o8eeP+PEHaErzDlhy98TYnvYKxsLy1x+O8h6uTOzRnDLzv6ye9eByLPLHDgbs6nEm9i0l/u+yCp7woSN07828SPSua6DvoyFc8Xk+yPO05Ibp+Joe8damVPP0ztrtQgrW86mf9PFmv+7qAf++8arayOyRcUDzVGwq8cFMtu0AhAj1AJV07rKbNPBoZnbxSHqO80wkEPaXJPDw14p67qo8eO0GonLw878G89awUPA+AJb1nYqw8PTi6u/v8Rj2F4Yo8KEZcvKMD+Tok6jc72ZMFPRodxjssFsW87UUPu+6UPz2q3QE8II27vP9POz2P4BS9QzcGvUfTqDxuXpE8tUTfPF6VwLylTOe6NzURvPt7qLpHehW8zMjAu6d0Frx1Oba7/ngsvPcHlTxrdp28bpvjvGi5FL0k6BO95iPYO7AfprzUnDC8KI8evEfUwDxiQiW89PBxOhL9lrzoG5y8qglfOxU5Xjw+xGY8DydKu2FAvbtJwZ88A7qNvMIJDbyDGas7DheWu86O5Dwmq5A89HYzvE1PNT1ltCg6g2zhOxZZRLwhwx+8qz+Ku2521jtU7h68yhOzPEnv6TsSFN+7Z0SmuaFYdLxDF3k774YaPPgl6LxaezM7duGpuxLKKjufF4K8DWRoPHS8gzzLhiW7dJPAO/z4PrwJ9EU7aAG7PLXobrzceqm7ZdGavEuIYbwepIC8dOVJvSmx7jwHyuS7HKlau3FvxLwvaRE8+7DnO5711bzjaFw8jKe3vOgXlDzCDHs8xgtNPPd0ljwknMi8h1tNvKYXo7vm85q8F6HcO3TsGbyJR4A7juCPvARkpzz9Jhm9OkM5Pa1dhTxGxBG9jmvavAAjT7txKoG8qsG4uxGRyLstuZO72qavOwJZrjtocBi9laSlO1rAObwQwRQ8XnIUPBya1bxQ6Ee8JQIHPF+b3Txd5V673EY6PRdH2jvcPNG7OotKORHk9DzfPAm7zfWfvK70m7sgRyW8H4aVPJiFZbwcNpy7e8DUPBtv7jzDbw08rNN9PKVpCjzNf2I8Sas0O4D6y7xZ0vI6EnZdvOU107xDTHg8xQYLvC4jQjxegaS87Y4ePBTluLtHowY9AkfVvEPKEjzDygI77sRdO5niDLtwkFA8vl8dvRCIhDzEbOu62j23vE4CBD2pRTY9hFYAvBHJpLweIry82e8hu29sWj3OEe+8f4pDPHHu5byQdrW7AbbvOyJYCjxXD+S8yvHWPLTYmLxS0js8OesCPOYWxTxWsYK8wQ3JO3IUzrvVRQa7FJ/FPAdiKT3ctqU7e/dXvdSXkLwJOjm6WjHPPEs5S7s+WAE7/JyHPJMWPzx+pAg81/zHPAbAwDyH5bo73+UIPab/CrzQ+oG8cI+tOyePwTv4OxE9Yf+1unl0BTvXSRS9UDeLO1Scijx7BBO9gNkePGaqezwO3gC9/QKDvKnWnjxO7t48MmdQPLedGjz9h7o8oGeTPNWyLTvNKbY7mzuLPDvgkzqb27c5YI6wPJh3W7wd9zi775q9vIA5lbwAGmw8m7/DvP/pGzynOiU84734vA2IFTx/E1O8zWoQPBE4Bzz9n++8sOENvCuomLwRqTs9snNOOzutIzqRF8S8csBZu3ZmArtSzRK9QDbBO0EBxrxgYpw7D70YO79rXzsAbWa8exABPSxdJjz0blO7ZwNYO5wqnTzIb+k8U6OUPPiSjTyJeIS8y4eau1twzryhIpw6fX4JPYz/+LuNIB299hbeOuSIa7yabJm8cC4EvSeGZrpBwBk863tOvCT0yTzziNw876CkvLfH5bzbd6o7edBmPMDuX7w4IU68WS6ZPEMLB72uAWS8DDgHPbXudzlbfD47wSafPF4btroHeWG8+WfsO94T7LojYjw8ygqqvH5iQD2eUKS8ssELPWS5BjzpGhW9drNKu8oxmjtR7f+8LR4QPAIq5jwroj28rxyFvLfhCzzL6US9CNVmu5EFdrwfTjw8k88lPCWtNzyhsPm8PZLfvEkoNbnRk348vJ8Ru27dLLvjLXQ5S37xPLSikDvwyic9+4TDu77FmjtCMUc8qVC5unFi0zwBE6Q8/VT6PK8caTyG+i88SKA3vGpwJD2jTS69tVCKvGfmmrzj7zk8/KDKPKX61rsDyqS8QGh+u2bR3TzdlfO6Uvddur58+zvN+q87UM6IO2W6dDy9pfo7fcaZOz7ZxbqG5pw7UpjJO/6tkTzN2B+8nuujPM3DeTozwVS8rkk6PF2JhTwPl+47q71+vAbGP7xZ15O8b0SGPMPppDuVyjO94VGgPLWs1rxh6l68P+qEO+uKxzsHrrg8gVEXPbwviTvFE728qKG8PGdrPDk3ija8O3mjvFxM3TvAJ8U6ieOdu+vDozzx0xo9Jdp3PGOKSbxBvdO8XG2QPEGkfDsbvP27umrQPCpiQrwtJt+83EdQO1F78jsu6hW9aeQsPHNfPL0LHLU7s/UAveyZBb3MRTi8AiPPPCE9rTzISSe6BLSQvHGIJLvzd8k8dLBvuxrsOz321ci6wnzpu6ryvzxfaly7J2+gOxmaZbzRAKU7a9uVOt5ujTzHD0o7+RfWvP/GlrwSBe28AgTpvOK8Uryvpnq73rExPAdkBbxDLpo8Wa6VO8/bqDxeiNm7A6FovGZj0jyFgni8R8oCPXDYcLz46V+7uj4qvDMDozx5IVY8A4FSvUPU2DxagLK80JSWvPg3XzynvCw7Cz7WvFkEUDxluOI8E5GgvIRuqrunB8Q8Oc0DOrSjMzuRI9E8OcEdvPH6vLl2G/06ajKRvDQTirwpgzK8fTQFvMy7DjtzFAY8oqudO2e2PLz5fCm9dKvQPM5OCDuCuN67oLBePAFztruw3c+8ksVwPKPwCT2bq4U8KKGcPGc+UTsPLSg75YPXPEkDL7tjQ9Q8kB5mPMi8ArxTiYE8zo7CPIZMqDyeRb68eQdyvAvyqjxWuqc80oIjuxti97sQDTS8qr69PIzmVDwWclu9I7Cku+giKbw1NS69csLFvLcZhjrJeAs8qA0rPOQterpaWFO8ajXyvAvilDzDTwc8gJLyuTY+9Dy/l226NT8lukgU4Dxc3YI8EKgOPZO1BrybDKa8pN3EO+VrPDyfnhg97bm9PK5j0ruxsj889x5bOxybHLzaTTM8xyTMPOMYQTstSpe88jzEPO/Svjx7M4O8loxGPDFj1zxZCmG8CGpYvCI28DtpqVi7FChxPTpRHrzVqhc8c3CtPENnrLvc5yQ7/D8GuzUUgDzpVy480Y3fObbGHb3xbQ68K920vLiNSTwI5xI8434ovAC8try00t872WuSPGEX3TwgSTM7TFEvvUge57w4n7q7XQR2u+TUCT1TeOa8at7XPEobWjygijE7LCmBvMr6cbztuXO8h7/+PFeDFD3wMgY8+gK6OqBS57w9Pp68cvLbPDGMLLxmj1M82aZFvN4rhTxVi2k8JWaCPFMeEr2GtPq6lhUbPX7hsbzMgH08bxURvVGskzwOMCE9XIeZu4On2Dyp7pM8FiakO56M5LpmJHA74vmDPLC6fjxUSzs7gMrVvDxH2bwwiby8MwJEvO/f0bqVOCC9qgjku8cCUrzddCU8vAQRPH2W/7yhQ4K8DyebPGSyGzySiyW6CbnzvApHlbyPPpO8xmmkO0PmsbwQXxQ7eeu+vPYfZjwU9Ka5u2ETvDY/TbtsPXm7A3nwu+QT/zsljXs8FVKQPHsp0bw3ABK9fsIOvPpxlDzhpSu9n2DDPBkgDT0LDZ082WGPPOuggbzpskg6anWnPBbnC7zvdzA8ZMumPJ7VnLyTnYA894icOlh1MbxfSZO8D7vcvCdkrDzLqxy8SEuTOzW1kDzVFaC87qZOPNGe/DySFqO8sCOMvJIyLLw26mq8a9l5O/DLgTxwR+o6wEN+OunGmTuryAS9nHekPATtOzyEZKw86oCjO1s3D7xcdxi8rCqEPPDrkLu/S4e8luOlO/wZvTxJTgC81sgmveF+fbybway7EDK4u971B71ucMq8tl+ePL841jyBamY8hHO3O3OGVT3zeyM8Mq+2uzfQaLqDEOu7qxjWPJEoyjxEo8+76rIYvGX7AbwsQmq7oAZ9PAoRZ7vuU228UchdPPqLj7xUCoy7/xAkvO6ZcTznCzk88HuTvJFTJ7wATfu76UtIPPPSqzyibpM8BoqEOijO+bwnuAs9STtPupDOhbw0jOO5pRhovIXbkrpB5jo5rAbcu/uEEDuGM8g8GwUBvOFKFbwzUZA6/xJYvBCM2zwBxiU7ZRQRPStMR7mWnPU79t5RPFyQgDzTSoi8FG9kPPTDuDxdzde8RpgBPWc1LzxgPQq9JhGsPDtqDb3Stci8RP2SulKBF72bfwQ8MmcPPNXmhryaTZW8IOh1PMXZqbxUWZy7vDGnu7KqGLsJkMO827mjvOcYdbzbsqu7PlZzvIlohLx1zgK9orslO2Hpwzsbagi7suCTu+ZkQLxrwgM9fRbRPAa1yDvzCMq75tqkPNeC8rzcYU28lqqAvIjporzjIbu8ets/PNrmCj2VKGs81XXWO6zbV7ywwhg8f9FbvefCxTzkyHQ8EfrZOl+WjjsrhMu8VstMPIb/Kb3szgo8FnaQO3xNLT1EWH+8CO6CvLfHFT3D4iG9WRiWvG8ZHTvK1IC8TEAsvN/kSr1cn1A9qwM7vJAjoTztsns5J64lvKHrVzsnSog8ljSbPMm0ibvvIKI8UxgfO36Jwrvmsyw7pdyCPFuUuzx/KKU88wOMPNE877vChNu80HkQPPW2ArzmF6o8G8PJOxznxbyjl927bPMTPBmu6DyMekW8C4O8vHBG+LzpuQs7rpLBvO6igTzEEPQ84ET9uTLuE7qEjaQ8eCvbvBd7A71gSEu8ovIrvNH4bDwRKDI8MTC5vIFDJ7nEr748UT4MOpXrB7xRyvo8o5RBPCp2Jz3k7Yo7HggcvEtT6LsH1z08ly2fvGyi1Lwj8FS8Js3rOwLr0LyqaYM6oemaPBLXvztssAE8746hvFXUgTyyDZW8NvIYPG5c7DtJkBM8SvUZvZa6sbzkahm9t6vTvCo/mzw/ANK7NYgpPQ0/mzw8aPA8RII3PMzwi7u9sDi8rEzLu1aQlbnxIJ671FR6O6UT2Twpvlw8npg6vA6gDj3fX4c7MIxRPPzMqDzTtya9U5O1vNn2dbypk8k8lcDBvDeQNT3xYDs7/YunvL2PRb3+HNm7lJgZvHLoYzxZlJw83L7EvEmuBDyRcka8d3xrPJXIIDw4aga9CPouvfo5hrxvWxC5sza/vA4/CbxbmEE5BfgHvQFfUbuJFuq63RjXvN0imjxN4sc8mZtBPGREUDziLIQ8AvihvLpjEDxEXgy9xZpRPBPdsTwfgdU70x4QvGjaFTz8J668I9rGO2/lm7sp/Wq8bf68PK/KwDyqVTe8aIb2vB/AITvJfEe8PcsnPAzqhTykBty80fobu6BPYjw3FLs8mmDnPChSBr1Vs8+8tliKPFDvnrqHzm+8sFJZvOgJD7wwN3+8dmMCPbzMeryvYcc6zHkaPNO4KzqwkUU8CZZ/vLF9jDz8GYI7IN2dOwHPrzz42Yg7c74svOmjBbxycK28yG4bvHb9abzZyYI85b5huhBHEL16gIC63SIIPX51CbmncnI7zQvHu/1/aLmqHCo8ZvjyPATzIzxyQAA8mHfSvDZ08rxiDoS8SspmutWYpzuXpB47d4YKvHTzabtpkiC81hGYvKYaerywh2K8TkooO20gxTyBtDo8qxfKu4hyvLycy+m6UmCcO+v/8zwmto48r6ptvEpfwrxziLa8CFY9vV/vGTx1va88xNI/vNnltDthTIs8FUQLvS+xuzx9ZKU88/8TvApFr7zIYTG6DWqyPPk7xztCKp664AmOOupOXTqjflA8CzGqPGMo97whExI8SwMVPFb/fzx3noK8bkd7PPuLirtrAGI7hNMmOiQ/HbyFPr48WTZcvID7cDwhmUs8NozfPFsXAzncBOo89UvPvCeSH7wmqrs8fWKZO/LKzTv8xqS612hkPI5Dczxac6Y86MzBuwksEDuo6hK865uVPLr/wTz1Dvy8UL8dPHFcuTtM86G881eHuvNwATycoRA81tDCu0k8Eb0YI4Q7HcMDvM5Yhbt0PgA7ivUYPcIHXjskKeA8MKoSvEp/kLtl9r86LYusO/S5ErxerNQ6Bj5xPOjUPDw+EdA8mEdLvMzM+DyF36m8sVUAvT1Us7wyXFS8YjkdvNvMITwvWNM7XR/IPCPajzrse0c8d2FMvHeFOTw/SKw6P/MIvFDjBrsVbzu8c2SaOxqxBrx2wQW8l7waPFFJQ7ver8g8TRVUvPq8JDyl2iE896ldPD1ambsziSa89jyPu/4XEDynaku83CMUvNB1Jrv3s6O8hxWKuw6TiLw6tuI4DGgGO1t8vrwDB7k8swa7OzkHMztVduc74SKEvDNexbuDeJa7Q2bFvLShGLxIe+W8TWUnO6k8MjsQAnY8d3n2u6LI7bzmL3S8cn6MvA== index: 0 object: embedding model: qwen3-embedding:4b @@ -1784,7 +9588,7 @@ interactions: connection: - keep-alive content-length: - - '32407' + - '44174' content-type: - application/json host: @@ -1821,9 +9625,15 @@ interactions: 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`. + Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer. - 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. + Chunk IDs come from two places: + - The `chunk_id` field on `search` / `await search(...)` results + - The `chunk_ids` field on `items.jsonl` rows (when you ground via direct file reads) + + Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs. + + Every answer that uses search or file-read evidence must be backed by `cite`. ## Document Filesystem (inside execute_code) @@ -1837,7 +9647,7 @@ interactions: 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`. + `{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 and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora. ### Reading files Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported). @@ -1868,27 +9678,29 @@ interactions: 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" + Structured document items. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file. + + Each row carries: + - `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results + - `label`: item type — one of `"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. + - `chunk_ids`: chunks that contain this item — pass to `cite()` to ground an answer that read this item directly + - `heading_level`: H-level for `section_header` rows; `0` on non-header rows ### 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. + Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `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. + Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in `items.jsonl`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index. ## 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. + 4. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents. + 5. 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. @@ -1914,114 +9726,342 @@ interactions: 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 list all different document element types mentioned. Likely the document element types are the labels defined in items.jsonl: "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote". But there might be other types in other documents. The question is to search for content about document element types or labels. So we need to search the knowledge base for references to "document element types" or "labels" or "element types" or "document element types" etc. Then gather all distinct types mentioned. - 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 produce a list of all different document element types mentioned. We need to cite the chunk IDs that support each type. We need to gather all types across the corpus. The items.jsonl labels are a set of known types. But there might be other types like "section_header" etc. The question might be expecting the list of types from the items.jsonl. But the question says "Search for content about document element types or labels." That might refer to the items.jsonl labels. So we need to search for "label" or "document element types" across the knowledge base. But we need to list all different document element types mentioned. So we need to find all distinct labels across all documents. That might be the same set as the items.jsonl label values. But there might be other labels like "annotation" etc. We need to check. - 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 should search for "label" or "document element types" or "element type" or "document element type" or "document element types" or "document element label" etc. But we need to find all types. We can search for "label:" or "label" or "type:" or "element type" etc. But we need to be careful to capture all distinct types. - We can use the search tool. Let's search for "document element types" first. + We can use search with query "label" maybe. But that might return many results. We can also search for "label:" or "label" but that might return many. We can search for "label" and "document" maybe. But we can also search for "label" and "items.jsonl" but we don't have that. + + Alternatively, we can read all items.jsonl files across all documents and extract the label field. That would give us all types. That might be more reliable. But the question says "Search for content about document element types or labels." So we might need to search the knowledge base for references to "document element types" or "labels" and then list them. But we can also read items.jsonl to get the labels. But the question might be expecting the list of types from items.jsonl. But we need to provide citations. We can cite the chunk IDs that contain the labels. But if we read items.jsonl, we can get the labels but we need to cite the chunk IDs that contain those labels. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide citations for each type. We can gather chunk IDs for each type. But we might need to gather many chunk IDs. But we can just cite a few chunk IDs that mention each type. But we need to ensure we cite at least one chunk ID that contains the type. We can gather all types and then cite a representative chunk ID for each type. But we need to ensure we cite the chunk IDs that mention the type. We can use search to find chunk IDs that contain the type names. But we might need to gather all types. + + We can use execute_code to list all items.jsonl across all documents and extract the label values. Then we can produce a list of unique labels. Then we can search for each label to get chunk IDs that mention them. But we can also use the items.jsonl chunk IDs themselves. But we need to cite chunk IDs that ground the answer. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide a list of chunk IDs. But we might need to provide chunk IDs for each type. But we can just provide a single chunk ID that contains each type. But we need to ensure that the chunk ID indeed contains the type. We can search for each type to get a chunk ID. But we can also use the items.jsonl chunk IDs. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we can use them. + + We need to produce a final answer: a list of all different document element types mentioned. We need to cite chunk IDs that support each type. We need to call cite before writing the final answer. We need to gather chunk IDs for each type. + + We need to decide how to gather the types. We can use execute_code to read all items.jsonl across all documents and extract unique labels. Then we can produce a list of labels. Then we can search for each label to get chunk IDs. But we might also use the items.jsonl chunk IDs directly. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we need to confirm. + + We can do the following: + + 1. Use execute_code to list all documents and read items.jsonl for each document. For each item, collect the label. Build a set of unique labels. + + 2. Then for each label, we can search for that label to get chunk IDs. But we might also use the items.jsonl chunk IDs. But we might need to use search to get chunk IDs that mention the label. But we can also use the items.jsonl chunk IDs. But we need to ensure we have chunk IDs that mention the label. We can use the items.jsonl chunk IDs. But we need to confirm that the items.jsonl chunk IDs are indeed chunk IDs that we can cite. The items.jsonl chunk IDs are the chunk IDs for each item. But we need to confirm that the items.jsonl chunk IDs are the same as the chunk IDs we can cite. The items.jsonl chunk IDs are part of the items.jsonl rows. They are used for cross-referencing. They are chunk IDs that we can cite. So we can use them. + + Thus we can gather a mapping from label to a sample chunk ID that contains that label. Then we can call cite with the list of chunk IDs for all labels. Then we can produce the answer. + + We need to be careful: The items.jsonl chunk IDs are unique across the entire corpus. They are used for cross-referencing. So we can gather them. + + We need to produce the answer: "section_header, text, table, list_item, caption, formula, picture, code, footnote" maybe. But there might be other labels like "annotation" or "definition" etc. But we need to check. + + We need to search for all labels. Let's do that. + + We can use execute_code to list all documents and read items.jsonl. Let's do that. + + We'll write code to: + + - import Path, json + - iterate over /documents + - for each doc, read items.jsonl + - parse each line as JSON + - collect label + - also collect chunk_id for each label + - produce a mapping from label to list of chunk_ids + - then produce a set of labels + - then produce a mapping from label to a sample chunk_id + + We need to output the mapping. We'll use print(). + + But we need to be careful: The items.jsonl might be large. But we can handle it. + + We need to ensure we use Path.read_text() and splitlines. + + We'll do: + + ``` + from pathlib import Path + import json + + label_to_chunk = {} + for doc_dir in Path('/documents').iterdir(): + items_file = doc_dir / 'items.jsonl' + if not items_file.exists(): + continue + for line in items_file.read_text().splitlines(): + if not line.strip(): + continue + item = json.loads(line) + label = item.get('label') + chunk_id = item.get('chunk_ids', [None])[0] if item.get('chunk_ids') else None + if label and chunk_id: + if label not in label_to_chunk: + label_to_chunk[label] = chunk_id + ``` + + But we need to handle chunk_ids: It might be a list of chunk IDs. We can take the first chunk ID. + + But we need to confirm that each item has chunk_ids. It might have multiple chunk_ids. But we can pick the first. + + Then we can produce a list of labels and chunk IDs. + + We need to output them in a format that we can parse. + + We can print a JSON mapping or just print each label and chunk id. + + We need to capture the output. + + We need to call execute_code with this code. + + Let's do that. role: assistant tool_calls: - function: - arguments: '{"query":"document element types","limit":10}' - name: search - id: call_539fpdo9 + arguments: '{"code":"from pathlib import Path\nimport json\nlabel_to_chunk = {}\nfor doc_dir in Path(''/documents'').iterdir():\n items_file + = doc_dir / ''items.jsonl''\n if not items_file.exists():\n continue\n for line in items_file.read_text().splitlines():\n if + not line.strip():\n continue\n try:\n item = json.loads(line)\n except Exception + as e:\n continue\n label = item.get(''label'')\n chunk_ids = item.get(''chunk_ids'')\n if + label and chunk_ids:\n # take first chunk id\n chunk_id = chunk_ids[0]\n if + label not in label_to_chunk:\n label_to_chunk[label] = chunk_id\n# Print mapping\nprint(json.dumps(label_to_chunk, + indent=2))\n"}' + name: execute_code + id: call_qd0d5ug0 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/ + - content: | + { + "text": "713678f9-980e-47ed-9e1c-e6b533231d57", + "caption": "713678f9-980e-47ed-9e1c-e6b533231d57", + "footnote": "73a2443c-3c25-4fbb-a6d4-0dec371ef104" + } role: tool - tool_call_id: call_539fpdo9 + tool_call_id: call_qd0d5ug0 - 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." + The output shows only three labels: "text", "caption", "footnote". That seems incomplete. Maybe the items.jsonl files only contain these labels? That seems unlikely. Maybe the items.jsonl files are not present or empty. Let's check the structure. - 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. + We need to inspect the /documents directory. Let's list the directories. role: assistant tool_calls: - function: - arguments: '{"limit":10,"query":"class label"}' + arguments: '{"code":"from pathlib import Path\nimport json\n# List directories\nprint([d.name for d in Path(''/documents'').iterdir()])\n"}' + name: execute_code + id: call_b60xwvbx + type: function + - content: | + ['40fa427f-7d9c-499f-9985-ce7f51d24db4'] + role: tool + tool_call_id: call_b60xwvbx + - content: null + reasoning: 'Only one document directory: ''40fa427f-7d9c-499f-9985-ce7f51d24db4''. Let''s inspect its contents.' + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\npath = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4'')\nprint(''Files:'', + [f.name for f in path.iterdir()])\nprint(''metadata:'', json.loads((path/''metadata.json'').read_text()))\n"}' + name: execute_code + id: call_rt6l2me5 + type: function + - content: | + Files: ['metadata.json', 'content.txt', 'items.jsonl', 'toc.json'] + metadata: {'id': '40fa427f-7d9c-499f-9985-ce7f51d24db4', 'title': None, 'uri': 'file:///private/var/folders/4k/n24fyvf11117vpdnpngx290r0000gn/T/pytest-of-ggozad/pytest-106/doclaynet0/page0.pdf', 'created_at': '2026-05-25 12:47:13.296904'} + role: tool + tool_call_id: call_rt6l2me5 + - content: null + reasoning: The metadata shows title None. The file is a PDF. The items.jsonl might be large. Let's inspect the first + few lines. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nitems_file = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl'')\nprint(''Size:'', + items_file.stat().st_size)\nprint(''First 20 lines:'')\nfor i, line in enumerate(items_file.read_text().splitlines()[:20]):\n print(i, + line)\n"}' + name: execute_code + id: call_nvweqb8s + type: function + - content: | + Size: 6660 + First 20 lines: + 0 {"self_ref": "#/texts/1", "label": "section_header", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 1 {"self_ref": "#/texts/2", "label": "text", "text": "Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 2 {"self_ref": "#/texts/3", "label": "text", "text": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 3 {"self_ref": "#/texts/4", "label": "text", "text": "Ahmed S. Nassar IBM Research", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 4 {"self_ref": "#/texts/5", "label": "text", "text": "Rueschlikon, Switzerland ahn@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 5 {"self_ref": "#/texts/6", "label": "text", "text": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 6 {"self_ref": "#/texts/7", "label": "text", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 7 {"self_ref": "#/pictures/0", "label": "picture", "text": "Figure 1: Four examples of complex page layouts across different document categories", "page_numbers": [1], "heading_level": 0, "chunk_ids": []} + 8 {"self_ref": "#/texts/8", "label": "caption", "text": "Figure 1: Four examples of complex page layouts across different document categories", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 9 {"self_ref": "#/texts/312", "label": "section_header", "text": "KEYWORDS", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 10 {"self_ref": "#/texts/313", "label": "text", "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["92e8dd8b-171e-4933-a0f1-16f5ce6ac545"]} + 11 {"self_ref": "#/texts/314", "label": "section_header", "text": "ACMReference Format:", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 12 {"self_ref": "#/texts/315", "label": "text", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["93ad7de1-8b78-4585-9865-01058d67019d"]} + 13 {"self_ref": "#/texts/316", "label": "section_header", "text": "ABSTRACT", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 14 {"self_ref": "#/texts/317", "label": "text", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["c0d1d300-462b-445b-8421-6a09606a2c9b", "912809c6-deb1-4a1f-91e8-ef3eadfeb852"]} + 15 {"self_ref": "#/texts/318", "label": "section_header", "text": "CCS CONCEPTS", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 16 {"self_ref": "#/texts/319", "label": "text", "text": "· Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ;", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 17 {"self_ref": "#/texts/320", "label": "text", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 18 {"self_ref": "#/texts/321", "label": "text", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 19 {"self_ref": "#/texts/322", "label": "footnote", "text": "© 2022 Copyright held by the owner/author(s).", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + role: tool + tool_call_id: call_nvweqb8s + - content: null + reasoning: Scrolling further might reveal more labels. Let's inspect the rest of the file. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nitems_file = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl'')\nlines + = items_file.read_text().splitlines()\nprint(''Total lines:'', len(lines))\n# Find unique labels\nlabels = set()\nfor + line in lines:\n if not line.strip():\n continue\n item = json.loads(line)\n labels.add(item.get(''label''))\nprint(''Unique + labels:'', labels)\n# Show some lines with each label\nfor label in labels:\n print(''---'', label)\n for + line in lines:\n item = json.loads(line)\n if item.get(''label'') == label:\n print(item)\n break\n"}' + name: execute_code + id: call_c2sddzkn + type: function + - content: | + Total lines: 22 + Unique labels: {'section_header', 'text', 'picture', 'caption', 'footnote'} + --- section_header + {'self_ref': '#/texts/1', 'label': 'section_header', 'text': 'DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis', 'page_numbers': [1], 'heading_level': 1, 'chunk_ids': []} + --- text + {'self_ref': '#/texts/2', 'label': 'text', 'text': 'Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['713678f9-980e-47ed-9e1c-e6b533231d57']} + --- picture + {'self_ref': '#/pictures/0', 'label': 'picture', 'text': 'Figure 1: Four examples of complex page layouts across different document categories', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': []} + --- caption + {'self_ref': '#/texts/8', 'label': 'caption', 'text': 'Figure 1: Four examples of complex page layouts across different document categories', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['713678f9-980e-47ed-9e1c-e6b533231d57']} + --- footnote + {'self_ref': '#/texts/322', 'label': 'footnote', 'text': '© 2022 Copyright held by the owner/author(s).', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['73a2443c-3c25-4fbb-a6d4-0dec371ef104']} + role: tool + tool_call_id: call_c2sddzkn + - content: null + reasoning: |- + So the only labels present in this document are: section_header, text, picture, caption, footnote. There are no other labels like list_item, table, formula, code, etc. So maybe the corpus only contains this one document. But the question might refer to "document element types or labels" across the knowledge base. The knowledge base might contain only this one document. But the question might be generic. But we need to answer based on the knowledge base. So we should list the types present in the items.jsonl of this document. + + Thus the answer: section_header, text, picture, caption, footnote. + + We need to cite chunk IDs that support each type. We can use the chunk IDs from the items.jsonl. For example: + + - section_header: chunk_ids empty? The items for section_header have chunk_ids empty. But we can still cite the chunk ID? Actually chunk_ids empty means no chunk ID. But we can still cite the item itself? But we need to provide chunk IDs. The items.jsonl chunk_ids for section_header are empty. But we need to provide chunk IDs that ground the answer. We can use the chunk IDs from other items that mention the type. But we need to provide chunk IDs that mention the type. But we can use the chunk IDs from the items that have chunk_ids. For example: + + - For "text": chunk_id "713678f9-980e-47ed-9e1c-e6b533231d57" appears in many text items. + + - For "picture": chunk_ids empty. But we can still cite the item itself? But we need chunk IDs. But we can use the chunk ID from the caption item? But the caption item has chunk_id "713678f9-980e-47ed-9e1c-e6b533231d57". But that is a caption, not a picture. But we can still cite that chunk ID to support the presence of a picture? But the picture item has chunk_ids empty. But we can still cite the chunk ID from the caption item to support the presence of a picture? But the caption item is a separate label. But we might need to cite a chunk ID that mentions the picture label. But we don't have one. But we can still cite the chunk ID from the picture item? But the picture item has chunk_ids empty. But we can still cite the chunk ID from the caption item? But that might not be correct. We need to provide chunk IDs that ground the answer. The answer is "section_header, text, picture, caption, footnote". We need to provide citations for each type. But we can provide a single chunk ID that covers multiple types? But we need to provide chunk IDs that ground each type. But we can provide a chunk ID that contains the text of the item that has the label. For example, we can provide the chunk ID "713678f9-980e-47ed-9e1c-e6b533231d57" for the text items. But we also need a chunk ID for the picture item. But the picture item has no chunk ID. But we can still cite the chunk ID from the caption item? But that might not be correct. But we can also cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But that might be okay because the caption item is associated with the picture. But the question is about "document element types or labels". So we need to list the types. We can cite chunk IDs that mention each type. For the picture type, we can cite the chunk ID from the caption item? But that might not be ideal. But we can also cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But maybe we can cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But that might be okay. But we need to ensure we provide a chunk ID that is present in the items.jsonl. The caption item has chunk_id "713678f9-980e-47ed-9e1c-e6b533231d57". That chunk ID appears in many text items. But we can still cite it to support the presence of a caption. But for picture, we might need a chunk ID that mentions the picture label. But we don't have a chunk ID. But we can still cite the chunk ID from the caption item? But that might not mention the picture label. But the caption item has label "caption". But we need to support the picture type. But we can still cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But that might not mention the picture label. But we can still cite the chunk ID from the picture item? But it's empty. But we might need to find a chunk ID that mentions the picture label. But we can search for "picture" in the items.jsonl. But the picture item has no chunk_id. But we can search for "picture" in the content.txt. But that might produce a chunk ID. But the content.txt might have chunk IDs. But we can search for "picture" in the content.txt. But we can use search to find a chunk ID that contains the word "picture". But we need to ensure that the chunk ID is from the content.txt. But we can use search to find the chunk ID that contains the word "picture". But we need to confirm that the chunk ID is present. But we can do that. + + Let's search for "picture" in the knowledge base. + role: assistant + tool_calls: + - function: + arguments: '{"query":"picture","limit":10}' name: search - id: call_f9c6zlic + id: call_1atkyf5a 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. + [93ad7de1-8b78-4585-9865-01058d67019d] [rank 1 of 1] + Source: ACMReference Format: > KEYWORDS > CCS CONCEPTS > ABSTRACT > DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + Type: picture 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. + DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis - | | | % 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 | + Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com - 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. + Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com - 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. + Ahmed S. Nassar IBM Research - 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. + Rueschlikon, Switzerland ahn@zurich.ibm.com - 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. + Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com - 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. + Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com - 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. + Figure 1: Four examples of complex page layouts across different document categories - 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 + Figure 1: Four examples of complex page layouts across different document categories - 3 https://arxiv.org/ + KEYWORDS + + PDF document conversion, layout segmentation, object-detection, data set, Machine Learning + + ACMReference Format: + + Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 + + ABSTRACT + + Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. + + CCS CONCEPTS + + · Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ; + + Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). + + KDD '22, August 14-18, 2022, Washington, DC, USA + + ACM ISBN 978-1-4503-9385-0/22/08. + + https://doi.org/10.1145/3534678.3539043 role: tool - tool_call_id: call_f9c6zlic + tool_call_id: call_1atkyf5a + - content: null + reasoning: |- + The search result shows a chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" of type picture. That chunk contains the entire content. It includes the picture label? Actually the content includes the text of the document, but the type is picture. But the chunk ID is for the picture item. So we can use that chunk ID to cite the picture type. + + Now we need to find chunk IDs for the other types: section_header, text, caption, footnote. + + We can search for "section_header" or "section header" or "section_header" etc. But we can also search for "section_header" as a label. But we can search for "section_header" in the knowledge base. Let's search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":10,"query":"section_header"}' + name: search + id: call_6uhchwxo + type: function + - content: |- + [93ad7de1-8b78-4585-9865-01058d67019d] [rank 1 of 1] + Source: ACMReference Format: > KEYWORDS > ABSTRACT > DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis > CCS CONCEPTS + Type: picture + Content: + DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + + Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com + + Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com + + Ahmed S. Nassar IBM Research + + Rueschlikon, Switzerland ahn@zurich.ibm.com + + Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com + + Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com + + Figure 1: Four examples of complex page layouts across different document categories + + Figure 1: Four examples of complex page layouts across different document categories + + KEYWORDS + + PDF document conversion, layout segmentation, object-detection, data set, Machine Learning + + ACMReference Format: + + Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 + + ABSTRACT + + Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. + + CCS CONCEPTS + + · Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ; + + Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). + + KDD '22, August 14-18, 2022, Washington, DC, USA + + ACM ISBN 978-1-4503-9385-0/22/08. + + https://doi.org/10.1145/3534678.3539043 + role: tool + tool_call_id: call_6uhchwxo model: gpt-oss reasoning_effort: high stream: true @@ -2081,14 +10121,15 @@ interactions: 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. + Accepts chunk_ids from search results AND from direct file reads + (items.jsonl, toc.json). Verbatim copies only — chunk_ids that + don't exist in the database trigger a retry. name: cite parameters: additionalProperties: false properties: chunk_ids: - description: List of chunk_id values from search results. + description: List of chunk_id values from search results or VFS reads. items: type: string type: array @@ -2100,549 +10141,375 @@ interactions: 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-927","object":"chat.completion.chunk","created":1779702566,"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-927","object":"chat.completion.chunk","created":1779702566,"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-927","object":"chat.completion.chunk","created":1779702566,"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-927","object":"chat.completion.chunk","created":1779702566,"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":1779176227,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]} + data: {"id":"chatcmpl-927","object":"chat.completion.chunk","created":1779702566,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" \""},"finish_reason":null}]} - data: 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1QyJuOdWU7yX2vw8FTI7vLy1qbksIY88lw9MPXp/dLttdpg8JRmmPH3Ba7wDQEk9SrXguuixIr2iKOU8to6kvaoOnbwnLQe99W02PaDZpbtZFbS8kZwGuyODVDx+onY8vvMYPA+pwTyOSpa8oRqqvbc1LD1StKW84O+mvBFTRr0Wo4Q9/7O2O/SbiDvwfqq8NSMCvF/W/7toEsy8019GvIMqFzuGMxS9zgHpu76Dy7oRjOk8nKGDPEjQnDrqwzI89vd9vOmbiryTaQQ7EN5QPI3/EL3szru8trQIu57RNDzam2I9EUtGu+LEBzyG+KQ8qJL2uhdKpzwgVlm9yt7AvLTCjTpC48y8gfS/vJpohb199pQ72Q4NPJkYirwXHaE81UQNvIDt1jy31Si9IHCuvLvMBLxDG/48u2vFO4OHAT3ZaSm8Yrc7vB+jCTyuNbI5W1aiPLO9qTxNV+k8w10sPFV/cL2tDRy83rlVPD2lhDwVFFG8paXUPH+1NruMqhS725uHvBWYhbucyXS8c21DPG0DhDvRjs07FFnPPJ8LKrz19EI7fCcGvfn7s7zNOEQ859yIPBaLsrve2dW7LENgu4vcJb0wZVu727uKvKU/srxdaZK8yQtSPbnKT7yF1So8K3duuzPBhTzIH9g7qlm7PPjJGTuzkga9f7aHvIxtprwJrZo6pqNaPAE5+DtuoLi8/Fz0O7FtBrzSbhM9DeE9O2oXLrpENWs7kl7IvP1XYjySHYu8++R+vLp1VztELag7Ru5ZvJO2B710Rqe7Lx7TPOpuyTzGwWg7jnWcPC/Aj7zLCDC8tUK0PFs2TTzXxJw84mChu/isnDxGrAw7Jwh9PIFjSbsBCYc8BKi6vP2aAT3aIvI8LZbjO1zDj7xUn+o8XnTku6adhTo6YOU7bCuVvI2QRLsSz+e7CWjpvBkZIbwzsne8YNkgvQAfBbzZKvo72UMHu9MTEj281Iw9k2RFPCv+Cbxbffg7/ewfvGYjmTw4Iio7iH0KPFrCmbx1dI28Zmu9O6EVFLxgqgW7cNCOvPCEGLztnkq8gAqPO9P2sjx2hiy7SAwaPPqU5TxwDFq8f5jzu2Viw7uW4go8wvm4u1uDZTxEMsW8Oj7NPJfplrr4e4G8a2pCvAtLj7VvR0886KPsvHygXLwC7XU8FObCPMbiJDyPcM46+PkevGKWGTuUX0a9d4kMu4E6Qjvnr8c6C8ETvGJaxbtOocw7uXQGPPCEbDmFp3m73RtKuxipQLweabw7eMWKvKmzarwkanu8Ps0yO8hXAjuiqoo7ydwgPNVznbvCGLu8jlEKunI4t7yPpla8+1LkvO5spbze7ks8M8FQPES3l7evD6o7SQioPFBnPbte8x882PQoO59QGD1aAjM8uvrcuxlmLbyL8Sm8N/srvDaNSDxFSKE8V2OAu5Xa4DpsxdC7oZbmPCIohru2vbC7zyvmO/PciDzlAJq7VFhsPBOA57vBLq06Q/T/PMlkE7wrRuw7hlsbvco/arzCsXy8ckBaPDWNY7z9QZK6UAgRvI9tmTsKDK081JT6vPhIA7m7rwQ8qioWuzJ61rysw6Y7ZjyGugZfZ7zv6Fq8//QVOw/wPDyGy887CJ9EvDWYi7xOKrg8YXG1u5Chlzw/5I083zgnuwH1ljyqApG83BKpu0OMqjb4pu47CY5Ovb/peTunNR+6gc8Ava8Qi7xzNCY73AVcvR52FbzjlI+8qV+4vEU3kDyIsPE8GiAbPPFFDD2eX8A8zfx2POL+oDwZn7W8lnQ/O4jvJTyJUJa8t60XPCSZEz1PdMk8IL52PDuiwLyhhoo87ReovP7z2Lx9i1u9Xi8xuqbwpDwLYte7N+jqvCwFMLzz3BW9z2B0vXKp77z5mqa8F1fKu52KGjwdfMc6DHhDuxhpczv4p7+8bxrqvG/DpDza9gM964iXPDJ+P70t3T67zLiivNkcvzy1TSS8RHsAvXjBh7wF1oI8VzWFPPrIHTyV2c887o3ru3mwpLzs0by604nHu7v9SLpShQG7fUFTvKLa1byW4eE8CFSBvEjRtzv3FbS8KrqxvBZRgbyeqFC8AIVLvCDUFLxeh4c751s+vAhzazxr8ww94LjxO9K1WjwUjes8KX4cvWlU1zzzaMy8jNWYvPvPHTyXb5S8myEcvfqec7xGeyc9YhG+POem9rzJzvY8nR2Iu9Ef3DsDGSi9U5nnvFs+/zyST5Y8bnBavITMsjscDzc8PfhNOnz7o7wVBLG8x/lIO1jStzyTQ4c8afszvGRJ2rvzZzi9fZp5OsxGsrrYcF48tEOwPB4pVj2j9IY8pDKLPFzWTzxbDPy7Qd1UvJFs2by+i6g7FUAwvDhsPrthwwY91qNsvFGOyrs2jIc8iUanu/HXJjwpowW8ZXkfummSurxlZgI82kxHOcJrRbs+RZU8WrmkuwcM7rvntii9yu+sPLRX4L3KwAQ8FWyGPP5IMrtOALO8rcNLvZ8BtzyyTBq5DuEsPXEPAD3UupG8P4MHvRp9oLuLYQI9OILwPBM0ojyLOFU86O5avOFqcTpL39I8JxYyPY6717tOBwQ9gI99PJlfpjximI084hqUPC3XQjxWGjU7QD80vD2bijzlZi88GpBUuLakqruXjI48wX7WO60xzTxapQO87R9QOxgORztqgBW8QWI7PAsifzyTrKm7X9QGvWgbujyYinY7Ueh+vJRkGzyU/O07GBiXPAfgzLpDz5K8CkspvaGl5TyEaYK8KQj6vJ3skbwC/v87cbX9uwe//rszlmq7H9KBPDuZmbyDfyS8xAdfPIL86zsVFNO8TSIQPGORbTycjeI7Fi/cOn+/i7x8L4w8CIUmvdKPHTwHiMe6RcfDPFJdzjzC60e8aykqukMmi7ouGD48TmNuO3yiRz0fCrO8ifGuvGoqAruQR9e84DTtO2p7yLq0sI67a4U+PVv3hru4yxQ8iCCLuQu497upY8484ZCgvPv6gryGkLe8f6C3uzAMXzzWLBU9wuALvN2IqLxjw0E68Oiuu0EP67sCP4A8aNUyvDi4hrtV/+q7hyUIvCghC73sptO864FIvDoWN7yP/ta8JnKcPM/86zv35MQ8INiiOiBOBL3XjFS7oK4evAo+mryPZBq8PgSNPCg+gzvOG/a8J9gbPOSfLDwejVa84hy8PH/WSLyCYKa73XPIvIM/0LvISZm8MBPhPLWHFb2PBi+9h7I8PBrz/jo2U4+8KK8EPEMW2jtSKhq8rEjRuzbBMjorXlG7B4gTPNIHnzvbYMA8DacQvYKsi7zvpdK89zdnvKaoqrxs1qy8aJOeu0J967o+Jo28Hs6+OjdoZb1kMJE8ycEDOtjDnLzRXwW861msu/1ltDv+w7Y8OhsWvIFg47tiX568V3TqvKZsKj321Ku8Vl2cvI8RADx3gzE8ObfbPPi6rDzvw7s8t7PGvIuD2ry8gK86vE2uvLSgAjyDVkq8c+TjPKktS7wTga08LevIuqdroLw/c+I8oKLNPHvYNruUyfM8+AGmO+UFrDv1Tb47gxNfvBPnQb2Dzzi822Twu3EBQjtpR1c94Bfxu29MC7yIpzQ9z34QvY1WbzxeDcI8b8fNvE25A7yl9No7MYkIPYt4Bb2XuhE85LnYPGrwszv0UHK8UimNOmd/sTsqhca7Sq1DvEvjAjzqaTg8WRaHPDddFjuM+yg9jhuPPM0NQrxLwKY87oLSu+ix2zwB7wG9aRwUvBCNrzw8uuW7CHSyu+TY9Ttc34q8XUAoPX/gjzsur+U5DxcLPJgYIz2m+gm8HPYevYRoUrs5M728gXxfvLBFwrtfMfK8wPSdukD6gry2slw8mPywPDV/8DyShKC84gpRvLM5WztMF/k8yGnUPDgPvLsMSU08JwAYPRnMwjwKYNg7NruNvFWcFD34nqW8YTpJPJ2ASLwKj6e7/B8GveRuMjxJKTi7xXmbvKq08jyZO7w8KR2kvKwDazy+DBi9gpCyvHCeyDyrxES7yz+VPOIdRzujuC89RakNvVV0XbyHgik8sTIXPFN8UDyx7ME8RLylPBXowLy96bQ7yhPou9Hw9zo9UPe8mieQuwrgrzwowfW8XAwbOu6OhLxCVw+9okygPEXOtLwxZgU9D9WCPIhkg7wed5q8gh3cvJsNXrvcUoS6+LEEPYFNhbvG08Y8467dPCjMEr1z6FU8Pwm9vLfHy7xmAIm8LmkWvTA8Xz2WE0a7/sidvA0JjLkZ6nA8OqkGPMsvKrzbAcI8C/sRvNdcQztHnrS7OxqPvOIn4ru16TY9p+H9O/en1zt6Fie8NIXKO4Bzcbv7aM64HsRlPEfPG72wnjk85wTAuxrnHjzCeBK8oCZFugkbsTtTSVU7APTrPMFouDw8LLo8HNh2u43qiruQjrI69RjUOeKxOD0DPKI8vkIAPEDXITz1QyC8dp6aOyOIkzwK37c7afC1u1jeiLyYgLU8xyLHvEXBpbz6jZ27h6cQPCJiljyAOgo9650DvYRzvLzwGQI8vHdWPFyLqDrb1Tq7YzZnO/75RDy6PEM9ZCh3vLZEZDwlUT87XDmqPK3gaj2APWM8+hcaPDi3pzsMvMM8/BvNvBD+9rzkPTi9Rk5TPPqLn7whU3M8XpOuvOV06jslCx29GPI3PFgtpDxyTQc9BkQIvM0hvjymZ4M6cWO0vEAjtroSZ9i79V4SPD6ZeDz7oOO8LCKnPPbAyjyWVri8NqgDvbBIFDwQLb88Sxw7vA8lkryhmPK7EaIBPAiE8Tz35J88UKrPOz5pQLtvyBK8IFiCvLsVgz31lIO83+0kO0pgqzybuKO8WA8KPYmwoDx8hqU8CXXguidj8LstYgg8gapCu8lgPryEHl29cyuFO7Y7Rzom8fe85EukvASemTkidX88l5bDvA5eWrxMCjk8S0FTPJ23s7s5+HG8E8i1PLC7+bzUyZ28QZATvH+WYr30M0O7VLk4OgfR0Dy58QO9DQdiO3F9fLwuIV686cqdO6FQlTwCHmg8dUrru7O/+zlirwK8qdm1u6okcj1FKxS7TTC+OqnA4jz34Xq7JTIaPSMwEjy/Q5A7PXQDvEWHCr1QHzq5jXuOvMgEPryjUxA9DYtAvHROszy8d5q8n2SrO150KzzycVa8NqwFPQhqDjze6ti7AUdkO5rldLzqDHe6rVPDu2I+WDw68Hc82DICPE13w7mE/646EFrHPHgxZDvC+T89nWZrPJ3ji7tUprm7MtqivDf8+rv3FNS8FnS7PP3cCb3zAwE6t9wgvS5fijsnTQG9PDKzvDCQz7umNs87BwCoOwvZHDw8XE89j+OHvHwLOjw1Lo08miLYO4HZ9byBpM87glkrPFLA37w+CRo8p36ZPDU4rTxvVIM75qByOxSUR7ziBH+7teifvJLVULqgxCi8Srn4vDNYkTzo00I82ge+u79j/rzHKK47wSuPvE4xtrv2Jow8m71kPDHmaDwwVqS7vdgwPCa4u7tekXk8VHnYPFXfIj2e+0M8fvZSvDmrTTwU6qs8VD+HPPg0b7tV+Ms5LB+sPDU7h7uSOM+82r+du8tsp7wyY7e6XJk3PDV8n7wz1gy82jZmPHVu/bsPtx28Vv/WPMHNCT3v2Tc8WKrsO8coY7s3SKw7vjmUvJIO5zwlwYE5tPHWO5ax/Tv1hE27gGElPGe7tLycnOO8nCWpOLroBbszLOW7FASgvK7MxDwz3SG89bG5POh2jLsPCDq8q90wvMzzhby6oFO8SjHIvKD5jrxPeZ68jD74vBYaQryXrps7yHj1O+HZJjtJmQG9elIHPQJhxjx68f+7L3IdPE8deTwWZoq8/lKevPtsiLxfOHQ9k8EtvPi/pbzAEqo8p5Y2uypABjxNkw0883aVvPLuarw2F7I31LuJvPZjtrxrmeI67R4WPZZD2brDIOw8MTPnvArnHzzo0bq8ww8nPMXXHjxcUge8MThOva+wYLvbsqk6lbOKvO218juqebg7fN+tuntUIzxH+Sc8BV8QPMJPFD21fxs8Co9RvAf5H7wLmdu8gm/APE14FT3BUJg8VCpDPT+SAjyChHe8h5ZGPMxq0Tux1Ye71NGyvD9aCLx9mH+8bjYtuzG4tDtH4Xo9FCmnu70nfj1zfJm7eWwUPX9DR7yeoQK8C0gpvJFfYTtrh8q5+9P2ukL6bzrmrqs8g5EavTsm/jyAGoG7EAlFO+XUKry/ySS8xIt1vH5WNjzSd/68fzrOPF95OLxpRLy7/0O2uiBtsbzxNcU8xCP9PBUSDLuQkHo8lBgaPZVMkbvuFKI8ZUT1vPkNJjywpd+8CdOJPIpQU7y5ORu95YnDu/m3KTzosBg8Fa2jvD83DL2FCcU8veewvJvTQDw8RyK9i7G/u0rSlbsM7FU87iIjvC/C6bziO5C8HMAuvEXFjryuSG+79F3ivNh7Ejyj/TI8yDiTPMHm8LpHY1C70nv/PIqSCD2gMBw8saMgu2SY8jyvII67FHSYvA9W0jy9hLM7mLI0PXTMlLpStWq8ElgTvM+AKzy7N9K8lcSdvEeExrwXuFK82/OIOuuvRLz1IqA7o9mQPHbSgjwuOVQ8sff0PI6J2LzFw7E8WJEfvDUEO7zVKI67rg9ZPO4TBrzBaJ487KGrO5NGEz2HgBE9OUDAPFfyBbzXxco8OcXFO45AwbztNDq9e5+2u9ozKb3ywAC8q0y1O7ZVlru+yoQ6IGnBu4URYDwyowA9+4apvDkcJTvgu128SURgvXfiWjumFJi7w++KPD2BTjwpzgs8jpa+ulAutTwOFLw8c/tZvbX30zzMi8C8Oh5HvHxvTLyiiZY7hHqzPLWgn7zMib88TFJlPCfhbbs2yIO8zxKsu5DOWrxDIoa8DDOoOwGLyjxQPU+9s1+7PBP4aDxSBdm8inOLvBDIaruPc627OCENPNqcQDpefcE68/fuvIBsrjoPC4Y8DWedPBTgFjyoHZ47gwL6vBNvqTzNjN87bi4SvBEbVbwn2rk8DMRrvJuAOrztFWg7kpUkPQmhrzr7vFa8pGCqu/8fRDtnqmI8DEXyu/+03zwO0rk7uj9YPMg5UTyH6Ye85Z4hvC70Bb0DJQ43fQYUPEZuprw0DOs8DCyuuFmTrzy6MTS8/a9ivOjKQ7zYUvY7ZfoqPNWO3rz+Loa8+R+IvBfnarxheuY7v5gEPUh3FTzafzy9BY6oPLn9GbyzI2+96bt8OYqFFLxQuiA8nFlfPSOXWbwPWsI8oJprPI7XSjtwxgq9VvqyPA0LM7vFzh68x2UJPGRFxryt+L28f3FeOjClCjwySFk7FaiZushONT1P+Rq8cgKaPMmym7qb4hi8dAppuvA61DzOsBK7ahuRPNetHLzzuCC8QEgEPMmvjrv8gsG7IE5pPPSvLD3xcYQ8cc67vGEboLp0Lye8SHltPGV62zyT/3W8afoAvaZy6DwyBMM7mNlBvfB4JD1X6w+8ok+XvOg7jDw5Dwk7KmaKPKpaQLyOoEU6lrLyuyLcF7w++Nw7i0/eueboOrxS2Si9VkDWO5bN6jwfqmS7jKMCvENjx7umzO68r6OvPM05L7xcn3u8/SybO1tUrzycQ1y5GdT3ug29jbwLO8y7zAazvL/nJD0Dj5g8P3M2vBlYfrucXtc8iVoMvPARhLx0udE6twYFPIK5ujs1X0w7Sg54PFC9AT2N2DK7kDx2O3SanbxhFKu8spVfvA5/s7zyBRu8fD4PPM+Nc7w3Dm68hOlROz7MPDoDyxi8IddqOmiyLjzD/Pk8rbravED2r7yvc+O72iiGvFLpNDwHK5o8F+BxPJ19urrjQYI7tAhoPSgrNL1VpQi6zfnavEVj3rtWMt68vZF4vE+mHz0Dcq48ly5bvDyBYbz0Zu08l6SOO6GxEL3o/xo81e4UvD+zJzxIDzE8BURxO6oh/DxTO6u8qcWvvMDJWbwSkx69hggVPCLS+rrMW9K714DsuncXtDtLvyW9bEdqPQecRjvKmkm9/C3ovJUzV7t0hgi9ipQ7PKqgYzxNopW8EbU5POntYrzBUWK8CAQQPV5m0jndus0869alPAdjtrztEqi7nhK4O66iQTs3zxi9x3h0PGRHvjovra48i8mWO5SjzjwI9Hs8deaWvNLukbuT1hu8MkNMPDqdEzx89VA8aMZSPMUbmTzCkwG7gwgEvCk1l7qgCxk9zul3vHDfT7yXhbM8S1A0vXNb+7y0JUw9kQYevF9UgjzMpNk7aLVsvCJjHzyEfgw9ob7SvEN2lTt5V6I6T2FkPBbGGzwLG5G8hdXevK89gjtXSto7J4QCvZ2FmjzXJjc85WaOPCkOIruBSbQ70fhdPD8zGD0fk6e8ZrntPLuh3LxCZpO8ivqVPNaA0zy1P6a7MXPsPFQ5HbxcsQk8Z0LSPLZ5njxnEbK820GnPMPfLjwUNaS7KRkZPEXEhjwB6g68/jFBvc+VKjw+9By8phAePIoSCTwGiDG8y3fRPIyB4Dt9C7W8Xuihu6fLUjwGWyM7SUM2PGyDA7ov9ji8BDjoOizbYzwpVtk8us3gvAXOlbytFkC9vNJ/vC4YIDwEvie9Zor7O8ysIrzqWRq87wkCvbXA6TwtDY48opUgPKcugbt5rMs8MQpTPITR07ubBlq7kY7pPClpXjz8F9E8PH7NPEW5iTqhGqq8Lg8BvSCaWzxwPoQ6uN/WOppWGzyKZn451v/svGrv/jyN9y68g8OdO/2PEjySKXi8nzIjO5rTBb1T0yU9kqy2PHQbhrwE9wK8Vq4RusCvh7or0Yi7xuKqukm5+7zfcz8897EavLWkijyi2re7Yy4VPeA1w7xxMN87Cg+dO4A5PTqaOe88+pJ5PML7DTzYKs68xu5YPNwuxbyoEhi6w4xIPZAktLsNm7u8iZbeO67a7zmIZ2a7RH1mvI2mX7yP7kI8v0wDvXs1ujwl7bA8G5UYu28shzryZLi8HAJ1POduWbzHR+e7GzMHvK+yq7y80JC8PnvROz6jqLpH6wO9ZWiqPPS7frpKG3S8kYnHuwILgLxy9cY8t3tlvGt98jyQ1Sq8BtVZPCLE0zw10gS9PLu4vG3wzTrg2F+8O9/mu+7MID3JVuK8zDaOu0QdxjtgYVG7Tz8yvCCJ47sbcJk8QC27PDDzKjwPGuO8ntNDvalQoTwkGt48Z8snvNTrcryE8DS718S5usG5mjyO9fo8xKIEvafGGzpBFJM86LANPeWcnTwki1I8H4p1PNulwDzWGeu70lOYPEZhzTwx7j68AOGNvAOwj7yxmTq8PvCePKZZXbq9r0S89VfNvGY9Wzzbzco6Sc7ZPFm7HbsFWJQ7jyGXPDDAgjxQaZ2677dCPEnwujubRI+67PIUu1S2UDsiWNe7dCkRPYD/HjmAftW7UGKrO5Sd3LnfK4C4a3WjvGyOE70TWcy89FMiu0hXJbwYlKO8lMqePOwBp7wSCte7UPVKvNobOT1Saoa64XZjPAaqpzxPYre8AeOwPEgxGLx2SpQ7E1EQOiv67bs7Noi8j8geu8rEAz19eQA94/JNPKv/krxLju+7WaOjPCsXP7t3c527zbPNPLJmFLrSPeC8l+U4OhT1+rupqPe8vx4HPSHdaLt3pao8NlQpvJxID72BD6e7WlrKuxN+3zxgJhc7PEaTuFsJiLtKiJY7KzPDvAe6Oj2ccpY8ToUxvKcILzy0YQs70rbsOl5/K7t+N6Q89DeovFycibvWOpy8NAtmvB8MabwEu+a8qmO6vLXhK7xJ6Ei8QhF7uwmajbzMWzc8dQptvJgQO7zKoaW6qCY4vcK/5bv5g9m8iGVvPOwa2LwpxLQ8aQ3YvKkkuzwuSEw88F8EvSA6UzzdHqO7SxgOPOxu4bsJ7KE8g9javJ9bA7sUWgS8MzTYuw86gjyweO+71+oGvGzTnbvEd0O82R0kPFa327v3B5O64DZTvF2007tn+RK9JR5nOpnI7bplZaE7EVLJujaunjzCOEa9gEDjPHh4PTuacsW85zlgu5gENbogaFC8Y4TFPB+2GD3S+KM8n0eRO5kFUTzUNk672AdYPJc43LqKck88sb8wPeqM97t68KE8THIRPf43+Tq5/2u83FqcO+IOuTxQzt08HF4JvKyiWbyb86m6MZPEPNOsCD1z/w+9q4EovAeE17wDVKq82mQQvQ/wlDu2te+7ffKGPJHU2zo6T+C86xVevUQ0rjpy3448VFVnvB9bLjw3NpI7b49huraSgDxQdMk85YGmPBHwsTtxMIS8F442PH2wObwlgsk8THrXPFVsozzBeYs7pdUHPKMTy7z7fsS680sqPAI8ujx2F+a8/POCOdBHjDzUJI27ZmW5POmALz2b5qQ8XEIWPKy5nDsKvJ68NF42PaSSWLzr++U8D9jYO9qNAr0UYyW7+MkWvAATbrtws3a8TIgXO1zHlbxvkMW8kpnzOOImhDvkSWK8QQ7Xu+VRyLu5/II8pchoPCfdozs3dzi7uSGZvHgYNr2cSrs77/HbO8zchzudfdy8vAz9PHP8f7xL4yE8Og/+O/Ves7uoSVK8lUYDPfGEXTxoKJa73FiPO6gUnrzBU728ODqpPMgItTu3oBo8V0y0u5G2IDzetKQ862yAPJgG37wCiY+8klucPCtH+bohpoq7uv3vvCw/pzz9adM8rQHKu8lW+Dw1ypE88xD4uyDszLxWfws7/fJDPABVLDybr/g6L0cavcWRLb2Rk7C8WwKJvFQiLLw54CC9T6iZvBx2V70cvgs93QVMvHU9U70HWeE7QLT7PGjURLz1Aay6lmQQvaRMiTvNCK68sOuGu9DbOr060Cg79NaxvKCfIT2xrJ+7BaOwOjubFjxGJZy8vd0lO5kQiTxwBBI8XNA+PEn8e7xvvhC9hYQRuzAAHjwie5W8e2GXPEifTzxyP508JGl5PEYXwrzY1hu9g1mcOwwN7Lrdil66Je7hPBu467soaY88Z8AZO+hF67vmsgy8mk72u9HkojxT1XG8xPfVPC6q3zws28S8FEUPPcdaIz2FIA690becuy6D1byrdos7TO4uPKUyKDx4O1M8BwEBvV5T5DoULKi8xLq4PDqIazrWcsE7nEuWuQlx2byg81O8InCPPHywrDf0FeS7Y1M/PG6QXbwHXcG8hLX+vKln2zvQaQ88MVbru167i7xg7y28rNk/PCU0AD08/1U511wVPN1V7TxoQ8i8FdDNu59qlLwQgL28jHUVPBIaZDuPniS7Au/APLxPcLtjTO47ZsAhPcDi/LsaP3u8eB6RPDf0Ar28kw+9QWHwuozoEDxl2788N/3XvPtSwruFabi77nuePFllwjzLXJs86yiqu9eAaLxfnJI8Th2uvKfKDrs7VRy9TU6YvGZIFrzSuem8NSkavKwl37vlKjG6uJX/OtLP5TrFBek7/l+ZOwNtMjw6oEg8ZtVcO8IklDy1Sfs6r/KLOz0w9Ls7Ypy8D3l2PJsb0jw045G8rcwZPX0J2DwRvlS88WsPPXmdb7z9UBG9EOJOPEDrorwek5g8a2KNu4m6zbz8Z6G8vIoxPCfGy7zRxxK8Pb8gu13fYDx3l/m8964GvXlp97t8IvK8P7mGPJiWijvytiO9KUJuPJy70rpsQiK8+x8Lvdzn3bq/NYY8zCcMPP0BaDzWUJy8HK3iPJYuFb2ixUm8nCnxvL+xCbwHjXi8dC80PL7q8zym8fE8K1L8usKlQ7tVXow8pJAQvZWx6TsMZyi8/LtFPM/4i7x39H680k2KPISdNb3puQg8oUZjPGPL5zycvZ68qwjDumiSuTyqjFi9/AmTu03Ne7xfEyC87jPiu+Ot47xAv/o8xEDHvHGGBzx+IpE7SmJ8vCnkB7urw507aHICPQnXFzuQetQ8zk+RO7FyGrzvnku7eaunOzgi+jwwbtI7jXvMOqMVCryfYri8m2oIufJWIrv/XdY80pEcPIVHMLwmOvU7JmOLvN7lXrzGHhO9c90/vWJczLw9BBk8A8+0u1Drt7uP0Py7P4NnPPCbWDx/thM7/i6svGsnrbwHGwE8e4Tru8cECTyGxZ47oeQou0a/4jvTX6M8KJ+wuzLR5ru+hOm7WJqZPPNc9DwShvS7Fn+6uuIHmrxXUDm8URgCvQQLLb14hJi7pdOdPBgB37waScI8hQAguj0YuDzpH8m7QXA7PKDWnDwSlT286PPfPDDlqzs6Ypc81bGqO5uE67xHOAm9c7KDvEEUjTujd0m68AoGPQLv7DvFoOk8Kcm3POqTJbt8u4c7MMeivA4IMDwabn45HtFjPPsrtjytZ7I8Hvi8vFjS1DwAt1e8asedu10vTzzAQia9Rr0evP8tF71atAM9FjUnPJXWczxnHwO8SBGDvJGK37xQz6y7KZVLvOKZdTxlxu08c6isu6rqHTyAD3C7CEIxvIM9DDwympm87RQjvbRKd7wKRCo8jKAfvOs9Srunzu47ZI6jvEKh7rs8Gyg71jW5vFRiHTyLtzo8Iz0aPO72hby6WZ88xyRlvARlGzystIE6ewpHPAVVxTxXaom83bshvNkRwzzsQXG8TQ1YPIYtwbvcw1q8bkzBPLKVYzx5hai8H5TMvK18KTwrwGm86fdLPAySQbvyCYi89xKQu8gwy7n6Ux07908hPa6BjrwQWt+8tIFmPE0zxruq52G8/NBlPGoqg7x9tdy89drJPA1c3bx3vLq8RNSHuT4hN7w8whW7gj9QuxlhqLngUuo8N0RTvDqE7zuXzKo7SkISvRy+GDw+vXi8iTONt+KhU7tZ7Hq6ePM/vJBGAL0ClbY8tQfTPBIvTjzi1EQ71iALvHh35rslTgM9OsQVPc8swTz32Ig6GV2ovMaEOTsNGxO8mKeUO+F4hjxKpas8yEMMPBoATrz37MO8fkyRO0lucrz8AqO8eMYgvJolRjwoqgM9Oo+cvCfLhbuwWPO8t9amPMidMDwp6B88fUESvMAqv7znM6g79Ywuvf6pXzyXpim7/+5ku0LJYbw/phw8wzWSvL0rkjxILng8YR/ouxmyLLxQ8+E7jvIHPfienjpmCt87pLIyvJD2gDowno08+7+XPD3YSbuq/Ow7uIUuOn/LPDxcEKI7Vm9VPJDBOjxpKgY9lTqkO2+fibx+NMc8ZDyAvAEhijyCMPE8KUeVPNQaELyy2LG7hZHsO9qqqrzrVKI8K62jPE1kn7z8bFi8zGmCPIL1jTwWEJA8VVbHuZcDOLyECai8Z0rLPMdBfzzaKju9oNFDu5qUdTyp4V+8aJLiOkWV0zqo/yy6LxcdPETGZ7wZp5y7qok4vLHk6Ds47oe7unUTPefaADslo6w8VeWLPHhZOzl5fC+8w/pTPLiqy7wdeWY8UMWIOxdViLzc5YI8Lz7ZvCgx3jzWj/G7HxEhvBvg17xMHC+8XmiGPIDYf7z/DAk9G564O8pq4Dt3XcE7qKaCOwdWejx/irQ8SWakuyS7ozyEE0K8lcSgPGkeLjzBl8K7UUaFvJ3Igru0aw48856Pu2CYZDtkV7o8aWV4PH8Ouzs9ULe83c+nPCmA6LhyF5U66hL+Oxab3rnz8pM7jMSEuqhunbydcwK9Qn6FuyFePLyTkL47V7V/vHxaxLsflx67AOqvvGXHQjyyAb27D1tMvOgiaTzwOBg8+vIfvAtdRLz76hg7+AVRPLTL0DwUxYw7sAUZOg== + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 2 + total_tokens: 2 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '48880' content-type: - application/json host: @@ -2700,9 +10607,15 @@ interactions: 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`. + Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer. - 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. + Chunk IDs come from two places: + - The `chunk_id` field on `search` / `await search(...)` results + - The `chunk_ids` field on `items.jsonl` rows (when you ground via direct file reads) + + Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs. + + Every answer that uses search or file-read evidence must be backed by `cite`. ## Document Filesystem (inside execute_code) @@ -2716,7 +10629,7 @@ interactions: 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`. + `{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 and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora. ### Reading files Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported). @@ -2747,27 +10660,29 @@ interactions: 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" + Structured document items. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file. + + Each row carries: + - `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results + - `label`: item type — one of `"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. + - `chunk_ids`: chunks that contain this item — pass to `cite()` to ground an answer that read this item directly + - `heading_level`: H-level for `section_header` rows; `0` on non-header rows ### 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. + Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `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. + Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in `items.jsonl`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index. ## 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. + 4. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents. + 5. 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. @@ -2793,139 +10708,405 @@ interactions: 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 list all different document element types mentioned. Likely the document element types are the labels defined in items.jsonl: "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote". But there might be other types in other documents. The question is to search for content about document element types or labels. So we need to search the knowledge base for references to "document element types" or "labels" or "element types" or "document element types" etc. Then gather all distinct types mentioned. - 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 produce a list of all different document element types mentioned. We need to cite the chunk IDs that support each type. We need to gather all types across the corpus. The items.jsonl labels are a set of known types. But there might be other types like "section_header" etc. The question might be expecting the list of types from the items.jsonl. But the question says "Search for content about document element types or labels." That might refer to the items.jsonl labels. So we need to search for "label" or "document element types" across the knowledge base. But we need to list all different document element types mentioned. So we need to find all distinct labels across all documents. That might be the same set as the items.jsonl label values. But there might be other labels like "annotation" etc. We need to check. - 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 should search for "label" or "document element types" or "element type" or "document element type" or "document element types" or "document element label" etc. But we need to find all types. We can search for "label:" or "label" or "type:" or "element type" etc. But we need to be careful to capture all distinct types. - We can use the search tool. Let's search for "document element types" first. + We can use search with query "label" maybe. But that might return many results. We can also search for "label:" or "label" but that might return many. We can search for "label" and "document" maybe. But we can also search for "label" and "items.jsonl" but we don't have that. + + Alternatively, we can read all items.jsonl files across all documents and extract the label field. That would give us all types. That might be more reliable. But the question says "Search for content about document element types or labels." So we might need to search the knowledge base for references to "document element types" or "labels" and then list them. But we can also read items.jsonl to get the labels. But the question might be expecting the list of types from items.jsonl. But we need to provide citations. We can cite the chunk IDs that contain the labels. But if we read items.jsonl, we can get the labels but we need to cite the chunk IDs that contain those labels. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide citations for each type. We can gather chunk IDs for each type. But we might need to gather many chunk IDs. But we can just cite a few chunk IDs that mention each type. But we need to ensure we cite at least one chunk ID that contains the type. We can gather all types and then cite a representative chunk ID for each type. But we need to ensure we cite the chunk IDs that mention the type. We can use search to find chunk IDs that contain the type names. But we might need to gather all types. + + We can use execute_code to list all items.jsonl across all documents and extract the label values. Then we can produce a list of unique labels. Then we can search for each label to get chunk IDs that mention them. But we can also use the items.jsonl chunk IDs themselves. But we need to cite chunk IDs that ground the answer. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide a list of chunk IDs. But we might need to provide chunk IDs for each type. But we can just provide a single chunk ID that contains each type. But we need to ensure that the chunk ID indeed contains the type. We can search for each type to get a chunk ID. But we can also use the items.jsonl chunk IDs. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we can use them. + + We need to produce a final answer: a list of all different document element types mentioned. We need to cite chunk IDs that support each type. We need to call cite before writing the final answer. We need to gather chunk IDs for each type. + + We need to decide how to gather the types. We can use execute_code to read all items.jsonl across all documents and extract unique labels. Then we can produce a list of labels. Then we can search for each label to get chunk IDs. But we might also use the items.jsonl chunk IDs directly. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we need to confirm. + + We can do the following: + + 1. Use execute_code to list all documents and read items.jsonl for each document. For each item, collect the label. Build a set of unique labels. + + 2. Then for each label, we can search for that label to get chunk IDs. But we might also use the items.jsonl chunk IDs. But we might need to use search to get chunk IDs that mention the label. But we can also use the items.jsonl chunk IDs. But we need to ensure we have chunk IDs that mention the label. We can use the items.jsonl chunk IDs. But we need to confirm that the items.jsonl chunk IDs are indeed chunk IDs that we can cite. The items.jsonl chunk IDs are the chunk IDs for each item. But we need to confirm that the items.jsonl chunk IDs are the same as the chunk IDs we can cite. The items.jsonl chunk IDs are part of the items.jsonl rows. They are used for cross-referencing. They are chunk IDs that we can cite. So we can use them. + + Thus we can gather a mapping from label to a sample chunk ID that contains that label. Then we can call cite with the list of chunk IDs for all labels. Then we can produce the answer. + + We need to be careful: The items.jsonl chunk IDs are unique across the entire corpus. They are used for cross-referencing. So we can gather them. + + We need to produce the answer: "section_header, text, table, list_item, caption, formula, picture, code, footnote" maybe. But there might be other labels like "annotation" or "definition" etc. But we need to check. + + We need to search for all labels. Let's do that. + + We can use execute_code to list all documents and read items.jsonl. Let's do that. + + We'll write code to: + + - import Path, json + - iterate over /documents + - for each doc, read items.jsonl + - parse each line as JSON + - collect label + - also collect chunk_id for each label + - produce a mapping from label to list of chunk_ids + - then produce a set of labels + - then produce a mapping from label to a sample chunk_id + + We need to output the mapping. We'll use print(). + + But we need to be careful: The items.jsonl might be large. But we can handle it. + + We need to ensure we use Path.read_text() and splitlines. + + We'll do: + + ``` + from pathlib import Path + import json + + label_to_chunk = {} + for doc_dir in Path('/documents').iterdir(): + items_file = doc_dir / 'items.jsonl' + if not items_file.exists(): + continue + for line in items_file.read_text().splitlines(): + if not line.strip(): + continue + item = json.loads(line) + label = item.get('label') + chunk_id = item.get('chunk_ids', [None])[0] if item.get('chunk_ids') else None + if label and chunk_id: + if label not in label_to_chunk: + label_to_chunk[label] = chunk_id + ``` + + But we need to handle chunk_ids: It might be a list of chunk IDs. We can take the first chunk ID. + + But we need to confirm that each item has chunk_ids. It might have multiple chunk_ids. But we can pick the first. + + Then we can produce a list of labels and chunk IDs. + + We need to output them in a format that we can parse. + + We can print a JSON mapping or just print each label and chunk id. + + We need to capture the output. + + We need to call execute_code with this code. + + Let's do that. role: assistant tool_calls: - function: - arguments: '{"query":"document element types","limit":10}' - name: search - id: call_539fpdo9 + arguments: '{"code":"from pathlib import Path\nimport json\nlabel_to_chunk = {}\nfor doc_dir in Path(''/documents'').iterdir():\n items_file + = doc_dir / ''items.jsonl''\n if not items_file.exists():\n continue\n for line in items_file.read_text().splitlines():\n if + not line.strip():\n continue\n try:\n item = json.loads(line)\n except Exception + as e:\n continue\n label = item.get(''label'')\n chunk_ids = item.get(''chunk_ids'')\n if + label and chunk_ids:\n # take first chunk id\n chunk_id = chunk_ids[0]\n if + label not in label_to_chunk:\n label_to_chunk[label] = chunk_id\n# Print mapping\nprint(json.dumps(label_to_chunk, + indent=2))\n"}' + name: execute_code + id: call_qd0d5ug0 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/ + - content: | + { + "text": "713678f9-980e-47ed-9e1c-e6b533231d57", + "caption": "713678f9-980e-47ed-9e1c-e6b533231d57", + "footnote": "73a2443c-3c25-4fbb-a6d4-0dec371ef104" + } role: tool - tool_call_id: call_539fpdo9 + tool_call_id: call_qd0d5ug0 - 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." + The output shows only three labels: "text", "caption", "footnote". That seems incomplete. Maybe the items.jsonl files only contain these labels? That seems unlikely. Maybe the items.jsonl files are not present or empty. Let's check the structure. - 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. + We need to inspect the /documents directory. Let's list the directories. role: assistant tool_calls: - function: - arguments: '{"limit":10,"query":"class label"}' - name: search - id: call_f9c6zlic + arguments: '{"code":"from pathlib import Path\nimport json\n# List directories\nprint([d.name for d in Path(''/documents'').iterdir()])\n"}' + name: execute_code + id: call_b60xwvbx 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/ + - content: | + ['40fa427f-7d9c-499f-9985-ce7f51d24db4'] role: tool - tool_call_id: call_f9c6zlic + tool_call_id: call_b60xwvbx + - content: null + reasoning: 'Only one document directory: ''40fa427f-7d9c-499f-9985-ce7f51d24db4''. Let''s inspect its contents.' + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\npath = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4'')\nprint(''Files:'', + [f.name for f in path.iterdir()])\nprint(''metadata:'', json.loads((path/''metadata.json'').read_text()))\n"}' + name: execute_code + id: call_rt6l2me5 + type: function + - content: | + Files: ['metadata.json', 'content.txt', 'items.jsonl', 'toc.json'] + metadata: {'id': '40fa427f-7d9c-499f-9985-ce7f51d24db4', 'title': None, 'uri': 'file:///private/var/folders/4k/n24fyvf11117vpdnpngx290r0000gn/T/pytest-of-ggozad/pytest-106/doclaynet0/page0.pdf', 'created_at': '2026-05-25 12:47:13.296904'} + role: tool + tool_call_id: call_rt6l2me5 + - content: null + reasoning: The metadata shows title None. The file is a PDF. The items.jsonl might be large. Let's inspect the first + few lines. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nitems_file = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl'')\nprint(''Size:'', + items_file.stat().st_size)\nprint(''First 20 lines:'')\nfor i, line in enumerate(items_file.read_text().splitlines()[:20]):\n print(i, + line)\n"}' + name: execute_code + id: call_nvweqb8s + type: function + - content: | + Size: 6660 + First 20 lines: + 0 {"self_ref": "#/texts/1", "label": "section_header", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 1 {"self_ref": "#/texts/2", "label": "text", "text": "Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 2 {"self_ref": "#/texts/3", "label": "text", "text": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 3 {"self_ref": "#/texts/4", "label": "text", "text": "Ahmed S. Nassar IBM Research", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 4 {"self_ref": "#/texts/5", "label": "text", "text": "Rueschlikon, Switzerland ahn@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 5 {"self_ref": "#/texts/6", "label": "text", "text": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 6 {"self_ref": "#/texts/7", "label": "text", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 7 {"self_ref": "#/pictures/0", "label": "picture", "text": "Figure 1: Four examples of complex page layouts across different document categories", "page_numbers": [1], "heading_level": 0, "chunk_ids": []} + 8 {"self_ref": "#/texts/8", "label": "caption", "text": "Figure 1: Four examples of complex page layouts across different document categories", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 9 {"self_ref": "#/texts/312", "label": "section_header", "text": "KEYWORDS", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 10 {"self_ref": "#/texts/313", "label": "text", "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["92e8dd8b-171e-4933-a0f1-16f5ce6ac545"]} + 11 {"self_ref": "#/texts/314", "label": "section_header", "text": "ACMReference Format:", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 12 {"self_ref": "#/texts/315", "label": "text", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["93ad7de1-8b78-4585-9865-01058d67019d"]} + 13 {"self_ref": "#/texts/316", "label": "section_header", "text": "ABSTRACT", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 14 {"self_ref": "#/texts/317", "label": "text", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["c0d1d300-462b-445b-8421-6a09606a2c9b", "912809c6-deb1-4a1f-91e8-ef3eadfeb852"]} + 15 {"self_ref": "#/texts/318", "label": "section_header", "text": "CCS CONCEPTS", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 16 {"self_ref": "#/texts/319", "label": "text", "text": "· Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ;", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 17 {"self_ref": "#/texts/320", "label": "text", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 18 {"self_ref": "#/texts/321", "label": "text", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 19 {"self_ref": "#/texts/322", "label": "footnote", "text": "© 2022 Copyright held by the owner/author(s).", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + role: tool + tool_call_id: call_nvweqb8s + - content: null + reasoning: Scrolling further might reveal more labels. Let's inspect the rest of the file. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nitems_file = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl'')\nlines + = items_file.read_text().splitlines()\nprint(''Total lines:'', len(lines))\n# Find unique labels\nlabels = set()\nfor + line in lines:\n if not line.strip():\n continue\n item = json.loads(line)\n labels.add(item.get(''label''))\nprint(''Unique + labels:'', labels)\n# Show some lines with each label\nfor label in labels:\n print(''---'', label)\n for + line in lines:\n item = json.loads(line)\n if item.get(''label'') == label:\n print(item)\n break\n"}' + name: execute_code + id: call_c2sddzkn + type: function + - content: | + Total lines: 22 + Unique labels: {'section_header', 'text', 'picture', 'caption', 'footnote'} + --- section_header + {'self_ref': '#/texts/1', 'label': 'section_header', 'text': 'DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis', 'page_numbers': [1], 'heading_level': 1, 'chunk_ids': []} + --- text + {'self_ref': '#/texts/2', 'label': 'text', 'text': 'Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['713678f9-980e-47ed-9e1c-e6b533231d57']} + --- picture + {'self_ref': '#/pictures/0', 'label': 'picture', 'text': 'Figure 1: Four examples of complex page layouts across different document categories', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': []} + --- caption + {'self_ref': '#/texts/8', 'label': 'caption', 'text': 'Figure 1: Four examples of complex page layouts across different document categories', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['713678f9-980e-47ed-9e1c-e6b533231d57']} + --- footnote + {'self_ref': '#/texts/322', 'label': 'footnote', 'text': '© 2022 Copyright held by the owner/author(s).', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['73a2443c-3c25-4fbb-a6d4-0dec371ef104']} + role: tool + tool_call_id: call_c2sddzkn - 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." + So the only labels present in this document are: section_header, text, picture, caption, footnote. There are no other labels like list_item, table, formula, code, etc. So maybe the corpus only contains this one document. But the question might refer to "document element types or labels" across the knowledge base. The knowledge base might contain only this one document. But the question might be generic. But we need to answer based on the knowledge base. So we should list the types present in the items.jsonl of this document. - 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. + Thus the answer: section_header, text, picture, caption, footnote. - 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 need to cite chunk IDs that support each type. We can use the chunk IDs from the items.jsonl. For example: - We should call cite with chunk_ids: ["b79aaef6-707e-4840-b3d0-b9941fd1b9d6"]. + - section_header: chunk_ids empty? The items for section_header have chunk_ids empty. But we can still cite the chunk ID? Actually chunk_ids empty means no chunk ID. But we can still cite the item itself? But we need to provide chunk IDs. The items.jsonl chunk_ids for section_header are empty. But we need to provide chunk IDs that ground the answer. We can use the chunk IDs from other items that mention the type. But we need to provide chunk IDs that mention the type. But we can use the chunk IDs from the items that have chunk_ids. For example: - Then answer: The document element types mentioned are: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. + - For "text": chunk_id "713678f9-980e-47ed-9e1c-e6b533231d57" appears in many text items. - We should mention that these are the 11 distinct class labels. Provide the list. That should satisfy the question. + - For "picture": chunk_ids empty. But we can still cite the item itself? But we need chunk IDs. But we can use the chunk ID from the caption item? But the caption item has chunk_id "713678f9-980e-47ed-9e1c-e6b533231d57". But that is a caption, not a picture. But we can still cite that chunk ID to support the presence of a picture? But the picture item has chunk_ids empty. But we can still cite the chunk ID from the caption item to support the presence of a picture? But the caption item is a separate label. But we might need to cite a chunk ID that mentions the picture label. But we don't have one. But we can still cite the chunk ID from the picture item? But the picture item has chunk_ids empty. But we can still cite the chunk ID from the caption item? But that might not be correct. We need to provide chunk IDs that ground the answer. The answer is "section_header, text, picture, caption, footnote". We need to provide citations for each type. But we can provide a single chunk ID that covers multiple types? But we need to provide chunk IDs that ground each type. But we can provide a chunk ID that contains the text of the item that has the label. For example, we can provide the chunk ID "713678f9-980e-47ed-9e1c-e6b533231d57" for the text items. But we also need a chunk ID for the picture item. But the picture item has no chunk ID. But we can still cite the chunk ID from the caption item? But that might not be correct. But we can also cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But that might be okay because the caption item is associated with the picture. But the question is about "document element types or labels". So we need to list the types. We can cite chunk IDs that mention each type. For the picture type, we can cite the chunk ID from the caption item? But that might not be ideal. But we can also cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But maybe we can cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But that might be okay. But we need to ensure we provide a chunk ID that is present in the items.jsonl. The caption item has chunk_id "713678f9-980e-47ed-9e1c-e6b533231d57". That chunk ID appears in many text items. But we can still cite it to support the presence of a caption. But for picture, we might need a chunk ID that mentions the picture label. But we don't have a chunk ID. But we can still cite the chunk ID from the caption item? But that might not mention the picture label. But the caption item has label "caption". But we need to support the picture type. But we can still cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But that might not mention the picture label. But we can still cite the chunk ID from the picture item? But it's empty. But we might need to find a chunk ID that mentions the picture label. But we can search for "picture" in the items.jsonl. But the picture item has no chunk_id. But we can search for "picture" in the content.txt. But that might produce a chunk ID. But the content.txt might have chunk IDs. But we can search for "picture" in the content.txt. But we can use search to find a chunk ID that contains the word "picture". But we need to ensure that the chunk ID is from the content.txt. But we can use search to find the chunk ID that contains the word "picture". But we need to confirm that the chunk ID is present. But we can do that. - 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. + Let's search for "picture" in the knowledge base. role: assistant tool_calls: - function: - arguments: '{"chunk_ids":["b79aaef6-707e-4840-b3d0-b9941fd1b9d6"]}' - name: cite - id: call_bevluili + arguments: '{"query":"picture","limit":10}' + name: search + id: call_1atkyf5a type: function - - content: Registered 1 citation(s). + - content: |- + [93ad7de1-8b78-4585-9865-01058d67019d] [rank 1 of 1] + Source: ACMReference Format: > KEYWORDS > CCS CONCEPTS > ABSTRACT > DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + Type: picture + Content: + DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + + Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com + + Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com + + Ahmed S. Nassar IBM Research + + Rueschlikon, Switzerland ahn@zurich.ibm.com + + Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com + + Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com + + Figure 1: Four examples of complex page layouts across different document categories + + Figure 1: Four examples of complex page layouts across different document categories + + KEYWORDS + + PDF document conversion, layout segmentation, object-detection, data set, Machine Learning + + ACMReference Format: + + Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 + + ABSTRACT + + Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. + + CCS CONCEPTS + + · Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ; + + Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). + + KDD '22, August 14-18, 2022, Washington, DC, USA + + ACM ISBN 978-1-4503-9385-0/22/08. + + https://doi.org/10.1145/3534678.3539043 role: tool - tool_call_id: call_bevluili + tool_call_id: call_1atkyf5a + - content: null + reasoning: |- + The search result shows a chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" of type picture. That chunk contains the entire content. It includes the picture label? Actually the content includes the text of the document, but the type is picture. But the chunk ID is for the picture item. So we can use that chunk ID to cite the picture type. + + Now we need to find chunk IDs for the other types: section_header, text, caption, footnote. + + We can search for "section_header" or "section header" or "section_header" etc. But we can also search for "section_header" as a label. But we can search for "section_header" in the knowledge base. Let's search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":10,"query":"section_header"}' + name: search + id: call_6uhchwxo + type: function + - content: |- + [93ad7de1-8b78-4585-9865-01058d67019d] [rank 1 of 1] + Source: ACMReference Format: > KEYWORDS > ABSTRACT > DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis > CCS CONCEPTS + Type: picture + Content: + DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + + Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com + + Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com + + Ahmed S. Nassar IBM Research + + Rueschlikon, Switzerland ahn@zurich.ibm.com + + Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com + + Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com + + Figure 1: Four examples of complex page layouts across different document categories + + Figure 1: Four examples of complex page layouts across different document categories + + KEYWORDS + + PDF document conversion, layout segmentation, object-detection, data set, Machine Learning + + ACMReference Format: + + Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 + + ABSTRACT + + Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. + + CCS CONCEPTS + + · Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ; + + Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). + + KDD '22, August 14-18, 2022, Washington, DC, USA + + ACM ISBN 978-1-4503-9385-0/22/08. + + https://doi.org/10.1145/3534678.3539043 + role: tool + tool_call_id: call_6uhchwxo + - content: null + reasoning: The search result for "section_header" returns the same chunk ID as picture. That indicates that the search + index might not differentiate by label. It might treat the content as a whole. So we might not get separate chunk + IDs for each label. But we can still use the chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" to cite the presence + of section_header, text, picture, caption, footnote. But we might want to provide separate chunk IDs for each type. + But we can use the same chunk ID for all types, but that might not be ideal. But we can still cite it. But we might + also want to provide chunk IDs for each type separately. But we can search for "caption" and "footnote" etc. But + the search might return the same chunk ID again. Let's search for "caption". + role: assistant + tool_calls: + - function: + arguments: '{"limit":10,"query":"caption"}' + name: search + id: call_3cw2bnun + type: function + - content: |- + [93ad7de1-8b78-4585-9865-01058d67019d] [rank 1 of 1] + Source: ACMReference Format: > DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis > KEYWORDS > ABSTRACT > CCS CONCEPTS + Type: picture + Content: + DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + + Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com + + Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com + + Ahmed S. Nassar IBM Research + + Rueschlikon, Switzerland ahn@zurich.ibm.com + + Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com + + Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com + + Figure 1: Four examples of complex page layouts across different document categories + + Figure 1: Four examples of complex page layouts across different document categories + + KEYWORDS + + PDF document conversion, layout segmentation, object-detection, data set, Machine Learning + + ACMReference Format: + + Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 + + ABSTRACT + + Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. + + CCS CONCEPTS + + · Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ; + + Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). + + KDD '22, August 14-18, 2022, Washington, DC, USA + + ACM ISBN 978-1-4503-9385-0/22/08. + + https://doi.org/10.1145/3534678.3539043 + role: tool + tool_call_id: call_3cw2bnun model: gpt-oss reasoning_effort: high stream: true @@ -2985,14 +11166,15 @@ interactions: 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. + Accepts chunk_ids from search results AND from direct file reads + (items.jsonl, toc.json). Verbatim copies only — chunk_ids that + don't exist in the database trigger a retry. name: cite parameters: additionalProperties: false properties: chunk_ids: - description: List of chunk_id values from search results. + description: List of chunk_id values from search results or VFS reads. items: type: string type: array @@ -3004,113 +11186,7031 @@ interactions: response: body: string: |+ - data: {"id":"chatcmpl-894","object":"chat.completion.chunk","created":1779176286,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"The"},"finish_reason":null}]} + data: {"id":"chatcmpl-112","object":"chat.completion.chunk","created":1779702574,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"The"},"finish_reason":null}]} - data: 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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. + + Chunk IDs come from two places: + - The `chunk_id` field on `search` / `await search(...)` results + - The `chunk_ids` field on `items.jsonl` rows (when you ground via direct file reads) + + Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs. + + Every answer that uses search or file-read evidence must be backed by `cite`. + + ## 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 and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora. + + ### 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. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file. + + Each row carries: + - `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results + - `label`: item type — one of `"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 + - `chunk_ids`: chunks that contain this item — pass to `cite()` to ground an answer that read this item directly + - `heading_level`: H-level for `section_header` rows; `0` on non-header rows + + ### toc.json + Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `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`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index. + + ## 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. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents. + 5. 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 list all different document element types mentioned. Likely the document element types are the labels defined in items.jsonl: "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote". But there might be other types in other documents. The question is to search for content about document element types or labels. So we need to search the knowledge base for references to "document element types" or "labels" or "element types" or "document element types" etc. Then gather all distinct types mentioned. + + We need to produce a list of all different document element types mentioned. We need to cite the chunk IDs that support each type. We need to gather all types across the corpus. The items.jsonl labels are a set of known types. But there might be other types like "section_header" etc. The question might be expecting the list of types from the items.jsonl. But the question says "Search for content about document element types or labels." That might refer to the items.jsonl labels. So we need to search for "label" or "document element types" across the knowledge base. But we need to list all different document element types mentioned. So we need to find all distinct labels across all documents. That might be the same set as the items.jsonl label values. But there might be other labels like "annotation" etc. We need to check. + + We should search for "label" or "document element types" or "element type" or "document element type" or "document element types" or "document element label" etc. But we need to find all types. We can search for "label:" or "label" or "type:" or "element type" etc. But we need to be careful to capture all distinct types. + + We can use search with query "label" maybe. But that might return many results. We can also search for "label:" or "label" but that might return many. We can search for "label" and "document" maybe. But we can also search for "label" and "items.jsonl" but we don't have that. + + Alternatively, we can read all items.jsonl files across all documents and extract the label field. That would give us all types. That might be more reliable. But the question says "Search for content about document element types or labels." So we might need to search the knowledge base for references to "document element types" or "labels" and then list them. But we can also read items.jsonl to get the labels. But the question might be expecting the list of types from items.jsonl. But we need to provide citations. We can cite the chunk IDs that contain the labels. But if we read items.jsonl, we can get the labels but we need to cite the chunk IDs that contain those labels. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide citations for each type. We can gather chunk IDs for each type. But we might need to gather many chunk IDs. But we can just cite a few chunk IDs that mention each type. But we need to ensure we cite at least one chunk ID that contains the type. We can gather all types and then cite a representative chunk ID for each type. But we need to ensure we cite the chunk IDs that mention the type. We can use search to find chunk IDs that contain the type names. But we might need to gather all types. + + We can use execute_code to list all items.jsonl across all documents and extract the label values. Then we can produce a list of unique labels. Then we can search for each label to get chunk IDs that mention them. But we can also use the items.jsonl chunk IDs themselves. But we need to cite chunk IDs that ground the answer. We can use the chunk IDs from items.jsonl. But we need to register them with cite. But we need to provide a list of chunk IDs. But we might need to provide chunk IDs for each type. But we can just provide a single chunk ID that contains each type. But we need to ensure that the chunk ID indeed contains the type. We can search for each type to get a chunk ID. But we can also use the items.jsonl chunk IDs. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we can use them. + + We need to produce a final answer: a list of all different document element types mentioned. We need to cite chunk IDs that support each type. We need to call cite before writing the final answer. We need to gather chunk IDs for each type. + + We need to decide how to gather the types. We can use execute_code to read all items.jsonl across all documents and extract unique labels. Then we can produce a list of labels. Then we can search for each label to get chunk IDs. But we might also use the items.jsonl chunk IDs directly. But we need to ensure we cite chunk IDs that mention the type. The items.jsonl chunk IDs might be the same as the chunk IDs in the search results. But we need to confirm. + + We can do the following: + + 1. Use execute_code to list all documents and read items.jsonl for each document. For each item, collect the label. Build a set of unique labels. + + 2. Then for each label, we can search for that label to get chunk IDs. But we might also use the items.jsonl chunk IDs. But we might need to use search to get chunk IDs that mention the label. But we can also use the items.jsonl chunk IDs. But we need to ensure we have chunk IDs that mention the label. We can use the items.jsonl chunk IDs. But we need to confirm that the items.jsonl chunk IDs are indeed chunk IDs that we can cite. The items.jsonl chunk IDs are the chunk IDs for each item. But we need to confirm that the items.jsonl chunk IDs are the same as the chunk IDs we can cite. The items.jsonl chunk IDs are part of the items.jsonl rows. They are used for cross-referencing. They are chunk IDs that we can cite. So we can use them. + + Thus we can gather a mapping from label to a sample chunk ID that contains that label. Then we can call cite with the list of chunk IDs for all labels. Then we can produce the answer. + + We need to be careful: The items.jsonl chunk IDs are unique across the entire corpus. They are used for cross-referencing. So we can gather them. + + We need to produce the answer: "section_header, text, table, list_item, caption, formula, picture, code, footnote" maybe. But there might be other labels like "annotation" or "definition" etc. But we need to check. + + We need to search for all labels. Let's do that. + + We can use execute_code to list all documents and read items.jsonl. Let's do that. + + We'll write code to: + + - import Path, json + - iterate over /documents + - for each doc, read items.jsonl + - parse each line as JSON + - collect label + - also collect chunk_id for each label + - produce a mapping from label to list of chunk_ids + - then produce a set of labels + - then produce a mapping from label to a sample chunk_id + + We need to output the mapping. We'll use print(). + + But we need to be careful: The items.jsonl might be large. But we can handle it. + + We need to ensure we use Path.read_text() and splitlines. + + We'll do: + + ``` + from pathlib import Path + import json + + label_to_chunk = {} + for doc_dir in Path('/documents').iterdir(): + items_file = doc_dir / 'items.jsonl' + if not items_file.exists(): + continue + for line in items_file.read_text().splitlines(): + if not line.strip(): + continue + item = json.loads(line) + label = item.get('label') + chunk_id = item.get('chunk_ids', [None])[0] if item.get('chunk_ids') else None + if label and chunk_id: + if label not in label_to_chunk: + label_to_chunk[label] = chunk_id + ``` + + But we need to handle chunk_ids: It might be a list of chunk IDs. We can take the first chunk ID. + + But we need to confirm that each item has chunk_ids. It might have multiple chunk_ids. But we can pick the first. + + Then we can produce a list of labels and chunk IDs. + + We need to output them in a format that we can parse. + + We can print a JSON mapping or just print each label and chunk id. + + We need to capture the output. + + We need to call execute_code with this code. + + Let's do that. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nlabel_to_chunk = {}\nfor doc_dir in Path(''/documents'').iterdir():\n items_file + = doc_dir / ''items.jsonl''\n if not items_file.exists():\n continue\n for line in items_file.read_text().splitlines():\n if + not line.strip():\n continue\n try:\n item = json.loads(line)\n except Exception + as e:\n continue\n label = item.get(''label'')\n chunk_ids = item.get(''chunk_ids'')\n if + label and chunk_ids:\n # take first chunk id\n chunk_id = chunk_ids[0]\n if + label not in label_to_chunk:\n label_to_chunk[label] = chunk_id\n# Print mapping\nprint(json.dumps(label_to_chunk, + indent=2))\n"}' + name: execute_code + id: call_qd0d5ug0 + type: function + - content: | + { + "text": "713678f9-980e-47ed-9e1c-e6b533231d57", + "caption": "713678f9-980e-47ed-9e1c-e6b533231d57", + "footnote": "73a2443c-3c25-4fbb-a6d4-0dec371ef104" + } + role: tool + tool_call_id: call_qd0d5ug0 + - content: null + reasoning: |- + The output shows only three labels: "text", "caption", "footnote". That seems incomplete. Maybe the items.jsonl files only contain these labels? That seems unlikely. Maybe the items.jsonl files are not present or empty. Let's check the structure. + + We need to inspect the /documents directory. Let's list the directories. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\n# List directories\nprint([d.name for d in Path(''/documents'').iterdir()])\n"}' + name: execute_code + id: call_b60xwvbx + type: function + - content: | + ['40fa427f-7d9c-499f-9985-ce7f51d24db4'] + role: tool + tool_call_id: call_b60xwvbx + - content: null + reasoning: 'Only one document directory: ''40fa427f-7d9c-499f-9985-ce7f51d24db4''. Let''s inspect its contents.' + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\npath = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4'')\nprint(''Files:'', + [f.name for f in path.iterdir()])\nprint(''metadata:'', json.loads((path/''metadata.json'').read_text()))\n"}' + name: execute_code + id: call_rt6l2me5 + type: function + - content: | + Files: ['metadata.json', 'content.txt', 'items.jsonl', 'toc.json'] + metadata: {'id': '40fa427f-7d9c-499f-9985-ce7f51d24db4', 'title': None, 'uri': 'file:///private/var/folders/4k/n24fyvf11117vpdnpngx290r0000gn/T/pytest-of-ggozad/pytest-106/doclaynet0/page0.pdf', 'created_at': '2026-05-25 12:47:13.296904'} + role: tool + tool_call_id: call_rt6l2me5 + - content: null + reasoning: The metadata shows title None. The file is a PDF. The items.jsonl might be large. Let's inspect the first + few lines. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nitems_file = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl'')\nprint(''Size:'', + items_file.stat().st_size)\nprint(''First 20 lines:'')\nfor i, line in enumerate(items_file.read_text().splitlines()[:20]):\n print(i, + line)\n"}' + name: execute_code + id: call_nvweqb8s + type: function + - content: | + Size: 6660 + First 20 lines: + 0 {"self_ref": "#/texts/1", "label": "section_header", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 1 {"self_ref": "#/texts/2", "label": "text", "text": "Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 2 {"self_ref": "#/texts/3", "label": "text", "text": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 3 {"self_ref": "#/texts/4", "label": "text", "text": "Ahmed S. Nassar IBM Research", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 4 {"self_ref": "#/texts/5", "label": "text", "text": "Rueschlikon, Switzerland ahn@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 5 {"self_ref": "#/texts/6", "label": "text", "text": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 6 {"self_ref": "#/texts/7", "label": "text", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 7 {"self_ref": "#/pictures/0", "label": "picture", "text": "Figure 1: Four examples of complex page layouts across different document categories", "page_numbers": [1], "heading_level": 0, "chunk_ids": []} + 8 {"self_ref": "#/texts/8", "label": "caption", "text": "Figure 1: Four examples of complex page layouts across different document categories", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["713678f9-980e-47ed-9e1c-e6b533231d57"]} + 9 {"self_ref": "#/texts/312", "label": "section_header", "text": "KEYWORDS", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 10 {"self_ref": "#/texts/313", "label": "text", "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["92e8dd8b-171e-4933-a0f1-16f5ce6ac545"]} + 11 {"self_ref": "#/texts/314", "label": "section_header", "text": "ACMReference Format:", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 12 {"self_ref": "#/texts/315", "label": "text", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["93ad7de1-8b78-4585-9865-01058d67019d"]} + 13 {"self_ref": "#/texts/316", "label": "section_header", "text": "ABSTRACT", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 14 {"self_ref": "#/texts/317", "label": "text", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["c0d1d300-462b-445b-8421-6a09606a2c9b", "912809c6-deb1-4a1f-91e8-ef3eadfeb852"]} + 15 {"self_ref": "#/texts/318", "label": "section_header", "text": "CCS CONCEPTS", "page_numbers": [1], "heading_level": 1, "chunk_ids": []} + 16 {"self_ref": "#/texts/319", "label": "text", "text": "· Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ;", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 17 {"self_ref": "#/texts/320", "label": "text", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 18 {"self_ref": "#/texts/321", "label": "text", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + 19 {"self_ref": "#/texts/322", "label": "footnote", "text": "© 2022 Copyright held by the owner/author(s).", "page_numbers": [1], "heading_level": 0, "chunk_ids": ["73a2443c-3c25-4fbb-a6d4-0dec371ef104"]} + role: tool + tool_call_id: call_nvweqb8s + - content: null + reasoning: Scrolling further might reveal more labels. Let's inspect the rest of the file. + role: assistant + tool_calls: + - function: + arguments: '{"code":"from pathlib import Path\nimport json\nitems_file = Path(''/documents/40fa427f-7d9c-499f-9985-ce7f51d24db4/items.jsonl'')\nlines + = items_file.read_text().splitlines()\nprint(''Total lines:'', len(lines))\n# Find unique labels\nlabels = set()\nfor + line in lines:\n if not line.strip():\n continue\n item = json.loads(line)\n labels.add(item.get(''label''))\nprint(''Unique + labels:'', labels)\n# Show some lines with each label\nfor label in labels:\n print(''---'', label)\n for + line in lines:\n item = json.loads(line)\n if item.get(''label'') == label:\n print(item)\n break\n"}' + name: execute_code + id: call_c2sddzkn + type: function + - content: | + Total lines: 22 + Unique labels: {'section_header', 'text', 'picture', 'caption', 'footnote'} + --- section_header + {'self_ref': '#/texts/1', 'label': 'section_header', 'text': 'DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis', 'page_numbers': [1], 'heading_level': 1, 'chunk_ids': []} + --- text + {'self_ref': '#/texts/2', 'label': 'text', 'text': 'Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['713678f9-980e-47ed-9e1c-e6b533231d57']} + --- picture + {'self_ref': '#/pictures/0', 'label': 'picture', 'text': 'Figure 1: Four examples of complex page layouts across different document categories', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': []} + --- caption + {'self_ref': '#/texts/8', 'label': 'caption', 'text': 'Figure 1: Four examples of complex page layouts across different document categories', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['713678f9-980e-47ed-9e1c-e6b533231d57']} + --- footnote + {'self_ref': '#/texts/322', 'label': 'footnote', 'text': '© 2022 Copyright held by the owner/author(s).', 'page_numbers': [1], 'heading_level': 0, 'chunk_ids': ['73a2443c-3c25-4fbb-a6d4-0dec371ef104']} + role: tool + tool_call_id: call_c2sddzkn + - content: null + reasoning: |- + So the only labels present in this document are: section_header, text, picture, caption, footnote. There are no other labels like list_item, table, formula, code, etc. So maybe the corpus only contains this one document. But the question might refer to "document element types or labels" across the knowledge base. The knowledge base might contain only this one document. But the question might be generic. But we need to answer based on the knowledge base. So we should list the types present in the items.jsonl of this document. + + Thus the answer: section_header, text, picture, caption, footnote. + + We need to cite chunk IDs that support each type. We can use the chunk IDs from the items.jsonl. For example: + + - section_header: chunk_ids empty? The items for section_header have chunk_ids empty. But we can still cite the chunk ID? Actually chunk_ids empty means no chunk ID. But we can still cite the item itself? But we need to provide chunk IDs. The items.jsonl chunk_ids for section_header are empty. But we need to provide chunk IDs that ground the answer. We can use the chunk IDs from other items that mention the type. But we need to provide chunk IDs that mention the type. But we can use the chunk IDs from the items that have chunk_ids. For example: + + - For "text": chunk_id "713678f9-980e-47ed-9e1c-e6b533231d57" appears in many text items. + + - For "picture": chunk_ids empty. But we can still cite the item itself? But we need chunk IDs. But we can use the chunk ID from the caption item? But the caption item has chunk_id "713678f9-980e-47ed-9e1c-e6b533231d57". But that is a caption, not a picture. But we can still cite that chunk ID to support the presence of a picture? But the picture item has chunk_ids empty. But we can still cite the chunk ID from the caption item to support the presence of a picture? But the caption item is a separate label. But we might need to cite a chunk ID that mentions the picture label. But we don't have one. But we can still cite the chunk ID from the picture item? But the picture item has chunk_ids empty. But we can still cite the chunk ID from the caption item? But that might not be correct. We need to provide chunk IDs that ground the answer. The answer is "section_header, text, picture, caption, footnote". We need to provide citations for each type. But we can provide a single chunk ID that covers multiple types? But we need to provide chunk IDs that ground each type. But we can provide a chunk ID that contains the text of the item that has the label. For example, we can provide the chunk ID "713678f9-980e-47ed-9e1c-e6b533231d57" for the text items. But we also need a chunk ID for the picture item. But the picture item has no chunk ID. But we can still cite the chunk ID from the caption item? But that might not be correct. But we can also cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But that might be okay because the caption item is associated with the picture. But the question is about "document element types or labels". So we need to list the types. We can cite chunk IDs that mention each type. For the picture type, we can cite the chunk ID from the caption item? But that might not be ideal. But we can also cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But maybe we can cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But that might be okay. But we need to ensure we provide a chunk ID that is present in the items.jsonl. The caption item has chunk_id "713678f9-980e-47ed-9e1c-e6b533231d57". That chunk ID appears in many text items. But we can still cite it to support the presence of a caption. But for picture, we might need a chunk ID that mentions the picture label. But we don't have a chunk ID. But we can still cite the chunk ID from the caption item? But that might not mention the picture label. But the caption item has label "caption". But we need to support the picture type. But we can still cite the chunk ID from the picture item? But it's empty. But we can still cite the chunk ID from the caption item? But that might not mention the picture label. But we can still cite the chunk ID from the picture item? But it's empty. But we might need to find a chunk ID that mentions the picture label. But we can search for "picture" in the items.jsonl. But the picture item has no chunk_id. But we can search for "picture" in the content.txt. But that might produce a chunk ID. But the content.txt might have chunk IDs. But we can search for "picture" in the content.txt. But we can use search to find a chunk ID that contains the word "picture". But we need to ensure that the chunk ID is from the content.txt. But we can use search to find the chunk ID that contains the word "picture". But we need to confirm that the chunk ID is present. But we can do that. + + Let's search for "picture" in the knowledge base. + role: assistant + tool_calls: + - function: + arguments: '{"query":"picture","limit":10}' + name: search + id: call_1atkyf5a + type: function + - content: |- + [93ad7de1-8b78-4585-9865-01058d67019d] [rank 1 of 1] + Source: ACMReference Format: > KEYWORDS > CCS CONCEPTS > ABSTRACT > DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + Type: picture + Content: + DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + + Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com + + Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com + + Ahmed S. Nassar IBM Research + + Rueschlikon, Switzerland ahn@zurich.ibm.com + + Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com + + Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com + + Figure 1: Four examples of complex page layouts across different document categories + + Figure 1: Four examples of complex page layouts across different document categories + + KEYWORDS + + PDF document conversion, layout segmentation, object-detection, data set, Machine Learning + + ACMReference Format: + + Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 + + ABSTRACT + + Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. + + CCS CONCEPTS + + · Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ; + + Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). + + KDD '22, August 14-18, 2022, Washington, DC, USA + + ACM ISBN 978-1-4503-9385-0/22/08. + + https://doi.org/10.1145/3534678.3539043 + role: tool + tool_call_id: call_1atkyf5a + - content: null + reasoning: |- + The search result shows a chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" of type picture. That chunk contains the entire content. It includes the picture label? Actually the content includes the text of the document, but the type is picture. But the chunk ID is for the picture item. So we can use that chunk ID to cite the picture type. + + Now we need to find chunk IDs for the other types: section_header, text, caption, footnote. + + We can search for "section_header" or "section header" or "section_header" etc. But we can also search for "section_header" as a label. But we can search for "section_header" in the knowledge base. Let's search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":10,"query":"section_header"}' + name: search + id: call_6uhchwxo + type: function + - content: |- + [93ad7de1-8b78-4585-9865-01058d67019d] [rank 1 of 1] + Source: ACMReference Format: > KEYWORDS > ABSTRACT > DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis > CCS CONCEPTS + Type: picture + Content: + DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + + Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com + + Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com + + Ahmed S. Nassar IBM Research + + Rueschlikon, Switzerland ahn@zurich.ibm.com + + Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com + + Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com + + Figure 1: Four examples of complex page layouts across different document categories + + Figure 1: Four examples of complex page layouts across different document categories + + KEYWORDS + + PDF document conversion, layout segmentation, object-detection, data set, Machine Learning + + ACMReference Format: + + Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 + + ABSTRACT + + Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. + + CCS CONCEPTS + + · Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ; + + Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). + + KDD '22, August 14-18, 2022, Washington, DC, USA + + ACM ISBN 978-1-4503-9385-0/22/08. + + https://doi.org/10.1145/3534678.3539043 + role: tool + tool_call_id: call_6uhchwxo + - content: null + reasoning: The search result for "section_header" returns the same chunk ID as picture. That indicates that the search + index might not differentiate by label. It might treat the content as a whole. So we might not get separate chunk + IDs for each label. But we can still use the chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" to cite the presence + of section_header, text, picture, caption, footnote. But we might want to provide separate chunk IDs for each type. + But we can use the same chunk ID for all types, but that might not be ideal. But we can still cite it. But we might + also want to provide chunk IDs for each type separately. But we can search for "caption" and "footnote" etc. But + the search might return the same chunk ID again. Let's search for "caption". + role: assistant + tool_calls: + - function: + arguments: '{"limit":10,"query":"caption"}' + name: search + id: call_3cw2bnun + type: function + - content: |- + [93ad7de1-8b78-4585-9865-01058d67019d] [rank 1 of 1] + Source: ACMReference Format: > DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis > KEYWORDS > ABSTRACT > CCS CONCEPTS + Type: picture + Content: + DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + + Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com + + Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com + + Ahmed S. Nassar IBM Research + + Rueschlikon, Switzerland ahn@zurich.ibm.com + + Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com + + Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com + + Figure 1: Four examples of complex page layouts across different document categories + + Figure 1: Four examples of complex page layouts across different document categories + + KEYWORDS + + PDF document conversion, layout segmentation, object-detection, data set, Machine Learning + + ACMReference Format: + + Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 + + ABSTRACT + + Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. + + CCS CONCEPTS + + · Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ; + + Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). + + KDD '22, August 14-18, 2022, Washington, DC, USA + + ACM ISBN 978-1-4503-9385-0/22/08. + + https://doi.org/10.1145/3534678.3539043 + role: tool + tool_call_id: call_3cw2bnun + - content: null + reasoning: |- + The search index returns only one chunk ID for all queries. That chunk ID is the same for all. So we can use that chunk ID to cite all types. But we might want to provide separate chunk IDs for each type. But we can only use chunk IDs that exist. The chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" is the only one. But we might also have chunk IDs for the text items: "713678f9-980e-47ed-9e1c-e6b533231d57" etc. But those chunk IDs are for text items. But we can use them to support the presence of text. But we might also use the same chunk ID for all types. But we should provide at least one chunk ID for each type. But we can use the chunk IDs from items.jsonl. For example, for section_header, we can use the chunk ID from the text item "713678f9-980e-47ed-9e1c-e6b533231d57" which appears in many text items. But that chunk ID is for text items. But we need a chunk ID that mentions the label "section_header". But the section_header items have chunk_ids empty. So we cannot use that. But we can use the chunk ID from the picture item? But that chunk ID is "93ad7de1-8b78-4585-9865-01058d67019d". That chunk ID is for the picture item. But we can use it to support the presence of section_header? But that chunk ID is for the picture item. But we can still cite it to support the presence of section_header? But that might not be correct. But we can use the chunk ID from the caption item? But that chunk ID is "713678f9-980e-47ed-9e1c-e6b533231d57". That chunk ID is for a caption item. But we can use it to support the presence of caption. But we need a chunk ID for footnote. The footnote item has chunk_id "73a2443c-3c25-4fbb-a6d4-0dec371ef104". That chunk ID is for footnote. So we can use that. + + Thus we can provide the following chunk IDs: + + - section_header: we can use the chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" (the picture chunk) because it contains the entire content including the section header. But we might want to provide a chunk ID that contains the section header. But we can also use the chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" which is the same for all. But we can also use the chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" for all types. But we can also use "713678f9-980e-47ed-9e1c-e6b533231d57" for text and caption. But we need to provide citations for each type. We can provide multiple chunk IDs: "93ad7de1-8b78-4585-9865-01058d67019d" for section_header, picture, caption, text; "73a2443c-3c25-4fbb-a6d4-0dec371ef104" for footnote. But we also need a chunk ID for text. But we can use "713678f9-980e-47ed-9e1c-e6b533231d57" for text. But we can also use "93ad7de1-8b78-4585-9865-01058d67019d" for text. But we can use both. + + But we need to call cite with a list of chunk IDs. We can provide all chunk IDs that we used. But we need to ensure we don't provide duplicate chunk IDs? It's okay to provide duplicates. But we can provide a list of chunk IDs: ["93ad7de1-8b78-4585-9865-01058d67019d", "713678f9-980e-47ed-9e1c-e6b533231d57", "73a2443c-3c25-4fbb-a6d4-0dec371ef104"]. + + But we also need to provide citations for each type. But we can provide a single cite call with all chunk IDs. But we need to ensure we cite all chunk IDs that support the answer. But we can also provide separate cite calls. But we can just call cite once with all chunk IDs. + + But we need to produce the final answer: list of types: section_header, text, picture, caption, footnote. We need to cite the chunk IDs. We can call cite with the chunk IDs. Then we produce the answer. + + But we need to ensure we don't include chunk IDs in the answer text. The answer text should not include chunk IDs. The answer text should be natural. The chunk IDs are only used in the cite call. + + Thus we need to call cite with chunk IDs: "93ad7de1-8b78-4585-9865-01058d67019d", "713678f9-980e-47ed-9e1c-e6b533231d57", "73a2443c-3c25-4fbb-a6d4-0dec371ef104". + + But we might also need to include the chunk ID for the picture type. But we already have "93ad7de1-8b78-4585-9865-01058d67019d" which is the picture chunk. So we can use that. + + But we might also want to include the chunk ID for the caption type. But we have "713678f9-980e-47ed-9e1c-e6b533231d57" which is the caption chunk. So we can use that. + + But we might also want to include the chunk ID for the text type. But we have "713678f9-980e-47ed-9e1c-e6b533231d57" which is the text chunk. So we can use that. + + But we might also want to include the chunk ID for the footnote type. But we have "73a2443c-3c25-4fbb-a6d4-0dec371ef104". + + Thus we can call cite with these chunk IDs. But we might also want to include the chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" for section_header. But we can also include "93ad7de1-8b78-4585-9865-01058d67019d" for picture. So we can use that. + + Thus we can call cite with chunk IDs: ["93ad7de1-8b78-4585-9865-01058d67019d", "713678f9-980e-47ed-9e1c-e6b533231d57", "73a2443c-3c25-4fbb-a6d4-0dec371ef104"]. + + But we might also want to include "93ad7de1-8b78-4585-9865-01058d67019d" again? But we can just include it once. + + Thus we call cite with those chunk IDs. + + But we need to ensure that the chunk IDs exist. We have verified that they exist. The chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" exists. The chunk ID "713678f9-980e-47ed-9e1c-e6b533231d57" exists. The chunk ID "73a2443c-3c25-4fbb-a6d4-0dec371ef104" exists. + + Thus we can call cite with these chunk IDs. + + But we need to ensure we don't include chunk IDs in the answer text. The answer text should be natural. + + Thus we need to produce the answer: "The document element types mentioned are: section_header, text, picture, caption, footnote." + + We need to call cite before writing the answer. + + Thus we need to call cite with chunk IDs: ["93ad7de1-8b78-4585-9865-01058d67019d", "713678f9-980e-47ed-9e1c-e6b533231d57", "73a2443c-3c25-4fbb-a6d4-0dec371ef104"]. + + But we might also want to include the chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" for the picture type. But we already have it. + + Thus we call cite. + + But we need to ensure we call cite before writing the answer. So we need to call cite first. + + Thus we should produce the following: + + ``` + { + "chunk_ids": [...] + } + ``` + + But we need to call the tool. The tool expects a JSON with chunk_ids. So we need to call: + + ``` + { + "chunk_ids": ["93ad7de1-8b78-4585-9865-01058d67019d", "713678f9-980e-47ed-9e1c-e6b533231d57", "73a2443c-3c25-4fbb-a6d4-0dec371ef104"] + } + ``` + + Then we produce the answer. + + But we need to ensure we don't produce any extraneous output. We need to produce the cite call first, then the answer. + + Thus we will call cite with the chunk IDs. Then we will produce the answer. + + But we need to ensure we don't produce any other output. We need to produce only the answer after the cite call. The answer should be natural. + + Thus we will do: + + ``` + { + "chunk_ids": [...] + } + ``` + + Then the answer text. + + But we need to check the guidelines: "Register chunk IDs as citations for your answer." So we need to call cite before writing the final answer. So we need to call cite with the chunk IDs. Then we can write the answer. + + Thus we need to produce the cite call. Then we need to produce the answer. + + Thus we will do: + + ``` + { + "chunk_ids": [...] + } + ``` + + Then the answer. + + But we need to ensure we don't include the chunk IDs in the answer text. So we will not mention them. + + Thus we will produce: + + ``` + { + "chunk_ids": ["93ad7de1-8b78-4585-9865-01058d67019d", "713678f9-980e-47ed-9e1c-e6b533231d57", "73a2443c-3c25-4fbb-a6d4-0dec371ef104"] + } + ``` + + Then the answer: + + "The document element types mentioned are: section_header, text, picture, caption, footnote." + + But we need to confirm that these are indeed the types. Let's double-check the items.jsonl for other labels. We saw the unique labels: {'section_header', 'text', 'picture', 'caption', 'footnote'}. So that matches. + + Thus we can answer accordingly. + + But we might also consider that the question might expect a list of types. We should list them all. So we can list them as bullet points or a comma-separated list. But we can just list them separated by commas. + + Thus we will produce the answer accordingly. + + But we need to ensure we don't mention chunk IDs. So we will not mention them. + + Thus final answer: + + "section_header, text, picture, caption, footnote." + + But we might want to format them as a list. But we can just list them separated by commas. + + Thus we will produce the answer. + + But we need to ensure we call cite before writing the answer. + + Thus we will produce the cite call. + + Thus final answer: + + ``` + { + "chunk_ids": ["93ad7de1-8b78-4585-9865-01058d67019d", "713678f9-980e-47ed-9e1c-e6b533231d57", "73a2443c-3c25-4fbb-a6d4-0dec371ef104"] + } + ``` + + Then the answer. + + But we need to ensure we don't produce any other output. So we will produce the cite call, then the answer. + + Thus we will do that. + + But we need to ensure we don't produce any extraneous text. So we will produce the cite call exactly as JSON. Then we will produce the answer. + + Thus we will produce: + + ``` + { + "chunk_ids": ["93ad7de1-8b78-4585-9865-01058d67019d", "713678f9-980e-47ed-9e1c-e6b533231d57", "73a2443c-3c25-4fbb-a6d4-0dec371ef104"] + } + ``` + + Then the answer: "The document element types mentioned are: section_header, text, picture, caption, footnote." + + But we need to ensure we don't mention the chunk IDs in the answer. So we will not mention them. + + Thus final answer. + + But we need to check if we need to include any additional chunk IDs. But we can include the same chunk ID for all types. But we can also include the chunk ID "93ad7de1-8b78-4585-9865-01058d67019d" for all. But we can also include "713678f9-980e-47ed-9e1c-e6b533231d57" for text and caption. And "73a2443c-3c25-4fbb-a6d4-0dec371ef104" for footnote. That covers all. + + Thus we will call cite with these chunk IDs. + + Thus final answer. + + But we need to ensure we don't produce any extraneous output. So we will produce the cite call, then the answer. + + Thus we will do that. + + But we need to confirm that the chunk IDs are correct. We have verified them. So we can proceed. + + Thus final answer. + role: assistant + tool_calls: + - function: + arguments: '{"chunk_ids":["93ad7de1-8b78-4585-9865-01058d67019d","713678f9-980e-47ed-9e1c-e6b533231d57","73a2443c-3c25-4fbb-a6d4-0dec371ef104"]}' + name: cite + id: call_snuw2sse + type: function + - content: Registered 3 citation(s). + role: tool + tool_call_id: call_snuw2sse + 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. + + Accepts chunk_ids from search results AND from direct file reads + (items.jsonl, toc.json). Verbatim copies only — chunk_ids that + don't exist in the database trigger a retry. + name: cite + parameters: + additionalProperties: false + properties: + chunk_ids: + description: List of chunk_id values from search results or VFS reads. + 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-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"The"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" document"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" element"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" types"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" mentioned"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" are"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":":"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" **"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"section"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"_header"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":","},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" text"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":","},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" picture"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":","},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" caption"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":","},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" foot"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"note"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"**"},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"."},"finish_reason":null}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":""},"finish_reason":"stop"}]} + + data: {"id":"chatcmpl-149","object":"chat.completion.chunk","created":1779702670,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[],"usage":{"prompt_tokens":14775,"completion_tokens":25,"total_tokens":14800}} data: [DONE] diff --git a/tests/cassettes/test_context_enhancement/test_expand_context_no_base64_images_docling_local.yaml b/tests/cassettes/test_context_enhancement/test_expand_context_no_base64_images_docling_local.yaml index 2b7db5c2..dc98d608 100644 --- a/tests/cassettes/test_context_enhancement/test_expand_context_no_base64_images_docling_local.yaml +++ b/tests/cassettes/test_context_enhancement/test_expand_context_no_base64_images_docling_local.yaml @@ -8,7 +8,7 @@ interactions: connection: - keep-alive content-length: - - '10466' + - '3480' content-type: - application/json host: @@ -17,93 +17,35 @@ interactions: parsed_body: encoding_format: base64 input: - - |2- - - 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' + DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com + Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com + Ahmed S. Nassar IBM Research + Rueschlikon, Switzerland ahn@zurich.ibm.com + Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com + Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com + Figure 1: Four examples of complex page layouts across different document categories - |- - $_{Affiliation}$, 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/ + KEYWORDS + PDF document conversion, layout segmentation, object-detection, data set, Machine Learning + - |- + ACMReference Format: + Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 + - |- + ABSTRACT + Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. + - |- + ABSTRACT + Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. + - |- + CCS CONCEPTS + · Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ; + Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). + KDD '22, August 14-18, 2022, Washington, DC, USA + © 2022 Copyright held by the owner/author(s). + ACM ISBN 978-1-4503-9385-0/22/08. + https://doi.org/10.1145/3534678.3539043 model: qwen3-embedding:4b uri: http://localhost:11434/v1/embeddings response: @@ -114,62 +56,29 @@ interactions: - chunked parsed_body: data: - - embedding: 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index: 1 object: embedding - - embedding: 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u7ZcuaP8hryc1Y88V72fPNdYRrq3bzo9+McJPZ3gU7tUoMU8Mx6GvATERTzQU8k88KaqO4fGI7ygTUC997qUvVJI9jx8jR28VkADPBa3Q7sPZB686AoHPZsCYT1x0es89Q+8vCXnD73VLq68GJ+CvR/Snjx3jW48pT0RPXuw/bxWk0w80ZrYO1duEDs28xW8x0RmvFy3b7v65bs8WAFMvJahnDw+b0y8bhmBOjWXDrofPrg830coO22OPDsMEPm87+YVvX5sjrzM13k775e4O0CxXL1S7QW8oWg8PUixTrypDTk9ynyHOdNwUrp2JJs82FGsuXJFt7rqESW8dfasvEgQjbzk3cK8TQEHPMW2gzhnIzw8F17Auezq4TiKbG27uwelu1nMLLzm80M8cvjevM8CnbvGj1E8I9PFvMB3yzwym3w88/Ryuk5wKzw0wD89kFDUu5aWQDvAu6c8Q6qvOhJborwifzU8ag8EujBjfTsCsYW8G1oyvPrDqrp8m+87jwP2vC0etbyaeoO72o1IO7/km7tig9G8gL0hPOTRjzoFuDU8sx+wvOXUObyNSSa8TG8rvJ6uAjy3Od87aMNhO23Fary5+6U8W78gu7pNuby0Eri6GqzlPL25OzyirEk8egbyu0YdFTyuFEu8mJxyPEB87zsgAI69HBs7vJZvBL0ECDQ9q5pfPA+TMjzlEa28znh7OTnFnLx4BDU8OW9VPH9xprx7x+S59kPIO5g56jpfjpS8O0PfuoDSETsInnW8++QvvLVbXr1bUQy89dwCvYdG6juZeYm7gH8wPE6nkLuwgo88g3aMOw1ijjyAFAA9f6O9vCQsUbv3eV47JcqaO2nSYLtb2qk8Ee4evLjhjD0c6x085eVHPGRIKLz8gH04952eOmk/r7wjY248LeUHvBB19bq6oYS7ZhPZvDGWVryr9Ja8NK3tO5jpgLvB9yc9Kw+TPPzoMD1ueBu80OIfPPZ3prqE+8a8b92Tun6tK7tzptY79YSsPGqg0DxiQom5rdKPvGXdnjw53pG7fL7Tu+hwnbzUVmA8qBUfPF+znDymHhw8ZDUQPFW8mjxuA5a8k40vvIwKjLz3kge7Aba9vG+P5TwWdQu87QTCN7wb7Lz3sZG8f6ZXu1fDcDzcqT48kDW3vKmkTbzL+BE9rauoPOGuVLvDYZe85oEtvEoMUjxH9iq84mRLu5iPRjxKdsY6XVLVu+VmFLzNH688Fd3gOvD4mbwmmQi8Kk5dOhTIjDx3jcQ7asFvPEM5ajzm8Vq98n56PDWxjrw4/7G8K9DiO7Fgd7sWiDq8QoCkO1Af8LxLM7C807KEvEOKJrw7/3A8LhCPu/cDbLyzFDG9K0mgu0sXx7w1lrm7qAMCuvYZ/LtJeVe7M9cTvVf+sbsf8Bq8NPoovGj5ALyxCxI8zFiavLTLtbzEcUY85lQ1PREWHDyHHoc8Nb5fPBsCVTyiPqK8oZZnPLA5ITxkiww8vQl7PNMdwrwNeTI7hNLKvCW8EbzXLN+8wBdZPInsaD1RoL66KfaoO8zc+Tyqwn48OOStvIFjqzywOtW8Va1kPGtuljz3QZo8U9qMPHg5LTua6BK8Q4TMvHeUk7sYxjs88rGHPOqQLrxc3v47xCSIPO8GILw5BJg7XNuwu1i/lDuWXqg7dek6u2SQB7w6VDo9UY/1uEPi8DvvGgs8qNQxvNrwh7y9m2m8FOl8vXp7n7wb6JY8RyPEvB8jqjxbuYY8NKwJPexW5jyd/eO8FSsxvLiQKT2sflC9TYakOqLUSbxvN7A83z4VvLzIoDzmcqW7nZxku/wVIrxIUxq7809+u7PUQL05GAS9O/CEu82LibsUc7S7ATmuvPD6zTs1d1u8aeqwvKMOh72tRwO8hmcOPaEYmDwCgyw81RqWOoZZDz0vapO78CWuvOkclrySckG7ZY2uO2EBr7wviJO864kXvMdMjTzBEyU9z8kFvAOCIzyFHDg8N2fSPA03kbt3HZq8MWA2u/3h17i6W1K8VHeDvNGwpjt4vIs8E66kPOC+uDup2IQ8AV0jvB20TjxGl2m8zgiIu1WETLoQpLM8N/mTO9wDrTylYRA8pMVou/uFyLwbRxs9dF5VOquv0jx6h9Q8WHY3vBVY9LwmH4G7zKWBvJrlVLxHyZk7PssFvTeLAr2O9G07MA8PukS1Yrwnjpm8zVauu9KamTzWVRG8C+07vUKFVbtrpQs9JC4bvENFe7yjT4M8YHzhvEDeFb2kc+w8kDlQvMvdFLvz8c48myxBufZv0jzbwVy8P6H8vFfyYbwZqrg8Ef+tPP/F5TyH3686Hc6Hux+7Lrzmicu8M8OSPK97Ar13G/w8681JPFZ0Rjv/T8k8oyPlvMKCMbu33R+7EXwPPfnxmTxl8gq9Ia+gvO+FiLwNoGi81rzYvAW5q7tKVp88TuRSPMwLZDwl10+9Smz5PInHM70678G7er6IPKLRabx3H108DRL6vHL9mLvije+7hW6XPHEB1zxkKdG6VZWQPHps5jxNcBe8F5VjOrV1xjykOhc8Qv2ePH+qJbuZYtU7/Ie2PFg9DzypfnE87IsjPJ8jtDybWGw9cVbZPF9ZkTvUlGY8D4SxPHaEAD3DcI27frmevDDk07r6ylQ82R5ou3w6DDyjuWK7DTs3vFpq5LzQNYW7N5/FvIrZ8Dznx1y7OHsauwXHEj1gt1o8d3k2OmXmaLwYamA84YDbPBBiPTt6AzW8IqwKvep4YbwM3Ba9c++gOh9vj7yynKW8PoUNPb+ovDs8nAQ8SMSDPEQmCDs+R0M8ZxHDPGfETD30JTa7uwZvO9j42TxAlwS8hSaWPDFacjzbSpe8RUSFvDaXfzzAcku89Rt7vK7dBT2pZQY9e7KZvCutkzxpVec7yKmRvGd8BbxlYVy8UDI1uw3zqLzwpow78PMYu79qj7xiJRS9VtAJvDzIiLzCFvc7Qb0VO/bpA703oEc8N2vjO8ufnbvTTde8qLSnPIqhlzxvysU8V7x9POqwWbtT7cQ7FCmKPN/oibzQxRc7tlOlvBcgLLxt7ls7HlkjvOLPwTw0+ek7TeD4OwZjjrxMfFq8vzskPKft4jwQnYg7ExuMuxZnkrtIljA7S9NxvKc9sDu86aA76UwAPatIvbuiGMS8qY6ovP5RPTzZDBG8F4f5u3KMD7ytTUu8mTUhvb/pYDtTGaW89EvMPM+++ryhsxq6tEhuPHyskrmldYS8R2jzPCnOYbyS9XK8PBPkul62XziKtDw7kYaXPDBBuTtfuyA8ty4IveglOLz9m/i8iHTMvLEEELwcj3q8WqS9PEWm8bxu5za7PkvBvM6CMbyW9O+8qzLavJ/jornoCWY8QKSevGQLaTzF9Dc9GrSUPDS0hbwFd5O8C0hcvc+5nDvShW87WI6EPI8u2jzRd6k8IyELPOrUc7zjoAA9uPE7u3dmRr07+HC9arHwOsMGFrt4okE7Eau7POcKy7ymSIM8AJe2vOTbmjmNlH08lLbhO5d74zxET4a7i66SPOIpkrxhPg+9zF65PP4Ihbq3a648VCiIvN5rN7weTGo84ZMPvImT5DsJlyk8WC6sPKDlNDrae2c8TdnQvG+vvrzdwhm88ZyrPP4kHjzzfzs71aDiPAxPeDyUzh87GUCyO9gP1LwZ2bC8KltzvEpBFjrmWIk8snLNO9azCLy+WX+7wl+vPMddBzx2JLw8jcGCPL7nljueRsu8z56Au7IclrxoRAG9EyjPOo1jGb0WlWU7ErxavL6vzjwKxhy8k3xSPOUdrjxAPJO7PCWBO9NTRb17osG7dnGmvH5YVLzLXKC8FxEKPGFdIzrQZ6O8q0eauiBkfbtz9QO9sQJZPHFDHbznnME8fy8ivB6UVjzUGam7HCpzPXKKW7yERA28S/aQPJAfLDyaF328xuYxPFz97zxyNmi85zGuu+hdojw8uVI9vqByvLorW7u150C8G23dOYOYYzxAcvm8whcSPCwrgjujHue53GsSPRbXlLvifK88UEEpPEda0bye55E8aLZJOcia67uwAdg72p72OyPjLLuln2q8SDKYu/Xi7Dv3CVu8tKYwvCDiWbtES2G70FuNvAn42DxqhcO8zT8GuoiBsboRPxs855KlPQDLrDt33Dy8G/SwvCBArTwQL5I7Ys+dvHLHkryH7Fc8yJcGvfJYwbvuGbW76eoIvKBjUTwyFcg7ddgSu5Dzy7reSmo8RQcnvKBylTxQoXU8QvDEvMkr+7tkU4O7CtZrvSjOIzu96tk72PdOPOSMmjtG/bw8G53JvKUKvDuDUEo9V/qZuzH6E7yR3TA8EqY6OrxpB71wzxg8oB1Pu+qih7xCqZ28rtmmOxB0BTz10Ce8yhKbOiIJsTxiQum6A6MZPEx9Bzvq2aw7lTu/OwjYTD36RLg7rUbxPI6XgjzSPu+7JnluO5LAYbxIwJQ8dVjbO3KNVbxFOiI97Ie6vCSojrvN6bu8IzKHvM1RIL2K7BU9SeNLvFS0MjwzsGw8OVe1vA8NAT0woda7i52huwd7pDt49Yo95uvyPB86Aj2ew108C5SfPGlGTTxm3jC7gcEwuxhZgrxfxaq6DY+DPMgyE70LasW8uIKqu4xG3LvYa226ogKPvIhbzrzsgAW9Bq4GPZih6DuB9PI8liynPHJcYj2Is3+7M4bxu4IfXrxs41i7oLgevHyDCz2cYe67Bm5cO4/tGj0LvfW8blwePalaHbxkU4g8hTalvB6dY7zztH+8jbKNPIYqs7xQ5FI7Sk0wvF5jzLu+pW68hN4evdYMKj2mr6y8KLatuxp9Ejz6+de73QFpPWpLHT0OMsI8W0TfvG6r4LvSB948LkC8u/y+Er366TG8idSBvDL+fTxTZ+28nbcROlthZbswN9i7u20tvZVAEztedqe89mLFu9RwYLv3LIG7uGKRPK6uKTpaCTe8hGEbvOYWIbzWwgI8A+a7vO0NPjy4FN+7mzlEvLBMRj0St++6Fzpku1UJKTuYbDg7YD5HvMUJ97xqVqg8J6eMPGW7oDwAW8y7ENykt6xxI7ybJ/Y7XEETOwe/Sr1LjO086S6SOVdjPbxskx88fTp1vKjSJrwkK8K7ZMAdPDkKKrw2rlc8zBSdO3uHijoabra8a1SDPAkCuruOfbw7jtbHPJCBxrzgaOC8vPaAPKL0przUlWW85rYOPEcC77tvZC28BvEDPBKnnrysAAo9DNFvOx7Ot7tH6ri7Ia2wvMHsiDuT3Y+8dupsu33mtTvZ9as8YqmvPDVwSTulp6w81MiMvHC6LTymEo47wL+bPLdvmTy4nvk8Qwa/uRT65zxfrx08iwPUvH0zAb0R9fI7/s2yO/DchrzwCny8Jb09vIrnqTxHprA8kg0GPb8RJb0QjJa8LPyQOyYd9juyQRo8FIdQOyRufbxS6s27a3dGPeOJhbuvViI9+FeSO7/IartFZmI60oeNPGfSyTzjrsG8I2J5PJUOkDxlcFm8cSiZPHzPFDvsxaU8L1Aevb4ozru8deu65AsVvf28V7yttku8im8jvDq7Rjl0rIO8H5FwPAknGTzGngQ7RNrTvF7D5jx/amQ8F0cwvDOrML0fLUU8tFchubGbRjy8RcA83PvjPOzgyLxFezA7a3QRPT29XbxLdkw89nlKPb5497sZBum8U9Q+O5Rw47wqZXu63UAvvWJ4PzsYNTY61ESEO/4wTTyUKQS99NvcPFfp37yF+cK8f46+usrYFb3DZle8T873vBVD4bx7dqe7sUcIvQnvurzE7lO8mSivPG7xjLxlf6Q7PimSPIi/CzyeGZm8D6/FPPwj9DxkFxO7iwc+O4jkjrzbVMc8fjWRvKb2q7zqL5y8Z4nBvLn3xry2w/M7jZXEO95OmbwR+zk8gQSyvI7xyrxYghE9D13XOrOOhLpRfy09lAkuvXE3W7pNioE8NIaGPFcCSDw/8n08DvOQvCPcV7zkObu8ZR4ZvXeHojzQyiw80fbTvAUiJT2fS6+61eMFvWZfVDtt+j08LkitvAsQ/Dx/SoE6EvElu5G5DTzLRwi8kCp6PJFkRTwvTGK8kFTJPJufQD2sQYm8NZKNvCL1Er1F/js8zUlCvCgmAT2IGF88b6IRvZuuFz3dS6c74TeYPPR6Gb3YGtE7tI60O08YSTtx/SY8L2svPAoghjuH9IY8abfPPHfx0jynbHi8P9UxPD4l8jru0nY7ZwtLvFgfg7x4boA8VwMRvBTe1DqRWT880PgevBL0Zb2zz4o8CpHEO2NXzzvnOIW88nOuPJqawbwIetI6RIn9OzBfnrySZ5A7B9YvPa84lbytlqO8eFqrPKOF/TvwMFo8FwYiPP3amDxcph09Q/yePDspaDmcEy69+pMjPQWBCzsGvhG83l6svF6Dk70hfK28LtNGvMuIs7zefHC8pQkYvU282DomYCU8sZ67u1L4pzznu4s8JN+ZPNDetbsE8he84UnyvKRfiDyVSUG8WuUsvFZMgzwvrjC83Jzku3ogfbpwc6u83OZ4vNDw5zv1wA489haIPHK3n7z2HWA8758zPcYkEzzCEuU79R0EPAj6TjwV4gS8fxMHvC1qu7qJwrY8ryoJvc1AY7xExGs7spIEOw8ztLzjiuQ8KwvqPBWcDLzg8JM9wIoluxqaobw8/Ak9FYrqumWvnruAOBm9LQbEPASIDr3k7k68zq78u5qpbbzxH6U8FIC8u9ICm7wouVs9/MTXvJ8RxLxz8e68h42mOzfsUTzAvkM7pdiSPAINXDzMLvy8uiYVvDfGJzxdgUq8aX22PCEXEj2cHMa8DwqbuzD8EbtHTlA7TB3ku9RCxLyTabQ88DiMvNnjkztGkUW80BulPKOfgTnIg8K8qSGoPJOMYryxCeu86o46PE3/YDxKVO280hcBvI8oN73zA+Q6loCBPFMYLDy0PUm8+OXivHpBvrwAy407hYI9PI2DyDznZig8k/LqvJk3hTyCMRK7n1SKO4bypblwubs8m0WMvEvMijxIWPs7bP5Auz3oYbw7YcW8ZNMzvBje9ryKmbm8iKswO4pv7LpM+HC86RyAvGjlSTypR9e6LzSivMhETbqip5I6ZJ+4vN6JujpGZOI7kWszPOjL5zyPRfG8rA08vMIG1jxn/Y47LhqAPfiKoLo3RAW9phU9vAMinbx6soO6z1ApOzZi8Ts9jjm9y6ZYuwyuXTuPJcG8AJGTPGShGDzDpMc6Qky+PPG9rDzLP6o8sPkuO4rknjtUMLc7gcx1vNzMvjuvJhG9DRMdPU6LhTr7V7Y6jLD0upW9FD0Kb4s8hBZBvPNEBzxhuAQ9IbyAO3W6cjuLWQC9gXGDOyUlaryVLZO5zHcGPe70ybzIGC+8HJiMPMJgvbxVRYA7lAIyPDNxCD2IMPw8NwyMvHddRT32dSC60kqVPAZKy7yEZt+8xb6UvMD4TD3Bp4u7p7KYvHEThTz9dxK7E2/OO1G/jrzw5Yc5+7Y+vORwDr2adNi7vKgcvMMQhbwRfMI8gKSHvCc17Ly32Oa8nmaavNbgVT2Y6ai8KeqevN9IcbswJ6S81gkcPNbr3bxR1JA7dqc1unZKNjziakS9I3kRvQ67U7zRWQI6BeAqOziZQDzuJyy83Iv0OwSoEbwpcBO77gYlvbyUwbxnsKa8lVtYO4by4DySu3+8z8q7ue15cTmBMzg8a4kTPMN+JLlk+CQ8LXtHO3z+GLzZgu+86lDou0FSQTy1uDc8Ic+VO969aLsx33W7tTrKPPrvfzwPDyK8gmx2PGeWE7xp0fC85EGnPHuEN7tLlCc8UWvLO/ueqLwMrhk9/NW3PIv6hbwY3RK9/u7DvAnK3bv1kiO81ru0vLRIpjwpvSM9EuBvvDv707wwSb+47021PLP6rDqwiFy6v9r7vJf01TuRfzw8MbPHPARBLrwhQuS7sbIlPJ7QsDxGvI28qdGqPFYUCDzDh327VwwivGkbvTwXeBy8N0YPPLlBBTyN+RW9Isu5vHLLr7xfHui8bUHSPC/WybxeIbC8wWhUvEngczwF0Pm7DEFtPJW6yLudI8k5lr1oPATs07wv1wq6ACNmPKp47zy8dsO8kiveu6pDvLyAhc48pOe3vHKjrDyK2YA8TbbuvK6h7bpey7m8nuXUOgh0mDxI0Kc8+3pdPAzerjwoICM8ZV0NPOCDGztIzwk8V1C8vLWhlbxGAXg8v6Ewu/p+xzuazTE94tPtvK5ujTxTfdw8ho/mukJNOrzOnAQ8hX+ROxkNCj2THQa8OS4kujkqJTy5e7u8YIaRvJ0rJT1SFpC7bTvivAvtOjzRJ7q76frmO+Vjm7v6LyA8lybtO4scSj3HWze99N0xPEZbWbpDMtI7M3ibOkZhkTyqJVe8i0VbOo5prrt+M7U7/40RPAK7BLwEf2C7iHcIPAcykzzxyLC7kUloPKEScruQlkE9oc20OrtZ2bsyiCq8lR+tvEIs9ruIMtY8UzAtPd3LKbwq1h+8WW4fPKeRE7zXXJG6ANfTPC+S4zsXDk87Q/sjvHBvMDwlTCg9zzf/POmo37uUJdC8pHbKvDoYpTwwYA68/3x+O4jUFLxxLqi87CjlvHQoiDuhPoq7vCdgvFQcAjzRW+Y7XyyBvKhNEL1JLq27yy9UPN8EHj0yo2S6FpAHPaPeIDzSp7o8YNwJvAMMurq/cZg8MFZbvN2VCDz6fNI8e72MuyIt9zxx8xc6rzfpO6W0sDt+AcW8b4ioPKwfvbtGkAA9qvpWPE/vsbtRiO47mZk9vNcTHz0fPCe9TuX5uki5TL22UKE7MOW1PG6T6zysFOs7DtL4PKgEZbyXDks9bQDqPKj5wDyXa8E8Ab/lvLUdqbnkQmo89SqAPEk5Prsivgg8BaJWPBf/BT1HvaO84ty8POFEVTqEuYo7Ky/KvDFjjTz4gS29hEMDvSMZGz3/sRq8lhlyPH8forwX96m8tRjsO8yhFbz8kPm8u4elO4qojLyWHUA84tZFPcbHt7wHYXG9uQPBvEsj+Dtx1IS82Uu+O5sW2jytnWk8TYgUPO1T7TtL5Kw87/yiPH++1TwxkVe9Jeycu0S12zlOZDa8AZgZPMb4W7mlKuS8PnMiPGcab7y375680H2MvFCC+joPzIe8sLFWvOorGzwB79i8AQLEvLpqWrwu5uk8nozbvEzvWTxM00E8jhqYun2fpDyDOGM7ri3cvPqk1joHzim8SYfSO4No1zyIIZy6+i2IvAVgfzzS5SA8JSSGPI6Pz7z9lDc8OdpDPPzLoLzCqfo6Tg98PK5VabynGeQ56d6UvAZymLzaSRC9PkkwPJQpCDyD7QI4O7WSvLEGLzwtFag85r+ROphP4Twg6uc7q5OuOnf2GTyjqBW9lH+Xu6BVybutTSu8wp+HvBtdkbtIAuS6P1rnu3hrBL3T4ae8qiL3u510Kbxhyya9x4WWvOq5EzoXWRq9OAw1PC6dtzt96Uq8RQcyPWsHkbzC/sI7cKt0uq/7yTz+3bc5uOM3vfVmhrxj3uu7TvUFvdtOgTxrNl88gxb+u34YYDunvhS8xeUVPI/63bzpcz29NkMRPLPD2TrwqZa86tv8vLAL7DsgGoe8XXAGPVQ8M73Mv3E8JX0bvCNKerynceC8b79JO+ZAsDx/FdE8jkdKux3A47xNlzs8cJ34O0hk8DzXgHK8yNK1OxnOgDwn3LI7p7zHPEgg6bySuhY8IFqKPDChwLpPyd4648vdvMPBBb0yfrk8huwCvT/bkTxMhKi7xmDuu3UEX7zuLVo8RP4xvOSezTuNMzK9t2Y/vQv2aboEfnG8RCzRPN2fz7yiiAQ91tcyPKKeHzwrohW8DPjguzIPjjz/Dp28VWTru+hbx7zE1A87Dh3YvLb6DDy6y1s8w99DvJ3qXjrhQCS8arXROVjSZLpLFb+7gPoPPK0D7DsYI7m8eU5TvIMCuLxnJ6+8H4EzPPCeOD0//C28GG6jPDx/Djxa44W886u3vDwui7yYafo7l8udvGpuwrzqPlc8xJsCPS/TtzzP1Ro8EOcOuiT0+DzZCjY8K2gePaFiB7ygXes8k0WTPJf5RzxLHZ88aZvcPNHzljtEJT68OOJLvEjPibuBOUs85IvJu5A91bv+qDw8/lC1vKDtszy8poa8HBG4PFMHtbwKCSu9TheqvFcsuTyvcNm8KjCuPJJb2jzABS67UI2pvICTZTwqzJq7gpHGOl4GEbvT3a075WtNPAcSiDtsmio81qGAOm9E3rwMPdS8OqMwPNsknzzpYMA8oFwXPAXTeruyRSw73hqPO5hnJTs/IUM8equ/uj3h8DwCn5K8Sd6svNDmQ7zpaJG8WQypPNGMbjtFCpI8GXnNPPdMTLpvT6G8eqkUPbf3NTxN9ti8TstSOmvKv7zJJj+8hG2yO9lgyDxINdU7OtZpvHBimjxSe127nRH3PMO5cLyUqkG86lwJvISmoLw+Gji7JHyrPP9oWLuQsrs8xMCyvBSk0LznZ1i8cOb0OU/aYzwMKjG9vWqZvE85rLvkBVY7aSSOOyL9RjxgWaA7IWmyPNjvsLvMZ/47OW1BvHC++LxccyW9m8OIPDwxfLwlDEU7pJrBun1VFjrdLuu8+OQ+PZ1DE73VUPm73DnfOuyibrznJBE8CAScvJNHajsqQxM8lsGLPOaSFTwQpDo9DLriu9yoeDxT8Ky8/tPLvNxm5jw6vRC82eV5uxeSz7yiBru80OcZPK1rp7yJo8a7HO2WvNJgA70mpmo8TxWYvH2mNb3jE1C8tbjju1KEbTzaRTu8JBG+O89oBrzolky8HUyKO2SWXzzl+L48GAjSu/t5YDyqm4y8NMHwutmL/zvhqM28vRayvIltJ7299OE8zz0ePeWPwDw55xi90YxAO2pF3zyCUb+8cwMLPC/n6DpX/II7tM6dPEJRjTxCR0C8iWDMvNM517zrEr48BibBOW+SxLyJoYU8z+o6vUXZ27v2oC+85kGGvN1bAz0WCKQ8g5oQvGBqKrvSX/687mMbPUkECLsZPsG8/hTuPL7c57xZ/GW8hldHuogCKj0Q4qu8jqqLvCIWc7yrI4+83sNUPGS0oDw/lrQ8f1APuOdyJrySy1q8cHDAuzCJZ7wzlc27u/gPvOCh9DysQxg7ywBpu9Em4jpNTTI8zHDIuyM1k7xE1zE6Ol83PN2/lbuqLKs8h2QiPXPL+Ty6Pqm8yAHBOgtZhzxZ0/282HBTvPNwZrxFli27AZSqPLgef7xZhwM7FE/2PONfsbwJXHm8XhF8PCR8Brvpdwe9nTRTvKnUeryHLmU7D1ydtwGwRjxehCO94O3CPEUukLs8QuQ8KAuNPJEek7yYpRw9Qw+aPORLN7xXaEM8sJgDPB4A+bxFO1c8X5ZUvLHHQrxxn0S8XswOvF0PlTlizsa825sHPVz4nTynDCu7CQqSO6gSe7rDVw28Q/utPAJQJrytW5Y8EHEiPPkO7DqR1gc97HEnPN/ZQbz6crW7AfxoOYfN7LrJd9I3aekku/+v4bzAUP08+7+cu9sin7xraxY8vWaSPDm6l7xTC4e7JSycvENNVDwxGrO7ETsZvR6qBL0f6Y875tRsvLgTsjyCmja8bCiZPGnpYzvNkqO7CGHSvO1c5jxARWE75tXwOqPgID0vxyW8GT/Xu8CMr7zF9dc8LxYAvU9GGr2+7lq8lBH9uz48pDw3Uco869UBvDzLojspO7w8i8GjvACqBDwj3B28hyEXPA6MHb03o288A5PFO0fx+rxHcOE77exIvIqs0jw01dG75iUDu+kSEj2XsGu6P0lNPftTmjtjtRg7iAvkOhinnrwu14w8P1bBvDLK8TwWRlg7yM+OvL5/lbs106O8tLkZPdqbdrxC0xK73V9WuvxVmDukgMw7BRgavMaAAT1zORo8zgYBvd60pTvGoXu881Yau+hT+7tWiwA9SMZyvNZQ/rxS2B275Cc+vCAO1zwggsG83E4ePTMV2rwEO5q8NMApvb03T7xuXdS8VTIiPL8+kro95gU8jROXvLYuUbxGJmE6Ct/IO+y5hDtCJMW8dNOuu68Ij7tzCkM8SBpePIahtLuQdPu7hjjnu7s7pDz98386yNogPJY/sjxilgS8gDhLvA9us7w5Pfk73LMlu65lnLz8lTC7D+4cPVrJGDy8rbG834YKPHeVaTpO3sw7jJnOPDBPFrvUpbq7Kc1vPB+9mzwp1BO9eFE5u/jQEr32uXk7j+wfOsSmt7yzACw9goZFvJBO0jvZJ6Y8wZURvcZtpzxWMAe8reMOvLjfDbvPx8U7pyZHvNGaHrwWD4A7NiMcPWQ11jyNupK8qruSuwfBkLsIh6g7sSFGPK1yVDxUimu7za9JvGnf/7xP7z88xrppOwAaEj2Og4k8uPxbO11tkrs7nbY8df22vEC1Vry3dc66p5y3OxMDDDsG7Ts8eSsJPA6P5TuqEbu8NLhIvF8cRbu8A0+7BJK8ul1tvDsEZiS7PQbWvNyMAz0hAVO83RobPE4mUbtuLa872j4dvNYHqbzw+Yc5+pKcvFYSE707Cna8iQb5u5liubxn20Q8tnSzO2D4CbzvU6+8vHaovIKAiDzo99e7ewLVO1VWibwCO5W8M66TPFpOzDwgoWW8IxylPAnIujtFtNW7evBrPHODurxlphi7IYdNPD0KvDw4+uG8AHbCPK3wurxveQM7TJajPJs+gLxRSWY9ifXDOz8fdzzlxz08DZdCPCu4zjuEXvK7yd2NPPnsGD2movG6yeCVu6Uxjjyig/48tNSnO8OIGLyF3Yc86jQPPLdLbryt61Q8x6KMu+LYtzxteZ68zvOMPEtKMTt6/RC7n4BXuehKyLophTA73uzUvM5btDyC9W88AiQ9vEHN7ruslQs5+GZ3vPIddjv27n28zcMkPPZgFDys+c47LkXfOxHQjryPUJg8SicAvCrktby7FCQ9AnpvvLI6Hr0Ld1m8HwpPuwU1WTrmHTI8732vPMAmMrz0WB09PYc1vZ4rYzyurqW7iEBNPF/Wjzw8uPY7CH1+PMPNzbt+S8Y8At/1uyNtmTxKEGu8dqwCPHKNrrvfK2c8Krs3va9sIDz2X+u8qr+cPGPSBTyMiA+8igG1PNFotLtNLZU8gq/Uu1Rr4TwRX5s7VDYKPPKWjzoAcRU802SoPJNsybvKin08sx87PImnhLynJT+8HZyNPIUB/bsabyI9XwvtvDBMmzyWFvO8kjZlvGcosDyfd++8SaUkvT5WprvjORk6g4oTPWSCEDuypzO7WnyzO7XKqbuk8hk8m7GUvKL8ATwpEr67PCwNPP0QAb0G0Uw9Y4MnvMv/+bqeL007v7pHvDIQErv7rKQ8kq2mO8ipvTtdcas81pT1udLBhbyoDrO7ifIQu67SUDu1fRK9e7qROmdx3ztNqI67G3GuPAVpY7rmkJ88t9K/u149ezxM+ks8AQ8LvSoiwbxd3Wq8/nMtvK2PJTrANyY8JVjKvJjMtzuKCDg8RGZUPDby+zwreJc835ThPDkNizxBYeM8xeXdutqyGbz9Nzu6qUH2uoEKAD2ISdG78bWZvDXlEL0+mG+8nSCWvBW9HDyCnji81jm1u/pVu7yDdqs7UTAgvNq2jDu2B6C7G3OZOwfaBz3yR0G8/RYXO7HanTz+krI8hZvAvBBON7wN/W68C0MqPA== index: 2 object: embedding - - embedding: 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OWQruU3PXLxtF/e67cAGPeJQCbrr4Tk9yFsePKPji7z33B48UlaSuwmQvzqO4uk8fhTsOnaUwbu0Dj69PJsKvfYjLjzb5qO7lu4ZPdw7nLsLiSG78bgWPS75Lz3riJk8BICrvG1/2bwG+oa8DRgivTTaBro5+o08MbkhPYDBwbxCqMw7pgOru4RZIzq3Zre755gLPBsVeLuz2JW6ZO2UvFn1MzwStLW8Y9Bxu/AZ/7oa8148wBm8O/R3rjv5avS8FpvVvD2JMjoSe407Dlr+O4MDC73LAay6BUgEPUOPGjr/G1w9xeeJulokx7uKLWk74PUlu6cl07uZ9do7QUUWvMDDQbzruUW8TLasOwidg7v35hs8sJVcvEB8ZTxHDyy8V1YvvJIZgbtAmOk8R3EKvJVCH7thp5s8FHbfvDbH4jyMadk89zboOxL1NjxCnz09GzYsO6Xjvrx05487GFsSO8lrBrwj4sw7DVH8OhZXmTzWcoa8/W3su9QMpjuKxWY8XbWEvLSsUbw+eqY8mI8xO2GPjrt7YYu8XsLRO3g8tDvSe049ZEbbvEyRhDq/Yna8zzG8vHwxtDqry248bCuBu6Nmxryxj+M8colYO5gNIrzL+6Y8u12EPENzbDyvfqk8fCLyu/LZJDu6d7e8JqpwO97CgzzcWYK9utPfu0q1F72G8so8WZXQPLF1hDy73iK8/PZ4PC8imryhQLA8NbyAO7lI/7zYZaY72hyQO2ufGTxtc4O8arsYvOwTi7wwD/q7JFM0vBamG71H1Xa7olQXvfxKvDtxKl+7H2AZPI1XUDtWjnI7kCv4O2VwjTyS+Qk9K4D+vNc8KbziSzS722qiuyi0Tbov4AM8r/T3vCDV4DzFtYW7lo05PLGJpLri/ke6ZxFovExOv7xxlw08+CjIu3Mt1ztqFjC8p5ynvHvJIbwIA6a7eaYuPHKXo7pOMWM8Ht99PPiaND1HEiU83liQPPs2kztDpri7+9iZPMiO5rw71T88mEl1PGBaGTwHNi68yjyGvN0IyDzx9og7FkfMOkErory7Csc66CjBPIJtfTwqstY8+1wvvIaCDj1SQ7+83NvwuzBfi7vebgM8wD6nvCWzYjyjvCu8jr0APIPufrx+ua+7b1v0vKQ7cDyebvo7mSGqu5lTjbuOuaQ8B78nvLH9XLvSTea7XSEkvClc2Ts0g6y8+4twPBgPlruYGUq8oB7FOrqYRrwxfUg82PLgOyQ8qrvpWfW7hITIOw6KYjyD/6Y7DeglPA4uVLmqoIG9A3HVO6i4KrwC/fq88Xuku9gLMbyStAq8k8t2PCGX2rzqGvq8CZDWu1z4B7xKbR67pforPDv/fbxN3ii9kR5VvCbBtLxTO+O7SHk3u3gMGL04aim8Semvu8iqiLuDJge8Ee53vKUv5jwEvy+7G5vZvLjLaLzQXq48BMEaPXUOazv4oBe7OWIOPMl4dDwEzjW888XautlJ7Tz4A6i7A00+O+I3CbzEeSC8FY2BvFQfirur9iG6ZFYdPOm1Ej05pb28HbMqO/b+lzsk2cI83htavPY4EDxr9i+8GJgcvOWsRLyYspY8UHWFu1MRzLts2mO69omvvAroYbxBp608ZDvNPDFBMryxEYQ8PgN4PODx7LyWJmQ7Z7e6Ow9dwLqOwug7+CyHu5h4OrwvPSM9YpSoOwORKry7r/S7fHfLu22W07yrose57Sd1vaf0ljxtITk87WORPHZ2HTwWPHI8tTTjPMo+LT3c4Cy7bE08vH3lQD3u5nW9o4kQvNO5FbtIcjQ8xxeSvKEYMzv/o8+7hy40vO8iHjx94mg72OImvADh67yxIAS9V8Fuu/Tjl7rE26u7Ulx1vEqBjjzjQZS8Dk4GvMNfg73fi2u86w96PcizqzqW5xw8aUWpuztoJT3SK5i8TWvBvI/NPbzUdnU8b9FWPEV69bwF9oi8vLwivFlExzxR3AE9K2wMvJT4rDzf3IU8jYPOPElfVbvFhI08w5YWulFxIzvSGkS7Syv8urrrkztFuYk7YZLaPEaaXryEXK47XFKMu/beXDw2eE68SjUevOZ9uTx2bbo8MWZTPJPiWTxPrSc8LE9Lu6ph5rzzntc6FZEvOagvZTv67AA9ObJbvNTlsLyhMgQ8TidOvM0EC73iijc8QwMSvZ0AXLzCzAA9ggBWvBo1xTsU9yK8GvNcvJ2jsjyQMsC8lcgdvf70X7z4Rv47b3+Nu8dot7zizZs7F+4xvIeozLyB9fU8yqWxu4h1QbyqAuc8g7ItumCXmjzzGR+8rAF3vMb8Vry7ESY9u4F9PIys0zyGfqe8LJkIPAsOmjuw/967BR2ZPLfElbwKO6E8yb+fPG7XVbttmvo8ZfojveQNmbyQZIg7rwn9PBQ1hLzIVo68unsPu9BPgryZmKS7m1CIvAv5Irzq0cc7di4YvOT6pLzLLRy9nJ7ZPFEYAL09H9Q8KlgtPNqjZbt8/6I8tdgkvLJQlLzkEDG7Tj8ovJ1qlbsMjZs8TzQOO7a3WLu3oHQ8FULxu3dAgLv2Qsy83WDPORq7lrz3rR48h+UcPEFjJ7ztFco8VrktvEAQtTw3bvw7TUV5PCUugLwbaU+7ulaaPEB+Vz3Fvgm8h1fSvOX1rbnaB0I7JTygPBCzMjv9PmE8WESVu7vZf7wxc8g7L34HvKo5XzwhOQ48zKL9u9J4mjzyxhs8qDSvOmiE6bzR0wo7cKEwPIgJCzyIQks83pn0vN0zA7xkjhC9NI6lPNi46rwcn5i8O4kcPbSHFryj9pM8htcZvXzIsrtIb3Q8gcpjPKHUCz3N6208n9LrO15GFT0nka06I4IOvAxYbDw7P/u884H3vA6BI7wGZ+C8fug8OyLeRTzgadM83oiiOt8mjLsnbAI8oCR3vNxsn7uXCEI7N6KGPGrJF7yAaAe8mmJwu16jWryOxKu83mkXPDXc9Lv1pQc8uDCOPDkWOLzACDo8jIXxO8dBGzwfxe68NAmlunwGzTxKlVk8ZGG9u0kvr7yg9E46iS/7PNCH2Lz9z2i7ATA3u4MlkLwAYMw8A+sivBT1Aj2och89e8y1PBkXezyGhNI7/hm6vPm+9zwLqze7eV9XvMErFbxhcg28E00CO3r9vboa4TK7gGZyPEIvsTyfC6q816YVvVu40Dy9Th47wLGsvIr4JrwzeNe601A9vQ8iXrxx5LG75PAdvM/vTjo668w8jZoMPYdWkjukVFo7G22sPN/rtLywgFg779PzPKYx+Dtufom79SgRPIKYijuT1Ts8HkIMvdHoJL3xo2u9gZh1vLZ9IzzqxKi8V1RYO3H5/rynwFe875GfuhAsirsIfC+9IdNRvUTfnTt0uPo8YxWnvBLearyr4Ak99NoZPTZc8LxLByK9lTfJvMDb/LsYl5a7GDizPMKC6DwQRwg8hp7MPE4ovTrZCfo8lFX0u5V5Qr0DYXG9rvOjO+D7oLtAHrq71a6tPNthDjzD2tC5/JRlvDIQFjzTzbA8MzESvNeMoDvgOZA8xk17PD2237wQKau70AS5PNJVvTlR1kA8Y6B3vN+CxroapwC8RmPEvAEbmzsRJ/E8iwwBPMACFLw+FJg8rHhtvFyqirxRWgm9IEgIvFdBQDybnF08pXG3POorB7pz5Qm8GVRJPIpe5DvyUaK8krGYvGjZgTsZnVQ9iRo/PP9t3LzBqwU87SmIPJfguTqJCIA8EFqvPFQ6U7yEVzG9GesmO95f4Lx/AZ+8VEosO//tAr3oxzA7Jxo9vFTW/TyCoYK72HjgPDovgTzmTJ08ctSuvPe6C710H8k8QyTevGVJ3bxFupu8sa7hPKwukruaIuq7rWWavKcwMTwyP6u8XlNvu0v2lrzr/Kg8TibjvG5rHz0ZWV67cdI9PaUvzbxUVou7jzqXPFuoBDxD/uA5/xCdu/RmjzyrtBw8d1/NvI9drbxcBDQ9Tdnau4wKVLtBIme8l5QNvJ5vFzxhaO685DaMPE46ertX2oK81PPzO2djrrxQmMA6lc4tPImzubxG5Ic7YAwMvIwicLwAZJ87k+iDO5/iWDu1Pg+9VzNPuNg1Irw87j08m/hIvdvh6bspMSi6ADBjvCD95jxj24a7VxGgO3JuRjwdQpQ8G7KgPRQjOrzWxme8yYIUPAcd/Dzkv8e8R1+wvEXLkbzqTco7UVvfvNsdhTs41QK8IMBEvDSFHD1xzbo7OefnOg+QhrhfAaU8N9+evHXIwDwqbdw72ECBvPhgHDznNpm7p8cTvdwsMrsHrBy7sJ9rOs4xibs2+iq8go1NvTBjlDyCoUQ91CLsPG8crbyY4UU8HUAduocQELyCRyK8EeMiu8oNELx6h928JSoVPX8v9rvW+YW8a3yLvO/s5Tz05R48/E2SOt2JiTzk7pw8+dUGPUvn2DzQX7i7iM8ZPQDxrTzsbmm6xeg/PKTh1Lu824o7+HGKugc50jskk6M80w0IvTBOyjuH+O28c+U0vIN5er0lUrE8hq3vunPgITxGe4k6UIKuvF5+4zxHEUS7x81HvGC6qjxJhnY9It+AOyzTdjzdrY48i+rZO2iAtzvh8N07y/ayPKerFLy06xm8wTYIPGyUs7x4zUe6XPibvOazurygj5m6w+OCvPxovLwMkqe8TkLvPJTeeTuraEY87qsVPJ0lNz1fBzQ8JaSFvLivu7u4BOa7bByivDIT3DswruU8lpiJO0tQhTwd9Z65P+FRPcyCjbzUW787hPD2vPpVPDxo/SK9G+9QPOEt5rxS6c486nxRvPaHmDwlmao7BhUtvfiPLD2J/Y68WT0jPAbS6LsYoTy8rWYAPPpnvTpuVrE81ySauyCCsTzP4CY96pRaPHyr/Lw6orW8F12RO77/qjsS9Fy96Cc5vBtaIzxxmI+8v61kvRJ/sjr+bdW84WCeu4A+47wRIPM7K70cuxRzV7t+ARo7n2b5vH+4ODtD6XM6R+qKvBMY2jvTrqy8UFSAPAAnkD16xg88rGuLO4OShDzSiTO8NfcgvBOE4LwZUek8BmfcPGjb2jtSIiS81hqMPBkwcrw0AVg7UD9vPMZ0/bwzkAE9K2WQvMIXobvBUmo8gboZPDH4Bbz8sz+8gtScPAdU7bztXWg8kiWcuoskULy01ce8r/6pPA9VTzx4M688db/bu1sWDrxqK0y85NwSPMM7N7yglwe8lJO/PDFD4TrEbru7ZN9XvH5A27ub6JY8ghQ8vOHf/buk2Hi8L6+ru3ychrwm7ZE8IR+LvAFpwrxyxC47PVVnPB1FgTzrLZ48oQvYO+UkQzx1UxQ8SWUwPAPf4jxj9gw9qK1HPJj+nDxTdQa83aThvCMo6bzblWE8DCslvCy0AbynIoa8PXZzvIIAqDwNl5450eJJPKFMzLwfxgm9mUcHvPyYVTtG/Fw7wEXDu4gBjLzrqpG6IyYDPeUHsrxNFUs9pxiJvHoWjzz+pI28mlvVPPy7pzsjMTu8OGYYPCVBjDvqy8S8gYWoPKVKJLvOkiW8QP44vb8UoLvi2fM7iFUlvcS4ZrwtNPG8wN2bu8gS0TyvCZG8FrWLuzbOtDyJOFa8uKr9vHawnDwyGQA7mJ8uunFgGb1qggc7KZDPO67flrxv6z88gCE3PQBmnryDzla8H+g4OzlF3btnJ187xu6rO8+EWzyNEF+8A8yHOY1/Pr03dZo7owlpvKo+EruHSzK81nGuO2vzuTv6fjK7iAsLPE3DJrx2OGO7mA5HPLDNHL3QSI28lOoCvXf5ebxPpXG8VQ42vDyIuLyJUqy7E5DJPNE5a7ylsM07ZRorPaP1k7wQ8Iq8vqeTPCjzjbsC7UI8ZtDMPKDLU7wrj0A8gdzUvL0797wgg327qqyrvMMTy7sfZ926cPELu7PsFrxsjAw84pwjvfvumrwDdgU9myVSvKodL7sfLsU8K40dvY392zsdzqw8HC+Hu1VeojxsbJQ7xiWpvJo+Nrz+GE284CIsvTm40DxADLI8qA4gvQkEOD2B2o487rCZvJvDkzugWZS8ahLEvMIuOT3Fq7u7Xw5HvBjGxzvTQnu7IbwDuxmbqDu2GNO7DRBkPEJrHT1fQ/+6n1WSvGynAr1yyW+8+8/1vBtW6jyoMp88nxHiu6zCbz1YRwa5u23SupoPYLyCIyw8KduEPHb38Ds5HJY8lZElvINaBjy7E6E8lTYKPedZv7q172E6fyiAPOmEGTzWLVw85YSZutT3/TvK8lM8wZGhvBt2cDwxKb48NTXVvNmuSL0ylY+7JQk3u+sMqrzHVoY7f0/dPDIokLzMRrw6KfNEPCvnPL1LSc26jHIzPXTEPbooDw+9BIXNu5dn57uOUQk8VDS6uxzYND1MDY08XptIPHvCoLt+A8G8uYOYPEAQ37yrQ8G8ZP66uuOKG70QiQS9WA1wvG8K+Lxmgh68TckSvSTqOrs+gUA8Y+mdvDV6ezqhqt48MEvzPEopAbz6g527/iWevM6UcrzH3US7Vb+VPKlI3DlfArA8a3tVvP31cTlfk6+8TWe2vP+qDTz6CS46gW5fO45pDb3m/2Q8516bPGOmHDzm4xG8OGtKPLDDsDzicMC7V6E/vCLL9zooDco8JjUAvSwhHzu+LaC8ivqQvFg3wbyy7vE8H3q4PI+zjrwkp5I9xdWxvOt/KTv1CjE8Bl8rvDxv3jw15ti8iMGMPH3o0bzLlr24cIf6urWN+zysvwU80UlOvA+tybwx13c91KbDvBdsurmrmey8HqZRPC/TuTzLgCi5TLnEPCvtHrz28Q+87DR8vD7mTjy+3La83WyOPK7MGT2iwwK8dCrku9OSxzxISYu7UTNCO2579rt173w82iYDvUqPpLpZZai8kS6XPEcHzbxtC5O8ZAs9PBx4Kb3YdwW9j2qSvGbMkLue99+8fLCmu+Y/Eb1roAw8EfPrufC8m7sT3g67UyvGu3/ZrjqUSwQ9oIdjvCWsvTykJyI8Nm7wvPvMIryUHQu9VVx4urjYPjxBqw09YFyYvCRfYDyTm586x0RMO5xIab2hkT+8gbdWvA0VojqxN1+82buEOz7isDlAaI27+WtVPLQGkTwPryM8URtmvNEOGDqgn4S8QG7/OSFtXTysOvm4NpLHu61lETx/lpG8rVFpu73Y3DzgMMQ8nvAiPWYIOrubAeq8SFQTve56K7250N+8RzquvOdzaLvwN+K8UGfsupH6zrsuZtG7IQqCPQI5Y7wOvIk8AKEwPAztnzzjcyc8/Lp3O/gxvrzUE7C7xLHCu6pxyjvJZUe9vdqvPAXjgDzD09o8H+eiO5GykTw2hlE6hCRovHxAt7vRHs88ifvbu7DU4LphmTC8UrwnO9GsiDpHY5+8eUYjPYZ4/LxYos+7R5KiPEftnLzaAzo7IucMvEPGbjzu0eE8A5T8vG1mLD3P6bs65zB3PMdaGL0M5ey7RctcvJ5vPj0N4jY8LXgyvD5EZTpQXYW8wCkpPI0svzu3oKu8bPd8vLtVE720xhs6RPWDOxQRZLysSns7L2/duqlULLzN6ja87g7juqjN1zzaE9O889oXPIu7MjzkEtG8nBW+PCN4JbtkOSg8ZV2avCvylDyoWTi95jrdvAwDobuUdpy7OWCGPIF5+bljCMW8nB1+OZqXtry+6im79FQOvYEjbrxjKP27hpV+vCKdvzy5p4S860m1O4T5fTz8qTU8tbQavEWCLLyk4HU8SQtJPKAyjjxdQNq8y4lnvMoYzjpFIVI84vb4vKKFP7yQEKy8thgwPR1NGDv2cmi6HFcOPQqbwrzvdd688yf1O/231zs5xxI7xQXIuVAgMLtxeao8G1nlO5dEubtSuja9ZhMFvfb2sbyR5+w7tmcJvPSVxziPFMY8e8z3O/xLnrzOC4g8tI9OPD2zE7vi8dI735kDvbXmRjx1hU46Y2WRPJeLiDs1vDO8WPnGPI+n5zxvW0M8CZ8lu6EgUzy4oR+8cOS7vMhAIrtaZiG7b9nRut/coLwtoZa8br1Juc/WL73aaaa8YjYAPK8pSLxEova8IYggvDjAyztH4xg8zq48POxt9btlwrE7VRTHvDHIL7ys00U7XoFHPHUmrzxtjqi8iUeDPH1j17xUgNm5tv4EvTdJOrzuWYE851YEvQ4KL7uEgCC9GStjO1uDkrzwlVI8lcUOOyyph7xI0CK71PWxO0iyrbu2Hu67c1nEvMfOQLz04dw71jOIvL1Lkzzcazc9AE/lvHy3DbwJJoY8kg+GPD4AarxL1yq8UIUOO8eZOT1xLZU6diXfuqoX2Ttkrhi9A/mkvNzx7DygNZy8cNYRvXGa1Tu/mJi7bCQru7Lbvjp5pt+73YzTvLBjOD2aKvm8S9tvPLYXfjy1GZi7SKhDvMjCKTyz0t67YjIIPaJtEjpjgT88sNVAu45sdbxwZdy6U+bSO4XkzzzYce+8h2KaPJd0Abw6xRo9YypmPJ6KBb0p8V+8ZYQBO1z+dbyo8/c8zgk6PeYxFr1rxfQ4GEdKPFukT70aOfO7fRkHPD1RWjyhz/u84TdIvE13NjwYfrI8nbYoPRhLh7yfDag8IU/sOmNCNT0oEny8rRm4O+xN+bx4vqA6tceNvPCPvTygloC8/beWvGaAoztS/vg7pMQrO6c1Ar23RwE7SVUOPP8IIz3LN1Q8pTusPI566Dww37U8TCQHvEyrS7ylb4c8oKP6vMCkMbyIfwc9g06UO6MSFT2T3kc7rouKvE3eijtc6h28zMBYPJVg7zpR0gQ9JYYZPQdfjLtPTzY8ruAgvJYGPT3IqW+9lVX1vArHGbx4j6O8Xc7tPN5CwDwcTQG87RGVPCuvELuiSys9NV0LPbF1Lrtwfyc8CCHMOvL2nLz/XHw8AVSau+3EnDxP2Ic8QrkWPLwuKT2JWDG8Myz/O6dpB73ceW28g5qvu8WoRzxx3xS9hfWrvO9MIjwdLi+8L86uPKdfoLwkvau8m593OoCkjrvxS4u8J/mBPAPaR7zMNJg8KjNIPYcRRrzPBHe9v9CGvBj9kjwvyY+8BCIKvGcSEz2w2NE7Onx0PHS3VjwHHF88MFZxPF0B5zy4/iG9Cn4XvByXSrzTB5m8YimUPGBT+LupzcG87b60PJ2uqbzT60s6turtvAoSTTtX+gO9FKcyvC/rFjyIvxC9dm3kuwP6ObwGHAw9a6mxvJCKBz2RpTE8ln8cPItcGzye4H+7DYkFPG/aSzxwgUe72Dwzulup3TyWcvI6an4OvOBwUrtdiys85u+DPLXRO7yw4Uo86PsOPbdbn7z6/Tg8DEZPu0GNobz6aYq8YgwZvEwuRLw1REC8/PQcvCIG2znndEu8KuNpvHzsojxn6Ts9hyx3PNOhOzwTGkc8AWJJvK6gh7zuJJa8rVLpOScKgLxObKG7Nxt8vBLqd7yIaxe8entWPHcqJDwjRr+52VDCu6/SiLvbAj29G2K1vH109rrYsh29aIA+PJAyjDyPDgg8nS8RPbhhl7vQpAk92kejO1C86DtJ31e8+QQmvWtq4ruq6fw7A9dPvUJgFzyMmQ48A4Opu3NLyLoAiQI7SJQQPDLjPjtVBMq8tQCUvHckgzwxTW68ck/fvOEvsjzQoZ+81G3hubYA8rwEFE080r9Vuo1dLrxwbyO8ruk6PDS65zvLWpM8LF2HvKHYWr2Ad2C8uHDdPLZgujyxQxS9vGisO58UBjxiYO66qZA3PMwMWL0Avq+8D7/oPE8zVjwIZAs7LHakvGZOv7yvlq88QeWZvOT9kTyLTxq8+X/Ou535izz9YJW8vZNeO6J0sryNVCu9OjITveiSk7x8Z5i86v7nOi0bL73AHAI9QMvJPAkIc7wu8+G78XevOi3NaDy3lyy8No6IvNJkVLtMc9k6KCdzvGGsYrwmaqg8uI62vJA+bLwH17G7js0+vLli1bs9GSA6IwANPN7wVjwU66i8dS4WvNtlobwcBPi7Qt/9O7BCnjyRtVO8vL1APNaoxTucJKC5xHGZvAXsYLwOj6g7nnK2OmH7C70TSYo7dkziPE3bLj0UYCU8sWvPPHAmCj3aHyI7Go0cPchvybyn6k06bJeWPBYDHTzi2Z88oV1/PK0MMTxoLaW8WslXvH5hp7zUeUI8WuZbu0+EsryFKnM8SaECvLC2YjwW0xo70ybsPDYDL7xchDm9GirIvNE+SzzBFqS70wyQPBJkWDxtEH88lKpsvGX2gDzcq4k7NjNoO9W9Wrxtpqw8ze1mPFAFijsFFCa8JXARPJmVirwUTlq8R2zAOwih+zyVVrS7s04kPAIkhbriokW8KA9bvPvEnDy680+8OGOSvP/R6Ttpeqi82867vBvyFDxY5cC81umduilijLsdrlY7Pp5VPGVlKrztdbK7ZKv0PAxutjvcZNO6iRgLOwB6j7wcJ+C7YyAsPK6IAT3l4Ig8arZxvLIaXjlJzBC7df8jPQuJubxN8Sw8O9GKvOMto7zNKfG7EQBmPD4GiTuYRBY9nxTDvCHkDzupUiQ7nV5/PAOdWTwEQM68mwPQvNl9VLzIao88L7RJu7zPiTxlUsm7xr8JPYcQfzx3w526nf/ru0+7M7wi2mi99asMPUJx4zsMe4g8M2MxO0uRirzwEcW8BZSAPQgH/Lwu3Us377DKvN/kd7x2sk48clz9vASBJzx6Vtc8QvhpPI08SD3jPN8876vKu0VghjweBty8/yHovCL1YDwkJ9e7bYgTvPVbobxQ02+8RLjOvGznT7xAa6A7OPmBvK2ABL18eJU6biIDu0h8OLyFfgG9SoWHvDKunzwQ0gO97NicO/LQALyauZu8p2Xsu/kKyDwfBMa8wET1u1/ka7jAjyi8duOOvNkLLTxC3pO7m1cgvJEq9bzWXd073IYPuyFHujzKyiG95Y+Lu1E2Qzyg8Qk77faIPBAQ5Ttwol48lsYEPPujEjyA0dw84GOcvAKO1LwE8Bw8N+CQPMxG4LzlQNk8zpYTvfluMrsyGJO8CLYEvHy/yjxCXUI85glDO4HaW7r5Pia8gGwKPTFsx7sx/aa8ew7KPNHar7y6dZc676OLPKDs5DxhyL67p4GGvJ5lj7uYDbW8xsHVuxq1FTzKeka64fIFvP3Gr7xZrlK8cP+2O6Jho7xZiRW7wJ0FO//NBz3vQCw8ZFgavan0FzwMxNI8XjvDvHIaMLwrDK27DLSWuyWdqjrfJe887nM5PVZBlTzlmc676p4AvNcbwDwzypM6Iz/rvMB+kbu0mhW64EfZPIGlxLxQc0s6aO6ePMF9/bz8nJ+86S5VvJs647tq7Ai8qcm9upfHLjuX8Io7dBQqPLNwEj26fy+9JN20u6JyXrz49lI9vVnVuxk6lrtJ6QI9wAyePBtoCLqI5ZI6pQklvKXVnbwc/MC70qaIvHsBFTycIQC72cI6PLQhnzvIU+28sn1rPF+3lzzjsBS70cK7PFIYSjuZjzU8+C+gPN10KjvL/dM8CdvUPPtJ6jlj18I824pVPMQXWLs9sgY6zCi+vFVpmLd4USC8epSOvEbyibxquQU9EtDZvDzZpjtxNqQ8TqwfPWlt0bpwY0A8mNYEvbrjCzy04C08SELuvLk4G71ZbIc7RkPXuqiN2LszMa68DQNOu/BVqDulT/o8T2b0vOnjtjzVhQ28KefDPIMk1zvTQ0+8TiaEu9EAerxuUSE8TznNvDYSxbyjRZC83o5FOoTQzTxfT208xrxdO9/BZDw8VSM8ikkgvHhQjjwYVgI8hVJDPCVXG70nkOi7XeGQvMyNybxEo4K6BPrnvNCnWDsQxyy8Ublmu0llOz2xB8K7PkT9PAE4XDy+laS5wWtNPCApFr0aHR897um4vIMG5jxI8do8qtZPvE3Ls7wHFhs77voUPWt0RbzkGAC7CRLOvFsvszr1Pho8/31bvF8DZzz5Gr48Zw3gvAmcRzyn18K7tBMLPGeaXjxyaxE96ToavALZKb1C1yI8s8Z1POqLGz27CxC982l4PXSSAL2/9gG9Ns1kvNayJrzxQ6q8ScYWPCUdfTx2JRS7BC16umIzorwftPK5Pq68O2Kra7sHDci89bPpOmgHZjxN75A81KiJPK3BuTwLcSi8WC2avDDB+zwvD0S8/QyEPAXYNDy3iNm7Mpi2PPlaX7sa/eQ8k7yXvKSeUDunDcW8lYgfPc6ZJzzU0yS8+D7FPOXvmrw1pbM7u/0bPcIwo7waPIm6PPCIPF3jPjqrjJm8ktl/vAb7jbzBdiI8+246O1mKLLxaOD89CAmqu2Nm3LrGHuk6MoLJvHgQbTycnIa8R27Vu+liJbvFLmw8RNhTvCd5Cr1Awe28Zk5vPdwz3jr7Hha9Rn22umz+gDvnfL27x1FJu6IQijlu6ic8U4axvL2a+ryE7NO7RNHFPD4JbzzFHFo740M4PE+fbTwvrs08Y/owOglwcryI0mS8/5EuPFYKmDwpHCW8KT6SvEUCtjomNqS850I7vCQjNLwRbn68oQbcvOFhGTz71Z07YMJYvNsU0TwIeJa84zh9PMvGPDzMTGQ8PSE8vHsRK7y4jq28WfiJvGCWs7yIBPa8tyqCO+WgsLw0aow6dKmDu/rtwLy3iUy8lU0ZvXGqZjzEyzy8QcnuuxW/g7ou8Le8MT8TPSiKATyHl7y8jB2pPL2A2rtpyk86H56QPP/gqjtcppI80eyYPIo9Vjz5s1e8i3evPAeYkLzYpXa8lsWTPAfowjvrLO88CKOpPGyWRzzpAmy8uM4WOwFTFztm11G7STijPOv0rzysDP68E4WHvCirtzsjmxc997DgON41jbxpiXo8KfUIPXGMELx/Gu08QVM3vLN9dzyOCaO8NRMGPanNCTwNL5y8UcDgO+3u4bvd/5y8QHG2vD7hozyjvJy6s28rPJ+VhLsrGC+5UwuxvAMVqLufB4C8F5qfuytCJzy+eA66VHZou/8ABjwtvWQ8M91tvCSqyjvXa/k81PTJvN+nDr33FZI7oG2AvMXyeTlbI1W8NPbCPHvqJTwQPMQ85LKfvKLacTsgM4W8fVEkvAmCwzrk6EU82KLcPCqlbLodpyM8oA28Ormnhzv/ZUM8zBbPPIjczLz4RrQ8j1YwvfibYjxJJmi8L3P1Ol1JITy7M9m8WjW+PDZA67wESdg8k80PPBMv9DxZZhc80BCHPL3xu7tuC/88HC0SPOP7RbwSKdI8kUi7PC5gaLwquCy7l7kBOz9FHrttBwY8I0QFvQPPETyLPjK94F4SOp7XozzRGdi7tZEYvWiLCzzGIQM8LNlCPVUEYbw0AfS7UQMkPFfUwbupa3q8eXiYvEO7rTtVKSq8sCQYvIgknbwB/Gw9Xt5GvLb0XLu+I0G8SWgEvEsWrzt0SQo86Ic2PFn2XLxUCKc8OS8UvGhisbxjnoG7NhydO7EvyDyJkn+8FosgvKoKxDxzK968Cw9IPObyHj2jEoM8uzkQvUiKsjseonU8rNtfvDNeuLyC0ES8Pd2yu8xtwTrh/KA8T565vBspijyjt7k8xGDnOw+ueDsCVXw8hnBCu4ujkjwwhu48oJnWvJNwoLwr8j87gA6jvDCA+Lt9k1Y86OEPvXm3u7wXqny7PYbsO9Iiyjz6nX+8JoBwuwh7HL29qby7yQUCvUv907tOM367qPwhvF9GVjxrXuW71HsCPcupljznkWg8LeMGvZP/bbwRbKe8Em3POw== index: 3 object: embedding - - embedding: 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GSoVuZN5ALxZPZW7PywfPcmEJLr0kGs9hEiMPL3OKDtIGTs8wa0gvBVoiLzDAh08eEklO7wyjTzv7kW9JDM5vaXh1zteIxM87UlqPcxzmLvuYXa7A9eQOyST/Dy9nxI9MECvvIgQnLxsxUq8JSUyvU1trLqYEtA8zLWLPOu3n7zIHCy8f2eMvKsrpzpb0RO8D1XvO4+FszqdY8i7ipdAvMqXrjx0O2S8BprBuslhTzyX6rG7JwQJO+T82zv7dbK8yvkSvdk9tjtQ09o7uffqOtoxqLuXN1C8BpcgPd3ZqzqWYzU9ZdieOlZ5GLnhU2Y8ty78u5VCkrww0eM7u1GhvOXAHrygRTS8irEKPHSX1LtdgxU8X9OqvMzHbTxqCze8XXWHuzSXNDzz/B89F1dau3RDdLsL9EM8/xwOvezgpDxAM8E8UI9KvOH0SjsgO0c93ng0u46TVLxXmni7rc3yO9I9p7y23rw8RE5CO6v+BDwEV/67Cu0JvakT3LrwuCc8mV6RvEVxMbuud3G6cJTeu2i6mLuZcqu8zDrUPGYe6Ljl8TQ9kFnHvAbCjLyjd5i8YF2avAKpOzw/w/E7yvXKuwpemzxFgr08TP8MPC33Vrt9Sm8822bqO9L1TDzVSxg81s3quyh5jLu1OYK8HDFVuwUCibqQBD69nfGAu7jZA72sn4o8LoWDPIuW+TyAiP274yCQPECq4LxWt/08uP1EPDd6D72a4aY7MrjYusQBiDz5nsK8zk2gOKmHObzPabe7enWIvMX42rwLZM45eYbxvPfsNTzBTWo6EUCOPJTjkrxKVb48fZVKu5ssQDw+q5U8iEvlvABuk7w1UBY7Ch6mOqfGErzYgE46aSrBvOXAGD2pUdq65SGTPNfNVTwDpco5842rvC6RObvv4IU8posCurzcbbvrnKC6aCTGvBLnebyDK3a7I6WrO2BLjboZ7LM8OmeOPORnaj0ughk94+7fPHgeJTtxQLu8oDA3O19WB7zfuR48gJ5JPBgpTrtEyhK8+JBIvIJVVTwQoC46biCEu2iJerqOaRA8IRLBukptfjwnOJg8s66luq026Twddry8ycpXvPfmArw9eT06uaJ7vCPxUDwjN/6798ibuhkD67z2EZ681XUPPJ88mjxFJ7M7d8InvNgNfLyn5bY8BGgXvGL0VLx8J3u8IaCSuyEIuzxg1YW7PskBPD3q7Ttx5hO86f7nu0X2UbwHboW6N35hO0Gx4bvH/BI7a2MAPNAYTjyr+xK6NFILPFNVKDx2ZVu9JcQvPBLK6Luf0eW8Ub8hu6Eq+rulj5k6V/NYPNELyLzDmgC9E9Xtu7kvs7pjXY45EoWXO4wZjbw9rQO9PWIuvIXnlbwc2SA8V9RcvDbK7LxzI+E4oPL6uy1aULxjs1C8mqAcvFyLazvK1po80CWkvMqTILx32mc8V1krPZuyfrunNVE8og4Tu9HyNDwUFJ07Q+6QOnc+rTwd5UI7rjS5O3tLHDyhlj07LRF3vJzuSrycgZO7oMrfOTs9zjyfH7W8l4YlPFfEZjwQT8E8B5dlvJz9obr90kq8unNdur1TAjzjugI9bHkQPFczFTqccLW7MWiivN0cxzrcEEg8p0OJPFwY6bt8tPQ8r49YPDz50bspRZA8fagEvY5rtTtc2wc83+BfPFf/qznGlxI9R3XSu+7YtTvskeG7EfWRvDLO1LtGmnG8O3VVvQv/4zvPbgE85F0mvHi+vTxQEyg8AsOpPEG5iD0oicW7CdiDu0Q0/zwiIjq9yY29uznJX7usTVQ8CREnvLl6RTz+OY68mGzRubsOITvjwDQ7gb9GOyoVYr1LPKe861PjO6UuUTxrrko7cBrFvEv63jyFIta6LPJxvLpKXb30k6G8lYoVPeFbRzyQyI67dVfeuiYmHj1JL5m8RUWwvMX9EL29DAw8ywaCO+hP9Ds4ZC+8of3bvAFW9jtb3rU8sC3+u+dU0zxb3aw8TLX8PE9Kyzwgng46cASSO54IW7vvpqq8lxQRvDzkzLv9ZEo811fhPLYRX7xSJXQ7HPuivGf8ADuaeX64OwHBuT/acDtwO4w7MxQgPKXX8zxwm6A8CZqcvFFH3zp3X+s8OWaZOrcFwTxFlog8MB/+vFnU9rwC8o485NGmu+cxmLyFye076NIivQ2f3bsU+9o81+YXPDtppzwtFm+9mbTTvEBXrDyHa4O7NbMCvYYPtbusTkg8ExDhvGl6Frwk0Kk6zGHnu36TGb1Goyk8ZxwXvNrmIrwAwgQ9ff8Cu3dr8ztpdgS8dnQAvZNJ8LvVE1k9B/uhPOpTuzxr+4a80zIRvOso0jy6cD+8x5F5ObcZJ72nnxA9gi2BPLg4mLvVsuc83fwQvdpLF7wmu0+7zhGsPEL+rDygN/+73iwLvT3nkbsuGUu8p4O9vL3kJr2JMww8rtxUvAst8rqMGOC8Lr99PFCbzLwGIXw6xwWxu56wGrzD+Uc9TODMvPTnObxFYR885sd8vE/8o7tndec8dL2gO3qEjDq/j1E8p7IivOnIrTyoxyc8JK+GuwVNc7zCBxQ8XN6eOuvrhjtx55a6WfhMvUoYXTwysLQ8HMcFPJqnULzDV848ikguPIP0HT0aUiS9j5WEvM4flrzjEfi6vqnBPBawrTwt9BI848SDvH18b7zl+5G8CXLwugAeqDwFtoC6352muzG+cjv2wIm6lizQPCRcejyhWwI6bP2CuRTDd7vtm607WMWpvNrE2btiT/C8ep6LPMLFsbhsqRQ7TXliPPb18Dn6ZHG7huiTul+UfDmzwWS8fhbbPCdISz1GlSY8la2RO+MJrTyPfcK76eOkPBffizz4lA69cJC4vFsD7rtR3eG8VfCsvDdn9Dx7Ucc8am1AO2hm57ucXoG87Q8EvCmUQLyY1x696D/DOxC0z7uH4EG7Ir8svO251LhrOeA7BqN1PAvy5rtTjii7bIDzPH+pLTuCBL08v33fOqxIq7vSfMO8SZ1Duu8fojznaPE8FSaQvGPfyDs3D5o7DeGPO27gb7xacuC7T2DJvH2hCryHJuc8SHgAvGAbID0KpPI8OmcCu9T7lzpajzC8oSxMPD5zyjz1BIS8ZDgbvD1LhDtg7Nc70+Q7vCwsSzxYapE7XlTGPPfwkDvDyo+7LR4FvUg/+jwQ3RA8BzeEuwZHiLz5CBQ648DUvKc8GTx77HS8cPEzPJHHnLtvlpi74ouVPDNQ+7vN/V68jNuhPDLnhLzeapM6MG/LO2apNzzyh5a56ZV8PMeOBTyVZ2w8zeGRvHH7lbyDqRW9anYuvAb4e7v/uHC7a7jFO3PvmzvIX5M7vQrNuyHt47tjywG9lZf4vPXsJzzzEkM8tlRPvKn/obzzMjs9jj+XO7rj0Lzw2Ay9qsrVvMITOLsz+oo7RMYKPA755zx7Fes7X7iZO+Z9+TtkpSU9vOH0u9UfD73RHQC9dhqmO1v9HTvGjYo7/Zg9PIce2LyeKMg6C6K0vOZZaTvEKs08UTtUPIbXFztIdag7M3uJPHe3N7w+dbO8rxcJPXmqqrzxMEo8lCM7vQxq67s4HgK8ilNivEgU2LtPZBI9RILcPIK1tLwIB6g8TFztvEXtPruIRkG8jyQoOvWc6jsyuaq84xQSvD6UeLzoLVM8djEDPKUh2LyVdkW7hWPOvK4ytTti/Yk8o7YHOcsJr7xbsoM8+++dPJr6FDzpJ6Q8ZVGlPDiNuTs0gvq87oruuvY7vLzca868y4HhOxSWf7yAaxo8ZphsvJOgcTxpEJ68acwaPauXHzyJYTI8cs9jvDh4ML0Us8y7+0Y0vYRGLbq8BbG8vIyFPBQVbbwBRJq7wF0kvMd3wTueIhi9VobjPF9bcDrzOqk8c+dQvG3tOz3YyUm7YimpPTf6CL1qb2+8omUkvGykl7sF4mi8RpL9u8qetjzNBgy8X43BO1PLYbxu1g894TU8PIYYr7tYcxC7V2qavHh97Ds+TwC9FRIZPKImtDxchgs8bzA5OyC/7rx1n4q8ZeWNvAmsr7wWDoE88N+9uzzyBjs8w/25r5gHPNWDwztPTQG9d83BO75vX7xKgIs7utO1vDoNJDxLnuu8OvGtPI01yTumzUy8KoqOOxdepjtQPSA7lAUYPYhbVrxU50o8gAf1OqEwtjyGYYm8TH6XvC29j7wBnOw8GRQ5On965TyEG4u7e9CAOyL77jwvuJg70R5SvBrI0rsiHtU8e2luvGNHDz2C/ku8qW6FvOqHnjseQA87O5TcvL0PtLzv6QY8N5akPCPjoDr2Dja8lyq0vE5riTvTpkY983RJPEgQ5bz2EgK7UeiNO+cznbxBKj27rOA3PGeJXbxBI2u8qnqlPHdbgjwzJUu8OwOUvF9+nTxZJdu7iC7fPGHLvTvUyrs8rx1mPJX/9TzNYEC82Xw4PTwqEbwYKvq6AhAiPYLziLtNVVw751NXPOuVZ7ztYrc7Fa0KvcFX1TkfxRA8vbO9vDn3Gr3wzwY9BBHFO/BkZju54iQ8T7nIvF8Tjzu4kq68MZqcuwNiqDz/Z0E9WdngPE1ndjxH44y74SVhvOmzqrpG5cg8EaGIPLzcELy6m8A7R5E4O4c427y+xIK8EhnruxRglTou87O733YVvei8PzsQS5S8z1L6PAdxxznEZJo88VeYPHKYDD2wyyU8s6u4vDf3vbzy2Ro8Y/j8u4OE3jzl+/Y8zTGMvKMjbzymCIq8EClsPSVm1bxzM9e7qqfEu2nPBDuT5Kk7aWCsPF4iJLwUw586znicvKtr1zwMrEC830SUvDSjAj0DoBK7ijNRPL1+HDyEPXS8hyiBPZ9X2zzpyxM8zCMlvbeTkTtXiPQ8w/+fvIbF5Lw2WCy8nvmPu95cWDz4B6a8Id+QvAYVBTru1c28LHFBvXU6PDyU+HQ8iC3uO8KA1LyzuWM7D65OPNTvPTvb+8I74KP9ugsw0DtVHYs8U39GvdF5v7yAsFQ8Zieou3KgCD38Zgo86OppOuArurykfWe75NSTu5757bwU/IA6ZEQKPZMNUTxeGYK8w8+APJSPwrwjjQw9u0IHPAKaBr3DqR89va6cPKc4KrxL7wE7TYMSvDqPQbyBtxk82kYgPOY3bLwvyyo7iugRvPgvSzrGHN67uc6hPC2ESbljwMY84bERPPlzZLx5JAi9e65AOqDet7xy9ym8EkWWO/ymCbxBaJ+8/IISu0XiA7sw9zM9FdtpvNsY2rspK6k8JTfkvHrmJbz96/M8cXJ+vIHCXDv2L5+7scS/PDrTDDwPDoM8AjjbPG6X1zobUk27Er7Nuz5rmTr6weU8cu8vPMnvPDw/kko8lQmYvEO077wYcwI98jOjvHDc8DuJtg69+iagOshwgjwWSho8d7y6PB2zGL3UT6m8zVUdPK5dDjyQ8jW8CZvqu+yuEb2gRFc8IVdEPR8VCb0J6yc9hvsQPDTOwjxLn6M7WbcXPSYJrTzLDsK8c2wzPafvnTzITTq7jiHtPNEsDjwi9VW7i7BFvTPJnbzFNiy8GpMwvSwEL7tt1lS83tNevFp0wjyNe/I6oSOhPG400jssBom8IU9XvGkYaDwyG1O7Cl9+PI4rnbwYJKc8IMaeOzFee7zBuWE6+KsrPCwpXbwRTdo7VpOoPP16ZDtJ3V88exTpPPFUczygAZI6DVkTvER9eb2Fc4M7HvifvJWOTzuDjZu7nAi3Ofl4CT1u0ce84xOVPCQkTrwLsJS8V6ioPJ7S1bygtSy8rSGKvGdxBr2orDe85zjFvM/DfryVfcq8ryzIOsstIr0260o75ogIvFSAhrxLvP+8q9SbPM+dZTwkogg8rZB8O2z3/btk/J48D7ixvNGkRbz5moi8xbY4vBM18bsrEH48+RKFu/ZuoTsq2TW8pj8dvZ2q7Lz2F+w8kYaqvBnt3rthDi48EB0AvbDSfjy93UK7z/mROPurtDzfy4y8Hq5RuzuSa7sS0EA8F2LxvLCMXzw3Dsc7bODfvNP2wzy3B328ka+ZvJWQUjuI0D68aF6bvOx9Mj3BDPu7TjEtvJ6pozySjRu5MNMAO+2IQTzg7Ny7cubGPFIcozxAEiO8oSr9u+aeIb2ZJVE88QcEvcDOCD3TXsQ7TydlvHpt1Tx66z26P5hmPJM3fTsNXRk8g9opPDNGvzsvS+O4KZjtuwyf2byulAE8fuEbPX6rZry8NW27mXeNOzle0jvihjk8tLx7PANptDwPwCo81BydvHYE1Ly5bes8hT8Au+7GI71L+nQ7236rPC8BRrzU9M+7ZaSUPJvrKLwKXYk8uY+gPJ1U1rz73xW8BOEePf6C07wA9e6861XIPO5VGDy9IzA8fMQNvEOEnTy25pU8BMkZPPzhfDsTWsO89/n6PFJ9fbwv2Oq8D0HFO7lFiL3aBU27mFKMvCLvCb0CoOG8Rd8ZvCCYnjyjdYs7TgnavFPmcrtBlZ48Y1j9PB32gzqqSZ67XRnKvE1esLzVRl26/o2/OyYvyjwNqwI8E6ODvC+wgrzlSA29o1mfPEmYBDwSy727few3vDnbMb36VPk88znRPKADEj1yvva7XywbO5JPszxAO8M5+VqNvEyJWLz0vcS74xMPvcBlC7spE8a8rPPeuyZ2Fr382Xo8J5IpPXUMzjqpekE9n7LivHUyJrzzulO7dFSkvETd9DvaOde8M922PDuLFr16wCS7vWIHO+LW5zwc76g8S6xPvLKNwbyZoUc9E4jhvLu73rwWVIY7DccqOpk2XzzNXAq8iX13OyFzIrwxO4S82TahvLLOCzxi7Aa9dwOJPKZ+rjz02Q+9nkS9Oo5DyzuUNOY7iKD1u4MrjryMuzw89RYMvSVrYLqAvD486t9DPLc/p7x5Yh+9efwDPLISCb3yE8O8OSBkvIH2Lrwzuku9PEVavMXUlbxY3nw8OolfOl+XcbuCVKE8RKBuvBwsWrzGmjU8+QSyvP+ogDzaHIg7P7MHvesJwTt2YWw8fOAfu6INPbxpENQ8k3LmvJ/EW7uDe3i8bVDsu6K4t7wlKn28rp0XvM1Qiby0nzC7eXqru8h5k7t8WqG7xwRWvNfwnTu4Hlg8MtBpvJobDzydruu8Fo46PJkayTzMAQ27A7aOOy3GkjzjMBG9jJASu84N0TxUeVU8K0r/POM5PrzHrdi8fk/RvOUk5rzRrhs8iAozvO5LvbsW+4W9WtQFOxYpQ7xKjVi8D6XrPMdl/Lv7M4k8z+aEO9jgB7swU8E64bktOwTsKTr9CK48RTr3u+BFkTx0kjK9/IaMPMDhkDw0KAg9Ty4/O3EEhjwv0hk9jvAPvevVgbgQV8w712A8ORkEd7yYk7G7NE2QuyRENLmAXZC8NkiKPH3O8rxM9X687Tw0PfSY+budXLQ7InW6u0rQJLoFQ0o8cMeMvLZTeDzWH2S8PpmYPCkNtrydlX+81Z22vI6oEj0Ug+S7+rrSvKFKEzw1OM27ypatPBcyOLut5pW8ZG6JvA+gCr2kNTG7SMEiu2glqrzXtP08EX5COwOu0by8r6+8PXfZvBY9ET0a2LO8HNuwPL8qLz1Mhh29vYnzPMqc8rsRF1Y8Kdnwuyo43jzrjiG9/U0zvVB9NzsEh0w6GwkNu5YFrjz9aMO8x+O+PFkk0rwNwMq7j2PXN4Rewrv8I/y8KjbIPJglAD0kX8q8LSTOuwVdaDw7Ci280YCpONzcFTqNLrU7ewI2PMRgEjxdc9i8j0SavMhrRDzy0Ww8MpK6vMmjYbxfES88lSUmPaof8jtNaHu7wuzoPEzIarzG7FK8ihRgPIahxjoRQpW7tUc2PDQA4LywlAQ8WcKnu+hUK7wX9aS8j10PvV0iI7zbdRm86+oSvcPCfTwDtng7grbdOzQHvLzP7to8Z4eAu+TpJzww64E8exB8vPr/VDz463W70FXfPHTCILxbrii8D+zePGLNlDx1i6S7BS+vOS4AzbsLp7i7uJW+O5M5Kju7z5e8IuBwPBoB0bvLssq80eanvJCXhLwVyrW8bopqPLK2wjxrOgC9rE1tvCMU/ruwebQ7BKsePWBFFjuu5Oq6dQkjvOR92by8EkI81lsxPGoLeTz7YE07TZSAPLWvpbtxYrm85SzRO4tDMLydfL+7dUWjvFI0IbzkIkq9pEUrvJAvebyjsNk8BMbEO0XKkzt3+TC7MEqVO4aHKjyx84U7FsQevBqQAL2hDvY7l0PIO7o6sjwiL/E77g8vvBQLm7vL2w89dPowPWXcXrwmnIg7YHO0O1X6aT2+/nu7eXaEu7mREzu8B3K8wVQovF4c7zx9FJa8kZ/mvDADYTs4ITc8D6cJuynOCjtBQe07e8vYvL4RzTxZphW9MlZXPFTilLxWKIi7+Id2uoQEVzs7Qna8t7SBPBEKMLsb1kI8I3TIug3kGbzJC6q7YaWTO8c0jbwHMHm8ePktPJu/9rvlQhg9b/9yPOOT2bxSwjO7xCVHvJSOGbzgoTA9uZ5yPY7eN7yBzMo7PK3aOy0EDb0JvC86frrPPOl0Cjuty9+8XtaHu9S7EzzRyxY9EdoOPHtVh7y15mE82X5Kuxgh+DzrPVW7H4uAPPHyzbthHLW75yMcvIbxKrsRPWu80uyTuwjEFTy38kk8B3V8vF9yD70zIjE7w6y2PEwp/Tyw84A77+m7PFrLDD1NivQ7ghsHvCynVruyrMY87c6TvJDLvruKMsI8mdLsPNY94jyjpqw77d9VvKbrjLsQRuY7dTEgPBjCWzxVoS49ldi8PCutLzw3/tE8IXuku7iORD0ltgW9debKvPS7Kb0aoze8Lc7pPA0NALysuJA7yVWJPOVwMrwEnRI9icYOPTOfKTyY/fw8ahfCu+ItybwNRoU7XS8zvNRKO7tsKrw87HAVPJ4apTszVWO8zBKqPEn5P7sd1co7CikeO4TzoDwRjSC9ZV6YvHLRwTviHLK8NoX7PLA/vryTcTW8MQ2kPJpWRjpu5Jw7grs5PIYnVby8BvS7yHjYPKU/m7oBFBq9/gOqvAiV+TtSi6C8HvYIPGC96Dynd/g8n+W6O28KyjuCuOE8Gtj3PF10oTzHSXW9/FokvLuRKzs5aOE72z1GPKDUiryibBq8cMt1PBsLZjxItZi7b4vVvD5KADysD+e8Pm38u92KojwG5M+84vm3vHvyI7xw2Rs8mtljvBHSnDpXCMY7sEH5uj1n/TzDK9k7aZqLPIl7mTxEKha8UOCyO5HT6TwzWpw8KsXFOyOEp7vh74M8vaK8PD9i/ry7M2o8dzp9PLqQprxX3SC8eGIDvPyWE70faJu8YhK8vLUUAL1zW4e8SLeyO6t/hzwMXhq6XbpjvPXCXTzNyF09/qMIPC3cy7tZqbk8XPcyvMM4ADwmVDm9odb9u+UCt7wzoEu8sIKovPpyMbt+5pk5sGCVvDjDELyNrxW9Eg+7vHppzrzNRya9URW+vCZitTuCAly99gkQvHyeGTzoAhu8Ble2PE8rzrz2fvs7Mv+NPACNxDyzy2K7YdcbvbMxAL1xYLc7wg1fvW34ADtIInU8bwXQO0a5hjxYB007qjjSO4nYQrwCbya9wBndu7HhwDwqoA69EWMHvRwWdTwHFNG8FP/ju8etIL3gvsQ8PTDHuxv/Cjy5do+7rlehOvJV7jw8T6E8e4tyu0FwcL1Iwoi7LFsjPFkK3zwf5M28OEohvPlUWbtSfkW7QUZXPKJILr3egba778CRPMkNfzsx5n05NXn2ukK9w7xKcjA9nbuIub7Ajjoz3UG8ACXnu4ZMfzvdmk+8n+kUvNkGDjuS+iy90aoRvSrek7wqpRe8x93wuxFcYL2RUaI898/fuz22yDz2Zam8WvkRvKVAwTx29Oo7g+swurzn87sXIIU8VC6zvNTfrrolml882TGqvCC0MryXeKW88AAiPKAthzvR2rM74yh1PJBIE7xKBKa89DqtvBy/k7xHZaG7ZajEPHoIJzvu1Fu8TKqHO2mRqDyJqdY5H9DauwWFIryf36s7H0mHPNfCKL3x3Fo68pQHPfeJDj3d/WO8bAQCvLKsEzzv9aw8RmUbPavCarzT9U88oR0JO2BbrTtx5NY8xj7APPsxmjyykQu8uJZmvFRfE7wJlJU8MwVDuwjb4Drllci6ADcoPIfQsDzBz+U76Fa7PGKJkLz840O9RsQOvUN+Nz3IBoU7NooxPSlfhzom8N47LNW9vOaGdjxNKAI8rXK7vBQmBryj7oa7yy0LPCEDCLxexBg8yfghu85O2LyKoJ+8ACL8O6+KszyO8cs7+5glPGpsgTzS0le8Jd6CvDoXjTzIVbK8BXE5vJ1taTyuAIm8ZkRbvCK+hjo587G8sPHOO/TsILq9EjY84yLGPAggbDzxliA8zk6zPI/eDDue4TC8xKtqPKx65jup5oO8igNZPKmEPD3JfKM8z+0gvKiigzseFl682yxJPWXzkLwf1mg8bYYEvKI2c7yoVTy8vPaiuhrDk7ssRT09ptxvutRtprtw9vW7Z7iZuZ/OWTwi2wC9FSKwvEQbl7yiexo8PcfXvKgwTjximIS77bdBPM2QujtfDsI7f0vSvIjzrrvozQu9WtQCPIDbPzscn188YA5APKShhTuqw3W7sXp0PRi6mbwWSCm8/b29vI0NOLwxByY8J24evWbhHrz2X8U8FwSpPMscEz2JjeY8qG9LuwAI0jy9TSu8oHa9vKpT1DzbR647I8WzO0uIX7xZAvy8MusKvWNZT7yqBh+86YTRvHtF0ryxlwO8GnYLOuULNbwV3v27ncOZvOZKmzzcShy9TCVEvKqa+TvSQe87qCY9vAZFCDx5XU+8nfEMO8mBFDugXtq7LuxbvBuKXzsTqf273LT5vMjC/Lz+NR880r/MO4G/yzxgRoy7/tICuz3f3jzIFS+8LzKSO2l8ejyqca472LJYPFmiijwVF648HGyavAAhlLoh7QI8gOl/vCyU0rw2t5I7ReslvVYmATy2BXW8g4Z1vAenozz7nAk9t5SauxHwjjszbMW8hljWPMhTUrzkNR68p5LaO5YVSr2NSlW8NNyePGLAhzzlc1K8eTx8vL+EwTuSx/q8ea6PvPcqojvEgKw8udN+ujloa7xvkiM7h7lrvOECRLzY5/y7IG4pOllp3zxNAda5EzzpvCEeFjtV++W67WDdvF31lrw7V7w7ZeioO9BJqLs+VBQ9Ri7dPEVCejwTjCq8IU5wvLizJTzVuBe8We6FO2cM6Lw5m6C7FoiRPMKMdbwxK1W8o0HePEciyryAxUy8YsuPPLE5Ab2ApX+8OqlGvIMNDzuhYuy6PM5+upRRFDzHEoy8O1TqO82LF7wFoDk9t53rOsa5absr2g49gA9TPD04wrxvI+e7/YQ9O0d3MryRDrc7R76vvKV/FjxPlhg7NQVCu+0tFzwtvLC8FMrhPOaKvDydsiG8NuawPELQUbyBwqC8tRkWPAlV8ztJ3O07Yp3XPG99GrzS7648iz3oPA9VhTrCw1A8Eo1TvK0gurvz4QK9h/CEvHRxRb0VsyY9Qhg0vShftzvQzsU7OqIVPOgTUbybFZE8z1n1u+3onzvoWmW7wwEAvTT+DL1pBfQ7XtMCvU/KxjxzZtq850BIPDzNCDyWaEQ8bk9WvKwyjzyu6C674syaO5MBID1limE821NGuj+0kbqJF6o8M33svGCh17z3/Ie8iNC7vELAyLrMvm08vq79u5VeeTxgflo8UP8dvYCOBzyjgYy8rEa8PHG1bb3P/K27ZXzmvPF8trw3R388DdtWvBsPO7t20tk5YClxuhWPlz0gCQE8V/XyPPDjCzwxpLG8JUNsO8/rqLz+XpQ84KU1vA66WjxjgT+6uGctvNL75LyFVyY8qBoUPWD6wbw3WoY8DDMdvfEn2rsrm5Y7Rc0Fu/aRXLvdvQU8bBEHO5XF3juMGSC8xeN0uxf/VTzyd348uhoIvDlZkLxUb4y6GPUJuZA4BD2mpa283TJBPbXK/Lzx3QK9kY3Xu4MI3LujU0q8V8ycPARuATyM85m7c+szOqDkyDsFG4m8ywV1PHzjgjtToSS9SBlnvGsZPTxcM6I76YcKO1EZUzuKRMW8WHBfvPo4Fj2WGnW8JC2iPD0JrDxG1PK3Y9+iPJOdhDz6+5g8RIEjvWnMUbysBx+8eLBkPY1vBj2BU9G82cysPH8EuDuV4gg9+ZtBPc4O+rq4WQS9OoRSPB7U1zrBul+8Fk9WvAfO7rzCyhE8C9mDu8roUrwg2Ls8b6D+u3RusrpVbbw82FoBve/gyzyNTOq88qs+vNQ6EL1Out48NegKvVERjLyFZiy8TGBMPVnzTrwSrIy88bpBuzWxxzsYe5u7LU6bu4cNs7myS0k8ICzGvNWVpbxQPJg8a9s5PAotvjsNg1667c5OPBRZTjyJDAk9DK9SvN/k7rw5I807opyDPEqBsbtbmGY84Il4OjuncLzGyNC8Q1GPvGEyB7y7wzO82XvVOx4XmTx183I6MtpTvFYy8jza9rG8V53kO1AODryFIqw8mGLBOyJQyLtQY0I7FQ6vvNtfCL3vka282W+BPKg277xCBEM8NBRUvNebrrkW5K68fpjmvBmsPDwUB7y8cbv9Ov/X/Luapr685KszPHnoMTwxWge9U2x5PKqo/jtHMjG8elgjPUlQkDtnaxE8AIMOPOLIkzzmCba8+RM9PdkcB7zSMTa8OKpxPA72tTzohwA9ZprTOx6INboo+tE7DoaIPIwmarzPihY8MD5jPP1v7jx1GLu8R33QvKeQgTxoDlU9cJEJPC1NsbxT/oI8Hm8NPGyg0zsN3lg8+XTCvIhorDw/E/y7YosvPXFb5bmG0uy7Pg9EPBFGprtf4aa70yigO6c5mDzHy/s79B8vvOLxyDy/WnY8Ut6Au4BVaby4Idi8H9irO/0im7vB2Vw83D1PvDBHfzo3VRA90nsUuliGtLt8cY48qTvGvEA1Yb140ZG89xsIvSbJk7xe6oG80PuVupdWATtDQtU8t4MSvebmzLrrKIW89NOmO6UkxztGXgI7EIMAPZ6XQzy6e3k8FjxDvPXxPzxGIV67CXkKPfQG57w1Msc8r/ESvR2mxjp81LO8f97aPATICTxxc1q8oCMOPVEhsTvQZ5c8rHBZPO353DytRfu7AufnPKqVwTx9H9U8VHuBu+wxgbxez0Y9kGMpPNjTlLs9LZs8wsNJO6R6orttrIo8E7huvQQkxbsU1yu9Qbc5vJEDTD0RRNi8H7zMvP56rjt2ABq8qlBBPb0H5DmfBpu8t72Pu70kALxBEyi70SynvIPNzDsjhYu8VQxWPMyMGL2L70U948bYvLITqzsWu0u5qLbIuGy8SDxcf4E8ti1dO4VAg7xQS4W7/gaZvNGEH7zr75M8k8mGPLjmVjz95n+9FUsmPOlo17sMzxi9DzTaPFydfTwSA0E8za8Dveim5zsAPrS6LFX5vBYGAr0dKZw8tW6KvBoO3zyPdte41V3cvAzvdTyaTBg9W3W0ux5LJLrncTE8l+OXPKgTxbvdHrs8EwELvEm7Lr0eSgS75Ss8O0cGKzsKRYE8m2GOvLqQCr0lr508IUE+PL9iHTyw5bi8c3x8O36bobyUjdm8BqSEvCZNwjtg3iE8QB7JPCUGaDw6A6Q77zCCPOeLozk7FKQ8Wc0Hu67xqrzq0Ly8ICW8PA== index: 4 object: embedding - - embedding: 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 index: 5 object: embedding - - embedding: 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 - 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: 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 - 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 + prompt_tokens: 842 + total_tokens: 842 status: code: 200 message: OK diff --git a/tests/cassettes/test_revision_round_trip/test_fs_head_short_circuit_skips_fetch_for_unchanged_revision.yaml b/tests/cassettes/test_revision_round_trip/test_fs_head_short_circuit_skips_fetch_for_unchanged_revision.yaml new file mode 100644 index 00000000..7257a034 --- /dev/null +++ b/tests/cassettes/test_revision_round_trip/test_fs_head_short_circuit_skips_fetch_for_unchanged_revision.yaml @@ -0,0 +1,42 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '75' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - hello + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 2 + total_tokens: 2 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_revision_round_trip/test_fs_ingest_writes_source_revision_to_metadata.yaml b/tests/cassettes/test_revision_round_trip/test_fs_ingest_writes_source_revision_to_metadata.yaml new file mode 100644 index 00000000..7257a034 --- /dev/null +++ b/tests/cassettes/test_revision_round_trip/test_fs_ingest_writes_source_revision_to_metadata.yaml @@ -0,0 +1,42 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '75' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - hello + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: v08kOaeSRbwipvg8oenkPHcEOjqilBU9yiq7PdQgH7stg908sZ/1O8W5yjzWmrm8QfXEuHKhgLwgFMk80L46PAvNEj2J0xy9cVsOPIO49LuQmSi8oB1VPDeLOz3BJUW9GXnmPMu167p6Bdm8LS7xvepd6TytUao7woWpvIC2EL3ejgw9OvTLO7SACDtl9DO87VqsOuheQ7ybPfG7WnnZvN6r9jxyTDW9H5vaO+wgNbsbH3U8HWW1O0gXgjpI7vU88GfAvH5AWLzuo4U7yeKlOzxXBj0b38S81gS5vIEZJbs6Oss8Qz8NORHWNLvy7ws8Qfm0u1df+jz48+y8I10ovEx5brvLZem8F3nYO7kHvr3mRXs8M8x9PGXfU7w5Z2U8ewAtvPkyOjwy58E8UshrvGwGu7vrqYs8817Nulzj6jyHIKm7sE3ovHvcbDzA7oQ8neYPPfJyorzida48ZyyuOgqJEDzccJG75i6FPO7OJbzeQnE7mcc4PSPakjpq+a46HEqKu51mvbxE5ja5oa8sPDOPUrwoiBi8Kzsxuzbzsbx6ob67e+a1vHOI3byiUow7PgpbPERAmTtmM0m8p4S1vBptErycARW9k+4IvUzlnbxIFFO8RN8aPVUZyTwtThu8d50HPFAMCz30ca87lvqvPFewbzusXkK8qQabvB0BCjuD0Du7lDYZPMmRlTzVhqe8ToirvOi1irxhF8Q8HdZXu+ea+ru7zZ07BZL1Ow6AZDwbqPC7+t4iO7ECgTuASoU8tB5MvKECq7zUsWe8+rCGPBL/1zwpQP+5i8YsPMQXn7s9hlE8DRecPJBpejzkk308m4jRu7426DzefZ67KyuPOxcna7sEk6Y8H7GIu2bjRT0cwKk7JDprvPZeqTxCpD28gK8WvKA4cbxRMjG8kDq+vCENdDwGTa27tWI8vNwdD7ydm4S8KL3iO2O7mLxIo9C8uRW6OmHi9bxFVe67l5g0vJlbmjzkmnc8IY1UO0eimLxXTUo7gAaLO1SVIL3LzYE7VBIivJ3AQrvBvL48AhlDux4TnbzE/u06a10ZPZrdgTzc0Zk6A8g4u1RCDjw+rni8ciySvIfWiLmxEpi8VlcevMeRTryvUaO7t4B3PHaETLoY4RS8KeYUvewKDzrczXk8jbP+vIs5dbxQLPY8LI/LPB7GzzzJnSK7ujXgu2Pd7TtBqkW9n6JiPKYvTbwlXto7ggoKPE0XG7xzD5C8Igq5PHRy5Lt7pyQ7FUbBPE36yjwp8U68/tHfvFQCAbxif9U7cCMuvOAVzrxZwR08rsEDPCwosTt6BM688mKLOqK+oryE3QK9H3OcvJKjiLzK5Ji7Ez+ZPNDtV7zOP4k8JaFeOhlRTbwuWBY8gP1MPHlyBzw1zqI7+5KMPMK+D70E4fE7d8uSPCm5NDv6abs8CZFTO9ruxzsrSJQ82zSBPLGU0ToAvg677LAnvPS+wrt9vru84UMPuyrhkLwoBJy7mD45PV0K67vkvKS6vbwPvRuombtkQMA81laUPGdBWrufKII8D/FDO8/iBTyZBSk7KvnJvHshCDxetOq7rQuEvNtNWDqYBEA8mGh7vEOI97pPwb28hWNkvLo0IjsrL5a7spPOO4JuI7uNFaI8Csg9vI8oOTuBYCK91fgWvV5SIbyMkCC9ulLEObDoUrvwRIy8AAwDvafn27suD6W7IxtavUmGCr3DX/07ibFLvWs22byAf927dxnWvJ+tmTwKFIQ81wMHvKj5Ozz7gvY7L3ILO//iPTyYVg26SVwHvCsWX7uhWoa8hMaLO2lXKz3l6wk52DcnuYv25bvjDBk8FZEHPSv0c7u/HSi9OcTXO2yF4bwlh4+8zwrXvJdKqbz6pjE8QJGwvJozmzxKO1K7RCYCvYr1qrsT/MW7jHmgum8I/DwXHlC8cXSkvITvlzx8jlY8bDKWPAdeubtqdlk7VUL9u6+2Ij2MxPu8VEqqvIMm1Lvatyq8z95kO5QqnjsfGCC8VEi0up7qkbyiY7O87JqjPKA9k7nwjt08JJI6PC+i2Lsg/yg93XgRvCJlhDyUIjA7bVEYuUup+rtHbeA5Yj87vPUJbjwuDm+712U0O34G6byt5vI8/osIu8FOBLrJPkc8DL4bu2lw5LsP7IG8KnkMva6Xpbz09SW89ukVvIREFbxgoVs9KNX9uqLwVbzuXwg9wgT8PKxgkDyXqz68JcupPMj3VDyu5r+64/MzvVbPcrzITpK8N1/pPKHDoLyvO5m8E6cpvMMstbstvws84snTvCNDtzxPlQQ7yILhO18HYbtzyDk88jjyu/yMGz2DecK5wSgyPKYBrDuJTpe7wmx5uxokVzsTe5k8DbiIu1EXBrwP00c8uJzDvMw3aLznppa62v0uu2LIzTy+dAc7F1/3u2Vx17m4z8s6Wufeu7Id67tFkRW8y8v7O8McMr2IJNm8TJVEvN1rKb0lMAa6998pO4RNnThr2028xBURvWdXjbu8Q6C7XzjBvJblkDuQqCC9R10YvIjWB72gYQo9w0eku7TFGz1JI948Hea2ul6iMLkX9u87xB3xPCVNMLyr7i87EqpPu2H7LrwTFpo8rD1CO16ZkDwuF567Ze5nO0UjWzx6BwA7gAdMux+Ybjy75Qw8HYVpvC4LrDouxn+9v7EyuoNyErwUYMK8vx73PHe7Ajx3mKC74QFivUPjnDsPW/g7gAlHvPkZ2Dz1+ak8lUfVOVXFlTsNFiS893EHvWA8MTtEUTI8N/JZvAa8dzwWMGU9UUqMvNDeH70VqZ28F7srPIXrZbwGUKy8ybWPPBP8WbzEh928g9ibvC5lGjyQfjm6x8zWO6vGgrvyB7E88loyvaZ0Vbzi+vQ7lPXkPMzYNr0qQWc7ulDTuwIdqDokwPM81g4xvJqUgzwa7YW7Xk/YO7Zkdzz0Hoi8vGaZPPL3tDt0cIo7qHA5PaNV+byEnSI7wKZKPWt9tTxGsDM9hNM4vMB/jLsrX1q8IgebvME24LzFtJ487zLEO5DXhryM0r+86BSJPCIrV7x6KgC8IAT4u08kmDx3kGA8lq2tPDF347vhhSC8Lkqxu3t2prwayX291myzO+ywjTyDMZI8yU/9uxK2Bb3NqQ88xwE3vM0XcTyNf0e6RmvTPAF7gTxKdp28tQgiPGRrszyNzGS8PnQ0PFXoarz/JQ682sTZvJFbZ7xUqK47zvIgPaE4DbzI2Ew818MOOxiMcrujx5U70p/8OtSxpzwZXsW7PgC9Ow+azTubSbi8bsWgvNBLpTzDmKe7HfgyvXhvwLvBaxK8rhUQu0mLWDkvu128wByWvD5mqrzh7JS7YJs3Ol8nTL2N0ua8yhwfvP/4E7y1yxi92MVOPBFohryuJ/88teusvDa7FTssi+O7HEXevHEYrDyT8/m7yP/VvJsukDz0FZo7ocasPFwhgDwyO1U9MVbcvBDjUb1QjQO8SgZRu/IPcDxj2CM9QTncPAtaNTwNtnq7AekCvIgHaTuPD8w8LpPDu26HBz0/eR68WEyUu7iNvbtVVpG7Va2xvOVa0Lw2muq8oZc3vXCYlroXBE09vkGYPAumVTyexwo98KSiOvUSWTxf+FY8vofcvI76mjzu+X08E9MMOznflLzR2nS8u7I2PBLn9TwteOe89C27OtYkmju7kFy82rIZPKhV4jtckA08JwlBPHv1ubooXg09w5C+PPjeszzio2a7InwEu6pJAT3Xnwy9ZNGFvB7JIbyK8vU7ifAyPPKVmbs0Xw+8mm3CPAA9DDwvE1g8uVZ3O+Yi3zxvHIS7GmQbPA+Xprxr/dW8dH9LvIFRuTp4yHi8m4s6PCmdu7z6xEg5HJ6nPAiOZDzVZ7G8r7kPvOszjruRAOg8hRIdPBRqKDzsIpQ8F9U0PVVEjzy2ABu9lh7AvKdK/DwwG5A7in7EPFnarrsPHru7GduhvHO7lbw1iBE8NoAAPT1AITxqrMk8NMwdPGbKZLyy14q8M4lHutA28jzuqku8CnIBOxT6RDx+G6e7HJrZvP4WrbukQou8oN2Pu+JKsTxYfOw8ccugPP5fnLwhMCO9LRs0PARASr38EAq8oHq6O22PaDxZkpq6SIUUvSzXHDtmLou78eCbPGftT7zy7/Y7BzqRPLP3eDzUuKG8I4n5vI6QqTxeope8SQjfPADgOb1CXxA9GJpyuzRz9LyYl9q7OS/LvG+qCb3iHYO81WGIvCJyDTw1yQ48F8VXuykk3Lo5vIE8UiYOOnGaPrzyjrI8qwsIvXC1s7sfQJ67Hhc+vAyGDrxC5uk8fDi+u19M4DwnlOg5jgKBvNaanjxtOQI8mI2QPHsWGb2+0FS8M0jAuxqMHryt1oq8Sdt6vE5KUbwG4dI7U8gFPfv5KjyRRGk8lUynuzC1BLulawg9Q2PftJRIqzwn6FE8PfRsvIJc2bpm0Jk87JroOQBzJjxMHv888uYEu1t9qbzSnps8CIVAvSEP/LztAWm8R52SPAwFOzzZins7ZJTGOohWwDs480q88FstPAmADTy9ssi8eJAqPduNWzx4dpQ9oHPcusqmiTwTtL0864zQPI38Xz1UxTE8NgyQuzuFS7yqCB06rtMVvRxL/ryTR228Jvxcuw3AyLzicB87z/ssuoUYNDr3zC691MWSPLb7iTwxsno8kxMOPRKsMzy7PWO8EP68PGcwz7ziZeI7GU78PBl/Tjubnbq8NumHPUVK8zyGrxu8fbqAvNFXFbwP75Q8HXWBO5U0Q7z9pjI5E0LRPNiItDzNReo8XbTLvAXo47wc0SG8ekLEu/J56DzoEBa8pXlxOxEsnDzwqL28EFqXPGiiMjw46Gc89DZZvBYVAb2J3VI8nki9vEYfPLz1DLa8EU70u1kJ3DxjNgS95pZLvEz8lrwfbcU88mYAvQYcZLzH97A8x+msvCUxI71V3GO84yMOPQTcCLzzheI7bMfyu9yuLb3v2F47afsUvQNfyTw6bbO8WqxrvLQ6Fzwadqy8pZizuuWm0Dv3ni+7yL1XPGM4+LqA3567wEoZvD9Jdj1VZ4w8GzhUvIUhrjyITaQ7IdhNO2lHEjzV4R68erSLPMngAr30qos8hcG1u0Nz+rwcrqk6/t/Kuy8PFT1Btri7H8lMvITQSLzXzh28nT5JvInNozyGVKQ7bwQtvPCJn7v/ffO7ka4jO7+Yl7zcCKG6YwxYvCE0ETznlh87Pxe0PDtOETwzyxk9KGJwPQ6XED0HogS8tkNHOjUXITwnOxa8hErbOIecMLvXXnA8kCSmvCV3PzvxMsW8aV3Ku0tnG7wG20G8W4VOPP3Tgjpwo+c8LlPeuqfNEDtaYbs7WZAAPZ3NCLwXkkM8V0IAOgZCk7xTCQU8nyEbu/LRqzx/9z+69lw6PNWFWLyk2SI8mRTgO/KwI7z0jtW8QH3lvCFZkTyYzfs7pIlMvDcxM7zoUVc8t4mTO8lgPrxgMTI9NAujPAMmxDzQ8Qg8mio7vJPU6LunAtm7UzUMPcYjrTyps4s7fDvSPJJ1frzfTas8zgEkPKK1c7xAYg28zv0PPdCuk7xL3n28H2QWvOm2vjruLco57YDjPJemGztVRUs8bBwcO0QpILxJL4S8K82ru9p8Oj19vqu8gDStPIINqry70pS7a3QqPHRFVjzngp67qmw9PI67xjvuuVa8G1jcO59RoztNe6k8Wl8zvVvNhDwp5km6B1zCu70yjTxcbUK7PK0xOlGNPrsnYyQ8p2hjOy4cWrs+tI+8+JNUuyQEAbsq5BK9bD0+vT/SSzzDUT68MjO/vP1agjzZvqC83ng3PXnBJTw6vx87ZuzMO2xveDywbY281VOeuwxhMryrdS09wW8tvKKsEruJm5c7TtdqPDba37yPDSq8hq6Ouy/3/7vO41G5lVb2vFu7zTt8SyO8O4pLPA8D0Ty9LY482V2cvG/01bkLRTO8RCwLPXvLbDx57ru8nk/Ju1tuz7uHI9y7IgddOwnjVzvep508lnv7u6e6yjsJty+8ZJ4gPf2EGD0E0/47FYAAvdG9NjsxFQE8znPNPDXI2DuVtx6897QbPW9pFDwokeO8wratvKuClDwBcXM8fWcKvUQUqjr0oQe8h8MBvBJRojuxLrI876qOvJMGjjzEeK88gPYGOzw9k7x0CDI8gWUHvJQtID2CfPK5xPbLvEvVjTsbCRM9e/RxO3uayTtmAEW6G0gRPAS9CLyDTjE8SuOwvL9t6TzwOx69uWlCPHwHlztRQgW9AEZfPAb6s7w4gC88ozTXPEwMHbzrIPc78F0NPQeb2zobliU7BJBKvUNF5DyD5Fu7HO0iPcQTHDxypo68turkPJCSrTw6FzK8UqwaPLETVLsLVkE9THL+vE5oj7rOOZq8MbVRur75qLpOO1G8N44TPN5R3LxBfcm8dWuPvNZ0K70S44C7zDv3vJvx87xXmHM8FhxwvHk1qjvUYBE8WoAxPS9gUbw7fqO8rN1GvDIHIj122j+82fGNu3vOCz26pnG5MmwaPRUYJ7xScmq8axebu+2xn7tuSpu8U0etvA6Wp7x4Bb48KOR7PJ1mCry2LSu98gHuulgy87sNV448t0hePcfTiry40gC8Ed28vO6FaDz5MMk8BE8lO/ZAZDyJLNg8R4MPvEljBDzfgB09ddp6PFtx0LyILDk8Crm6uxDqCjtOnvq7X60xve5xPDw7pzu8YuDFO2VvADyXXrQ75UAuO0zdsTzxjpg8hdzIO3LLPLuTNls6aWfXvLWNALwU6Di654+CvHEAJTtJWu07nYSYPGe5RzyqOjE9w+JyO6rn8Tx28YS6tlU8vAPE6ruSwuq8b96EO8yMH73uW268IQAIPcVxdTyaoHU7q41zOo5zhrxdkr27JP8UPX6krLzVhRK9feECPNcZyjtv7My8z5Slu9LtZDwjC0C8EOwVPcviJb2Hn1u8gMg4vOS4orxRw0o8HPC0O8fSZbs+nlg7QwvRvBnqqzz8U6I7BDcxvcGZkjwYhqQ88wRzPDOEVzyL4ra7kGqNO2zsILsqbgu9oCMQvXDngzsUBKQ7iljEvLXZBL3pxwQ8YYy2PDmPWDuBrra77eYqOr0rN72VPRe6L7wZPKUYCb2iHAu7orGDvD6m97sIrm87vHP4vCuhgzziTgY73qm5O/Gz0LwzEpm8XU0wvDkOz7rtCt272uSvPPlcWLvMz/u8ezeaPCh2NDz8aAO9iz1OPOjmgzqH/AI9O5S/PDy1hLwjFaw8/36FPDf8qryah6y8rw4fPQQPqLy8E6y8kmx6O3hJxjwUV9W80q2CO2kYZDwsxNI8usP8vK3pTjxnk288o0MTPQ8khbxffCM7MD6BPLU8wDsx+bq7dOnnPHC27byY7L68kKAEO6k7DbzrpqE8TOctPFK6NT2XjCU86oYMvMBNIT1VhvM6COv1O2nIwDvamn28pEqUvLcGOD0zs4s8x6sPvCs9xTzQ2+m8wi5QvO96gzwYmpQ8CGUUPDh4Hr3ktW08vOFpunhoZTpPvsc8mfvnPJR3NL0ecpS8aumLO4t4OT3FW0o8T1MLvQz/J72frkq88ulOPEwuwLui3J68E4i1PL7tdjwmD467UWFVPJgWILyt3yA8KloWvKMO9zyxh9k8RwFCPJSdk7z39Bw8gQFOvH4VybzykLc7abR0PKjC8Tvl3k47mwpyPF6nHD16oVm8YovfvFIolry/moc8e8nLvC/WfrrpxYA7d0oRPYBLKL1pRns86udFu2+PC7z6XRa8TCezvGboBzyzdKo8RUsgPJQsKb3oTGi8T1TDO0UsADxgg6M7LBntPD1N0bwSkIg665gOPbpNMr0/Sbm8zc5Fu8Y36Lxikq68Pe4wvaLlNTt8hQk8e6NTvFq6izxjQxo9vvtLvKIEdb2tOig8bkI1vOOZ+zpFbA88HC6BO9x0szzDGv27XfjCuntPU7yzJA69CbytPHpW1jwzRDU8qeHMu73Tozrdxci7Zx5BPZwDcDwJNuO85aIMvSl0qLxyE568fqYHPZ3iLjv53ZE8ZNGAvMRRz7wb/+C8R7nLPFHxl7wmuag8OPjNOhYIVbwFdI27DTSFPA9dYzwolCA8XD2fPCPjRDw6vR07W1CTuramGD1GQJY8pWWru0NVl7z9tPc7afKxO57jX7tfH6O7RYEJPbZTqzzi9go83DdQPK9uXrsQYV06QIOMOkQUmzvwkbk8e+vWvHrAXbyolGc8cGY3PM+CtDvJW2k7o9lTvFu09TsBhU68ZiXPvMDazjyDJi88pH+TPPS4wTrMaUC9UWAPuwvkyjxDg4K74U4UvRV9TD0WRl88s6lrOzBDlzxU8U489y7Qu2qHbz3cbge9twnhO7x0GrzV+oS8GfDNvOOuQTyX9mS8prNtOx8tRL3Ky/c7zKsrPGYkBT33U7G8HMEoPPcnAjrgN947lKrnvHANRDwJSlY8BVhQvAzVnriMopC7PCM6O0QzLLwcSt88rEeCu78tcLybfWu7VUYjvIfRdDwe5588t0H5O/1d27zbraO85T/KulimFTzjO0w8JRcKPJf9ITqkn6i8ic2huRoLkTz8BHK9RsKjPCCj0zzhACC9kLC/vHcgvDz6kuQ8IR2BO1sDCTxM4Gm7jeD7OuYlZjzuBDS8MuFiPDSB0jtHSt48gtSbPIrN0rsHPyK9HDE8vXBHrLqHKiM9HRS2PIjm0Tz/3YS8cQzHvExcHT0zMNy74AQWu53YqLt7KsW8JalXu9Jyb7wkmdE8kqwovPoRA72JWM860PQ2vIYWSTyTGJc6M+v7uiICtjvjo5A8bi25O2iylDwpZW08SxLdPFT2YDxQFjK6V9X2O7X/qrtBZBS8EUK9uJFoQTwl/CC8NoORPKSiF72mJZ+8Bv5MPdoxRzzcgja9v2u0vI3nBryoCni8zmlnvLqF8DqwSXG8gL8gvbblLT0Jsgc8zNeSu6HByLxoG5S8/zUIPW0CBb3ro8+8mL/SO1vAtLxazIW87E3dOtI9V7wktqG8mvN7O+ctMruN3OY680rLuy+twTtGnv4772L3u7EDJj3YQhu86mseuyXeejxxqO+8lgqUO1OdkbwKMZW8PUsaPB9Ks7otHdy8ra33Oy9GxboAyDW8xWcQPOeWTLzFgeo7d8mbOxr4ezzS+ze8bhitvJw7XjzGZq87YV/GPIZPQjzvmFI8GLB3PNgAGbxdJRQ8eMEMvdiFizv6A0g84ykaPKuUfjydtOA8MzyEPG0WMD2IGqI8fY2hvGKurTzUZkC9q9cLvCjQNb3pqD28Lag1PIprTLuyuP854J0ovBtZFT2CP/K7rCy4vDJtbTzwJgK8my63ua4WSTyKGYA87dWFPHYkJTwJA5O7XTmZvDOPwLzB5iu9VKAhPdERCzo9Tw28PmFcvNXt0DxiOqS7F2rYvMFOMb385OO8I8MLPCvI5Ls3zRa8IDOQPNeyQbw+g+M7eG9kux7gHj37+Jg7DbVWPFsRfzwEDSW91DZxO6dcBzzXiVm9P/2AvLljzLvOBy+8lQmjvJX1VTxEcmk8UQv/PNCY77zUmwc8MtL7PG4XGLwqYu28jtD6PE5qsjvz5eW6FzAtPDuMDD0PMAm94Ec9PIAebruJIZI8/J2GvLG4gbxVYQg8C2NTPBwDDD2sdA09N0A1PK+xe7vrb6y7nDwIu4mIHj1gnDE879DAvN9I2ruvTZW8Yq1/u2ugIrxdI3084GYovA6EPTyqpye8P6+9vJ3ZOLzzNwk8aP6qvK8rOLyTzJm7HUdUPNr+zLt16s48dvSVvK9trTxdvm68UvYNvemHOTxu9pC87BUePB0s/Lsc1f87ETWEOzU+hbzLSsC7EHu9vM2UHDvp6Ca7ofU1O15iu7zTLY083FcFvM/5sDtkCLW7bGLKvNE5q7yV3wg8WljHuz+W3DlmJiC8QO0XPLfZ/zwDo3w6Ghx9vM47krzjVtu7WG42OzgO0TuZOxs81iYpuxtwRDwA7Cy9Din1u9v2arzRkc28yTFRuqXe5LqD2sK8KPCyPCq4pDwA0kg8j/PBujbUeTxEhMa7q1vMPJ4BJbvQKU49uMXHPMeguDtvjpE8jTbhPDK5kDw/LEy9bPyTvDfFfDw1RRE8roeaPB4cPjzl8os8POG7u971dDypk6w69cdjua12v7xHHQW9jF/svPXQ4zrkYDc8LT9RPF5JZ7z6IuK85ugHvSDNQbzoRUo75x0xPP3tqjz9CDS8khMOvLm2GD086Aa8rD1MPGFDAD0C5cq8R+ykvG3tXju5tdE89ZF2PJ9IIT0Lh2E88jmoO2qGUTxu76c4rlrpPEY3UrytYmu8+aHAPOOg/jqsL8g6AZvKOxdwuTyPAIA8BWS6O/9s/TsOWjc6CEXHu6YNxLw09UA8q0sJvMZLWry/liA6XBLzO95wqTxMHiO8U5MLutcLKb0DDQa8g1Suu/KZ9byRo+c8jnxcvJ/ijrwmxCo85llfPNjNsjznCDU7hrTgvLvD4bx1Wry6+nEhPBDYPjzOX7u8MhDlPFTPlrx9koY8lt+YPLR3prvH9yq8yVFFPP8GvjyZaYu6poicvBzfL7qZctW8fg5PPKY2ELoJdDQ8Ipw2PHaKFDw2rWM7eNspPAPlHr2q3G+8Uu6ePN59kLt507s53YiMvCyKpjzkiy48a5QEPIEnCbxMSpQ8zRGOPKHjCDy9gbs8VZG+PLdZXTzDuR+80yAMvdfcBb3NB6m7SuQXOk2yE71vMwK91tELvIrRcL1bCTc8SmQOu5iawryRzIe8WtWwPDYew7qYw807cjWKvDOuPTw6Lzi8tPSsvOBY97v3+LE86+39u372gTy6+Bi8N6mYvN200ru4UDu8ILDYPJvhFLwewig8hVaMvBaICL1d0su8NldZvDzhDT1h8Wu8rP6UO0+js7pdtdc8A76UPKocsrv8Csm8UvifPBJznzkWy9e6da7SPGEu0rz/ylE8heQwubGFtjuLttC8TtZzvL33LzwPiR283jQhvMvkYDxw2D28SXW2ut0ROLo16N67L4Zpu276Xrwfdek7fbFDPJOfybtBAwQ7MkNKvcFCZbvjkOa8QBQiPOFsg7z+AAk9BI2+un122byPa1S7vKMEvONYXzxnN/q7QF8wvLAsEbtPhX+8C+RYvRDCYTyneSK9ueFau1jj37yQvcC8RPukPIJ1ITpsqiI9ZBs2PAKmgzwQ83W7H7Pgu3d4k7xzGn46Ibl2PF5EWjxM9XC57sMyPNEj5jzoPmw8jOPYPMPhPrxNmlY8aZ3ku4VMWrwz65k83eVOvJTHDj0NJRw8yFC4uqomSjsDW6O79NOKPAm7ozx5rUK88GcRvDC7Q7xzrNO7RfHSuzFKjLsy71C8rcYbPNMwJL3uEXe9GkNDvM+8qrqGnO25LJGSvBLZMLyFWXu7RzBWPEUUwTtwd9G7Zg1+usIpybsxizg90BGYvNFka7wvUjA8oxNuPAPYojz8Fpk89bc/PFS1Fz37tw+8b5XGPMN3yLx5u1+8csWsO/djML1R9s48tMyNujQVD702yYa8FdjbOre5+Ls0weE7e8ssvRAntbtEOEy8Ed+Qux6roLycpbm8llaPvO96J71dZvO79hxCu0hDfbpZIQW6HZvOuw+qvLsjWuY8VRBrvJiwlDx86rC7lJxAPCc1x7vuGcs8v2Z6u7znjry0KEK8B5v6ux8fMj2t4908h2eQu8VZ97zdpR49Zb0yPHmbrDwqy9M7LfKUPBi5Er2DiCC9s8yNPOCdNb1J8LA8JBepOwfq0DtlQEW9JiDcvKkkxDw4uDa9CrCBvJa3DbwhUei7x27tOsK49LzeeVg9wjocvXMUpTygPS283qZXO+VUgLxmHgo8zwjsPOgtlbg7PrA86K6lu78rMLogaAO9PMBMPIXowzxyRv08WBOXPEEZ/Dv2dy07mxoFPMYc4LqoV8o8nsgwPWK80jsZy7S67S+WuuO+B7yCRYW8sw2fPPU+m7u+Lha8HcGlPO1MBz0quu67KL+CPBXrMDtQtqS76Ft9vORVkDv87JI7caM9uuKHCjyuesA8VKb3vGG93zvVSQg9rWH4vHPBD70tV7w8q6ZMPDeSbDzRfCg8ykuaPGboKDz3nQG9GgTRO0+tgrwdEi48Jmc0u0uLlLxRMYk8FxnfPCNj+juPrga99zWdPHncFTz1qXy81rWIu1SKxjuSbtU6KOTRvHPKbLzkR0O9gCIOvCeBdzx+x688FIk2PXjCtbuUPJs8osarPHH4LD3/JGy8z/pLvYvJDTsWTMw7aXHCPMN4Qjwx8k08apP2PLN62bt+Kai7BgO0O1ZrET0vgpe8XovVvOMRhLx75ls6QkLcO03F/DvQc9i8o+p7vNdFl7waY0c8r1CyOhOlFz3HM9k8RlInveLvyru5zzC7GL3FPNIqmDyEYwi9B2hIu5nfIrvR91W8IbAFvchrljsTkRy8TZmGu1OFzjoBttM7wM/qvCWssDwZLVI817owvD/2HTtQFYq7Umj+vMH02Tv+l8u8SD42OwVvBTyr37q8mzJIvFt1vLtQBsy8KEMOPAhXwTrfG5u8jNADPX3bHrwMxu85je2dvITh1jq0O+a82Th7ucjIWLxLeAi8q79zvPSCn7l/CYs8TudTOV6Da7yMiFW8PNoyPbmox7wG1FW8nslxuw8izrsO9ee7SX+HOg/UibpCC/a7ZL8+PPbntbysEq07n0KRuj+01jsf/wI9M9CRPJc5ozsoCim8XnWvvNNzBLsa5bi8Ji3BvKRTWjvMaU06iyXiu64FC7wMPEE9IUXFPEPpF7xwCp07hJNcvF2yOryeWv07Xorvulkpujwbr4M8TgJtu/NPEr030DU40DrzvFNVWbwiPSG7KFKOuyY9ET2zVSi8y34ivFDmUTwXgCG6Bmpiu8qe+TwF6bg8ug9rvACFnTrUira8s8tVPGY7JTvLUJc8KO6qvH/bx7ylW1+8n6BtOd9fTDx0V3a825QIu9wC4zsFvxU9bcnsvATfhzsJWTs8aayPvHWvk7xpt9I7SUuQPC+1EbzxcBi96+q3O9EAIztNe6G8WyUFPB1lFzwtME08ciLMPK0Odjx/m988rLpMPDVYqjuijwe6OA4Vu/1Owrz2FIy8pjFcuxE5qDyKLp87B3LTuoa7Eryc1Ec7FehCO5IlojzJHBW50ac6PFf8nDwVgOG7LamDu7PdPjxHqRG68wfBPI9DI7x4HSq8f6vqvFYfkDyko1q8SsY6PIUGYzxfRE67rrMZPRLurzvG7yw8GuhFuqy8wbvhrqc8I3TnvDYOHDsJZ3I6EVjZPAn3zDsSpo682xiTPLYs1DvUoVy7w3VCPKlDFLwWfOg7c57PPBVTZbxSm648qVLIvNjSgDzNqFK8HhufvAyQq7yC+PK7Nt1bu78a2Dvw+ha6v+2qPOQ037tgDvk8xWfFu5WSFzrsP8a7uNpkvOOrjrzJa8E7CX7QPFGG47swiMk8zh3YOZ35lbo76mw8x2ZEvOZtCzz8JKM5P5PBOwXJlry4F4y8JOK8PL9tpDj+Fnq7mF7uPBW7fjn0LS68BUf2uzfw87x/QLs8uQGCPDcmLbw6nUc88FPtu+lGSrskYA+8iWZLO6HR9Lv5RMS5hGlNvL8Fb7xYR7i8PAHYvA3lZjseq2s8QMDZu255Szt7UpQ8ApGJug== + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 2 + total_tokens: 2 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_revision_round_trip/test_fs_second_sweep_emits_unchanged_after_ingest.yaml b/tests/cassettes/test_revision_round_trip/test_fs_second_sweep_emits_unchanged_after_ingest.yaml new file mode 100644 index 00000000..7257a034 --- /dev/null +++ b/tests/cassettes/test_revision_round_trip/test_fs_second_sweep_emits_unchanged_after_ingest.yaml @@ -0,0 +1,42 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '75' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - hello + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 2 + total_tokens: 2 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_revision_round_trip/test_fs_second_sweep_emits_upsert_when_file_changes.yaml b/tests/cassettes/test_revision_round_trip/test_fs_second_sweep_emits_upsert_when_file_changes.yaml new file mode 100644 index 00000000..7257a034 --- /dev/null +++ b/tests/cassettes/test_revision_round_trip/test_fs_second_sweep_emits_upsert_when_file_changes.yaml @@ -0,0 +1,42 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '75' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - hello + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 2 + total_tokens: 2 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/ingester/test_pollers.py b/tests/ingester/test_pollers.py index 5f6f8210..11f78569 100644 --- a/tests/ingester/test_pollers.py +++ b/tests/ingester/test_pollers.py @@ -265,7 +265,11 @@ async def test_storage_options_thread_through_to_job_extra(jobs, sync): poller = _periodic(source, cfg, jobs, sync) await poller._sweep_once() queued = await jobs.list_jobs(source_id="bucket") - assert queued[0].extra == {"storage_options": {"endpoint": "http://seaweed:8333"}} + # _otel is also threaded into extra so the worker's `ingester.job` span + # can nest under the sweep that enqueued it; assert the source-specific + # keys we care about and ignore the trace context payload. + assert queued[0].extra is not None + assert queued[0].extra["storage_options"] == {"endpoint": "http://seaweed:8333"} @pytest.mark.asyncio @@ -282,7 +286,8 @@ async def test_http_headers_thread_through_to_job_extra(jobs, sync): poller = _periodic(source, cfg, jobs, sync) await poller._sweep_once() queued = await jobs.list_jobs(source_id="auth") - assert queued[0].extra == {"headers": {"Authorization": "Bearer abc"}} + assert queued[0].extra is not None + assert queued[0].extra["headers"] == {"Authorization": "Bearer abc"} # --- PollerManager lifecycle --- diff --git a/tests/ingester/test_revision_round_trip.py b/tests/ingester/test_revision_round_trip.py index c546cc55..af3b745f 100644 --- a/tests/ingester/test_revision_round_trip.py +++ b/tests/ingester/test_revision_round_trip.py @@ -4,6 +4,8 @@ as unchanged. Catches the FS-specific bug where revision was lost in the pipeline and every periodic sweep re-enqueued every file forever. """ +from pathlib import Path + import pytest from haiku.rag.client import HaikuRAG @@ -11,7 +13,13 @@ from haiku.rag.ingester.sources.base import SourceEventKind from haiku.rag.ingester.sources.fs import FSSource +@pytest.fixture(scope="module") +def vcr_cassette_dir(): + return str(Path(__file__).parent.parent / "cassettes" / "test_revision_round_trip") + + @pytest.mark.asyncio +@pytest.mark.vcr() async def test_fs_ingest_writes_source_revision_to_metadata(temp_db_path, tmp_path): file_path = tmp_path / "doc.md" file_path.write_text("hello") @@ -24,6 +32,7 @@ async def test_fs_ingest_writes_source_revision_to_metadata(temp_db_path, tmp_pa @pytest.mark.asyncio +@pytest.mark.vcr() async def test_fs_second_sweep_emits_unchanged_after_ingest(temp_db_path, tmp_path): """The full round-trip: ingest a file, build a sync_state-shaped snapshot from document.metadata, hand it to FSSource.discover() — must see @@ -46,6 +55,7 @@ async def test_fs_second_sweep_emits_unchanged_after_ingest(temp_db_path, tmp_pa @pytest.mark.asyncio +@pytest.mark.vcr() async def test_fs_second_sweep_emits_upsert_when_file_changes(temp_db_path, tmp_path): """Counterpart to the unchanged test: a file modified after ingest still triggers UPSERT. Ensures the round-trip doesn't accidentally over-skip.""" @@ -72,6 +82,7 @@ async def test_fs_second_sweep_emits_upsert_when_file_changes(temp_db_path, tmp_ @pytest.mark.asyncio +@pytest.mark.vcr() async def test_fs_head_short_circuit_skips_fetch_for_unchanged_revision( temp_db_path, tmp_path, monkeypatch ): diff --git a/tests/test_client_analyze.py b/tests/test_client_analyze.py index 3c4c3521..ed9313b8 100644 --- a/tests/test_client_analyze.py +++ b/tests/test_client_analyze.py @@ -89,8 +89,10 @@ class TestClientAnalysisIntegration: @pytest.mark.asyncio @pytest.mark.vcr() - async def test_analyze_search_and_extract(self, allow_model_requests, temp_db_path): - pdf_path = Path("tests/data/doclaynet.pdf") + async def test_analyze_search_and_extract( + self, allow_model_requests, temp_db_path, doclaynet_first_page_pdf + ): + pdf_path = doclaynet_first_page_pdf config = AppConfig() config.processing.conversion_options.do_ocr = False @@ -122,6 +124,11 @@ class TestClientAnalysisIntegration: for label in expected_labels if label in answer_lower or label.replace("-", " ") in answer_lower ] - assert len(found_labels) >= 6, ( - f"Expected at least 6 labels, found {len(found_labels)}: {found_labels}" + # Page 0 of the DocLayNet paper (title + abstract) names fewer + # element types than the older single-page sample did. The + # assertion still checks that analyze() pulled real content out + # of the doc; ≥3 of the canonical 11 labels is a reasonable + # floor for a single-page extract. + assert len(found_labels) >= 3, ( + f"Expected at least 3 labels, found {len(found_labels)}: {found_labels}" ) diff --git a/tests/test_context_enhancement.py b/tests/test_context_enhancement.py index aad53a6d..977df96c 100644 --- a/tests/test_context_enhancement.py +++ b/tests/test_context_enhancement.py @@ -522,20 +522,20 @@ async def test_expand_context_no_base64_images(temp_db_path): @pytest.mark.vcr() -async def test_expand_context_no_base64_images_docling_local(temp_db_path): +async def test_expand_context_no_base64_images_docling_local( + temp_db_path, doclaynet_first_page_pdf +): """Ensure expanded context from real PDF does not contain base64 image data. - Tests end-to-end with doclaynet.pdf using docling-local converter. + Tests end-to-end with a single page of doclaynet.pdf using docling-local converter. """ - from pathlib import Path - config = AppConfig() config.processing.converter = "docling-local" config.processing.chunker = "docling-local" config.processing.conversion_options.do_ocr = False async with HaikuRAG(temp_db_path, config=config, create=True) as client: - pdf_path = Path(__file__).parent / "data" / "doclaynet.pdf" + pdf_path = doclaynet_first_page_pdf result = await client.create_document_from_source(pdf_path) doc = result if not isinstance(result, list) else result[0] assert doc.id is not None