diff --git a/examples/attachment_management/manage_attachments.ipynb b/examples/attachment_management/manage_attachments.ipynb index 5a25a2f..9cd118d 100644 --- a/examples/attachment_management/manage_attachments.ipynb +++ b/examples/attachment_management/manage_attachments.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "4755b294", "metadata": {}, "outputs": [], @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "df33c2b4", "metadata": {}, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "a29d6888", "metadata": {}, "outputs": [], @@ -135,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "fae4dee3", "metadata": {}, "outputs": [], @@ -153,19 +153,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "7f4b1a9c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INDEX_NAME='tmp_20231020150637'\n", - "index_key='89d0d034c8dabd59d3f066e8f23f2dfa7476d5cd'\n" - ] - } - ], + "outputs": [], "source": [ "print(f\"{INDEX_NAME=}\")\n", "data_index = api.data_indices.create(proj_key=PROJ_KEY, name=INDEX_NAME, type=\"Generic\")\n", @@ -183,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "abfe5cdd", "metadata": {}, "outputs": [], @@ -200,92 +191,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "9d2e2c6e", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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For example,\n", "you might have generated your documents directly in JSON format, or you might have converted the documents\n", @@ -15,6 +15,9 @@ "in Deep Search.\n", "\n", "\n", + "*deprecated; in future we will accept only Docling Docs\n", + "\n", + "\n", "## Access required\n", "\n", "The content of this notebook requires access to Deep Search capabilities which are not\n", diff --git a/examples/bring_your_own_pdf/upload_explore_download_pdfs.ipynb b/examples/bring_your_own_pdf/upload_explore_download_pdfs.ipynb index 8fc4fcb..bef9197 100644 --- a/examples/bring_your_own_pdf/upload_explore_download_pdfs.ipynb +++ b/examples/bring_your_own_pdf/upload_explore_download_pdfs.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 22, "id": "13560ae0", "metadata": { "execution": { @@ -58,7 +58,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The example will be executed on the Deep Search instance ds-internal\n" + "The example will be executed on the Deep Search instance None\n" ] } ], @@ -73,7 +73,7 @@ "PROJ_KEY = notebook_settings.proj_key # project to use\n", "INDEX_NAME = notebook_settings.new_idx_name # index to create\n", "CLEANUP = notebook_settings.cleanup # whether to clean up\n", - "INPUT_FILES_FOLDER = Path(\"../../data/samples/\")\n", + "INPUT_FILE_PATH = Path(\"../../data/samples/2206.00785.pdf\")\n", "INPUT_OCR_FILE = Path(\"../../data/scanned-samples/2206.00785-7.png\")\n", "\n", "############\n", @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 23, "id": "1ea3cd26", "metadata": { "execution": { @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 24, "id": "fae4dee3", "metadata": { "execution": { @@ -168,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 25, "id": "f5496626", "metadata": { "execution": { @@ -189,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 26, "id": "530fd67a", "metadata": { "execution": { @@ -200,17 +200,28 @@ "shell.execute_reply.started": "2024-05-27T10:46:46.232420Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['SUCCESS']" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Upload and convert documents\n", "data_indices_utils.upload_files(\n", - " api=api, coords=data_index.source, local_file=INPUT_FILES_FOLDER\n", + " api=api, coords=data_index.source, local_file=INPUT_FILE_PATH\n", ")" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 27, "id": "bfe83b90", "metadata": { "execution": { @@ -225,7 +236,7 @@ { "data": { "text/markdown": [ - "The data is now available. You can query it programmatically (see next section) or access it via the Deep Search UI at
https://cps.foc-deepsearch.zurich.ibm.com//projects/e74892839ff04df16a87cae3e96248a24eec7756/library/private/44770b3d86403db9fe2afb8339c82db6478a3807" + "The data is now available. You can query it programmatically (see next section) or access it via the Deep Search UI at
https://pr-516-cps-dev.deepsearch-dev.zurich.ibm.com/projects/468b81ffe515a99172f93b07dd20cd50d4c19a3b/library/private/3ae2e330eb133a1bf4657d256290eece5dfc537f" ], "text/plain": [ "" @@ -261,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 28, "id": "6a9a31ab", "metadata": { "execution": { @@ -277,7 +288,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The data index contains 3 entries.\n" + "The data index contains 1 entries.\n" ] } ], @@ -291,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 29, "id": "a669e5a9", "metadata": { "execution": { @@ -306,7 +317,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d8b53e1cc3554a8c88a04a8fdb064f50", + "model_id": "ee6a0e72d9b044cd87ae98180be10ad6", "version_major": 2, "version_minor": 0 }, @@ -362,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 30, "id": "e997bbae", "metadata": { "execution": { @@ -444,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 31, "id": "b7addff1-b857-463a-b82c-02ed9dd5ef5f", "metadata": { "execution": { @@ -459,7 +470,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e04a6352dc88483d9f30f2360047a5ff", + "model_id": "ed559131f4cd45509352161c238fc0df", "version_major": 2, "version_minor": 0 }, @@ -474,8 +485,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Finished fetching all data. Total is 3 records.\n", - "Data downloaded in /var/folders/32/5m4rnj816kl4pxl4vm97_wbh0000kp/T/tmpzb6oosaa\n" + "{'_index': 'cps-dev-deepsearch-dev-projdata3ae2e3', '_id': '6627d1b67955c51ff1aa8858de671bb5a62ad70c77e62e0ac57c153d0078b7ea', '_score': None, '_source': {'_name': '', 'type': 'pdf-document', 'description': {'logs': [{'agent': 'CPS Docling', 'type': 'parsing', 'comment': 'Docling 2.7.1 parsing of documents', 'date': '2025-01-16T13:53:04.836922+00:00'}], 'collection': {'type': 'Document'}}, 'file-info': {'filename': '2206.00785.pdf', 'document-hash': '6627d1b67955c51ff1aa8858de671bb5a62ad70c77e62e0ac57c153d0078b7ea', '#-pages': 11, 'page-hashes': [{'hash': 'dc8ab77215bdf5d1c50e4635bc886078ceb862131df9de406624cb73be373fc1', 'model': 'default', 'page': 1}, {'hash': '58474aac22030f7608c307aeb1aee7b69b8b4ca533c80ae41ab68153790d6ab0', 'model': 'default', 'page': 2}, {'hash': '1d96fb50e9a4d970cab5199fa3b76469b804c2457390c4bdda04890bc651b813', 'model': 'default', 'page': 3}, {'hash': '4dc4d130627b1f9841208469936e65cf4667f47730044ef31ecbf9664a41d706', 'model': 'default', 'page': 4}, {'hash': '4ba309b0bcae706030066723f66989d89818f00305702bbd5935d6eca9d4db6d', 'model': 'default', 'page': 5}, {'hash': '09357309f7ee2ad6efe2951fcc314bcdac7f6296b244724a78d9e9037bd071ff', 'model': 'default', 'page': 6}, {'hash': '1b8ad8508fe45a56af6aafcaeefcefa168b0b0e8530b9b0c60be1542ce001c14', 'model': 'default', 'page': 7}, {'hash': '8289d80b04962b29ea5f2967b69ca0ae43fb56535cef4fe36630c58a9edde998', 'model': 'default', 'page': 8}, {'hash': 'ba08e96ab334e61dfcde2a71203a96856d5261280e445df5f7d84d7e5974170e', 'model': 'default', 'page': 9}, {'hash': 'f90176f27b3737c29840cf46f7c23ef86f607a2edd94f4c4201e931817f7d2ee', 'model': 'default', 'page': 10}, {'hash': '5c71fa7ee304c4d871411bbb71734c39660173335fc28f955dc3ee8e044fd748', 'model': 'default', 'page': 11}]}, 'main-text': [{'prov': [{'bbox': [17.247167587280273, 236.501953125, 36.33979415893555, 572.52001953125], 'page': 1, 'span': [0, 37], '__ref_s3_data': None}], 'text': 'arXiv:2206.00785v1 [cs.DL] 1 Jun 2022', 'type': 'page-header', 'name': 'Page-header', 'font': None}, {'prov': [{'bbox': [55.040950775146484, 685.9405517578125, 556.8915405273438, 738.414794921875], 'page': 1, 'span': [0, 89], '__ref_s3_data': None}], 'text': 'Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [104.35169982910156, 622.2041015625, 203.9119873046875, 670.6500244140625], 'page': 1, 'span': [0, 75], '__ref_s3_data': None}], 'text': '1 st Christoph Auer IBM Research Ruschlikon, Switzerland cau@zurich.ibm.com', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [98.21205139160156, 542.8441162109375, 213.3932647705078, 591.4330444335938], 'page': 1, 'span': [0, 81], '__ref_s3_data': None}], 'text': '4 th Cesar Berrospi Ramis IBM Research Ruschlikon, Switzerland ceb@zurich.ibm.com', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [262.605712890625, 622.2041625976562, 362.1552734375, 671.0858154296875], 'page': 1, 'span': [0, 74], '__ref_s3_data': None}], 'text': '2 nd Michele Dolfi IBM Research Ruschlikon, Switzerland dol@zurich.ibm.com', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [263.0400390625, 542.8441772460938, 362.1445007324219, 591.435302734375], 'page': 1, 'span': [0, 77], '__ref_s3_data': None}], 'text': '5 th Peter W.J. Staar IBM Research Ruschlikon, Switzerland taa@zurich.ibm.com', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [47.97799301147461, 252.72711181640625, 300.4272766113281, 501.2793884277344], 'page': 1, 'span': [0, 1518], '__ref_s3_data': None}], 'text': 'Abstract -Document understanding is a key business process in the data-driven economy since documents are central to knowledge discovery and business insights. Converting documents into a machine-processable format is a particular challenge here due to their huge variability in formats and complex structure. Accordingly, many algorithms and machine-learning methods emerged to solve particular tasks such as Optical Character Recognition (OCR), layout analysis, table-structure recovery, figure understanding, etc. We observe the adoption of such methods in document understanding solutions offered by all major cloud providers. Yet, publications outlining how such services are designed and optimized to scale in the cloud are scarce. In this paper, we focus on the case of document conversion to illustrate the particular challenges of scaling a complex data processing pipeline with a strong reliance on machine-learning methods on cloud infrastructure. Our key objective is to achieve high scalability and responsiveness for different workload profiles in a well-defined resource budget. We outline the requirements, design, and implementation choices of our document conversion service and reflect on the challenges we faced. Evidence for the scaling behavior and resource efficiency is provided for two alternative workload distribution strategies and deployment configurations. Our best-performing method achieves sustained throughput of over one million PDF pages per hour on 3072 CPU cores across 192 nodes.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.34965896606445, 232.261962890625, 300.6181335449219, 251.952392578125], 'page': 1, 'span': [0, 103], '__ref_s3_data': None}], 'text': 'Index Terms -cloud applications, document understanding, distributed computing, artificial intelligence', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [135.26025390625, 209.32899475097656, 213.54257202148438, 218.677490234375], 'page': 1, 'span': [0, 15], '__ref_s3_data': None}], 'text': 'I. INTRODUCTION', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [48.13774871826172, 72.90673828125, 300.5203857421875, 203.037841796875], 'page': 1, 'span': [0, 668], '__ref_s3_data': None}], 'text': 'Over the past decade, many organizations have accelerated their transformation into data-driven businesses, as studies have shown its positive impact in efficiency, decision making, or financial performance [1], [2]. Leading companies are increasingly deploying workloads on public and private cloud infrastructure, including business intelligence processing and machine learning models in data analytics platforms [3]. This is owed to several factors such as high availability, lower cost for compute, and storage [4], as well as the flexibility to scale up or down a cloud-based business process to fit the operational needs. Workloads and services can be container-', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.1437683105469, 479.3659973144531, 563.2503662109375, 501.63055419921875], 'page': 1, 'span': [0, 108], '__ref_s3_data': None}], 'text': 'ized, deployed, and orchestrated through widely adopted and standardized platforms like Kubernetes [5], [6].', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.15472412109375, 347.8852844238281, 563.2810668945312, 477.949462890625], 'page': 1, 'span': [0, 673], '__ref_s3_data': None}], 'text': 'A key business process relevant to many companies is document understanding. Documents may constitute contracts, guidelines, manuals, presentations, papers, etc., which contain valuable knowledge for their operations. We observe that several specialized companies and all major cloud providers offer dedicated services (SaaS) for various aspects of document understanding such as Optical Character Recognition (OCR) (e.g., Amazon Textract$^{1}$), forms, and invoice parsing (Docparser$^{2}$, Nanonets$^{3}$, Google Document AI$^{4}$, Microsoft SharePoint Syntex$^{5}$), or conversion of unstructured formats such as PDF into structured content (IBM Watson Discovery$^{6}$).', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [310.9252014160156, 143.3106689453125, 563.5765380859375, 346.3759460449219], 'page': 1, 'span': [0, 1015], '__ref_s3_data': None}], 'text': 'Conversion of PDF documents into a structured, machineprocessable format is a particularly challenging business process due to the high variability and weak normalization of its input. To name a few dimensions of variability, PDF documents can be short or long, encode programmatic or scanned content, have simple or complex page layouts, may contain tables or figures, etc. Thus, the process of recovering their structure and extracting content in high detail entails several dynamic steps (see Fig. 1). On the computational side, this relies on multiple algorithms and machine-learning (ML) models specialized for particular tasks. Examples for such models include OCR [7], document layout analysis [8]-[10], table structure recovery [11], [12], figure understanding [13], reference and citation resolution [14], etc. Furthermore, the ML landscape is evolving rapidly, with new models frequently exposing significantly different characteristics in terms of computational expenses, memory usage, or accelerator re-', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [319.5043029785156, 123.1485366821289, 426.5840148925781, 131.6661376953125], 'page': 1, 'span': [0, 37], '__ref_s3_data': None}], 'text': '$^{1}$https://aws.amazon.com/textract', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [319.4458312988281, 113.28553009033203, 392.4120788574219, 121.45208740234375], 'page': 1, 'span': [0, 27], '__ref_s3_data': None}], 'text': '$^{2}$https://docparser.com', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [319.6492919921875, 103.42252349853516, 427.7674255371094, 111.71142578125], 'page': 1, 'span': [0, 38], '__ref_s3_data': None}], 'text': '$^{3}$https://nanonets.com/invoice-ocr', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [319.6199645996094, 93.4466552734375, 444.7857360839844, 101.82421875], 'page': 1, 'span': [0, 42], '__ref_s3_data': None}], 'text': '$^{4}$https://cloud.google.com/document-ai', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [319.3656311035156, 83.270263671875, 546.199951171875, 91.654296875], 'page': 1, 'span': [0, 73], '__ref_s3_data': None}], 'text': '$^{5}$https://docs.microsoft.com/en-us/microsoft-365/contentunderstanding', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [318.8615417480469, 73.23809814453125, 470.4822998046875, 81.661865234375], 'page': 1, 'span': [0, 48], '__ref_s3_data': None}], 'text': '$^{6}$https://www.ibm.com/cloud/watson-discovery', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [415.03265380859375, 622.1795654296875, 513.1004638671875, 670.93896484375], 'page': 1, 'span': [0, 72], '__ref_s3_data': None}], 'text': \"3 rd Andr'e Carvalho SoftINSA Lda. Tomar, Portugal afecarvalho@gmail.com\", 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [169.74295043945312, 539.94189453125, 441.62701416015625, 548.11376953125], 'page': 2, 'span': [0, 78], '__ref_s3_data': None}], 'text': 'Fig. 1. Sketch of operation dependency graph in a document conversion pipeline', 'type': 'caption', 'name': 'Caption', 'font': None}, {'name': 'Picture', 'type': 'figure', '$ref': '#/figures/0'}, {'prov': [{'bbox': [48.25772476196289, 400.16717529296875, 300.41497802734375, 517.3280029296875], 'page': 2, 'span': [0, 574], '__ref_s3_data': None}], 'text': 'quirements. Additionally, many bottlenecks hide in logical operations such as parsing the PDF code, rasterizing a PDF page to a bitmap, or serialization and database transactions. On top of that, a cloud service to solve this business task needs to be flexible enough to support different consumption modes. Some users may need to convert large document repositories in bulk, expecting high throughput, while others may want to convert a single document ad-hoc, expecting short response time. Satisfying both expectations in a common application architecture is non-trivial.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.10683059692383, 236.3572998046875, 300.54351806640625, 389.4085998535156], 'page': 2, 'span': [0, 760], '__ref_s3_data': None}], 'text': 'In short, a cloud service for document conversion needs to be easily adaptable, must scale with model resource demand and workload sizes, and ideally avoids strong assumptions about the consumption mode. As such, it shares many traits with cloud services for other business processes but also holds unique challenges, which make it an interesting systems engineering target. In this paper, we take the subject of document conversion as a case to illustrate the particular challenges of scaling a complex data processing pipeline with strong reliance on ML methods on cloud infrastructure. We outline the practical problems our team faced while developing a cloud service of such nature [15] and present solutions in several aspects. Our main contributions are:', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [58.926998138427734, 169.04527282714844, 300.02191162109375, 214.8209228515625], 'page': 2, 'span': [0, 177], '__ref_s3_data': None}], 'text': '- 1) We propose a validated novel approach to efficiently scale an end-to-end processing pipeline leveraging ML in a cloud environment for the use-case of PDF document conversion.', 'type': 'paragraph', 'name': 'List-item', 'font': None}, {'prov': [{'bbox': [58.82088088989258, 144.35357666015625, 300.02191162109375, 167.091552734375], 'page': 2, 'span': [0, 113], '__ref_s3_data': None}], 'text': '- 2) We reflect on the design considerations, challenges, and implementation choices of our PDF conversion service.', 'type': 'paragraph', 'name': 'List-item', 'font': None}, {'prov': [{'bbox': [58.47614288330078, 120.86260986328125, 300.0218811035156, 142.80609130859375], 'page': 2, 'span': [0, 89], '__ref_s3_data': None}], 'text': '- 3) We introduce a set of comparative metrics to evaluate the performance characteristics.', 'type': 'paragraph', 'name': 'List-item', 'font': None}, {'prov': [{'bbox': [58.079864501953125, 72.9669189453125, 300.2283630371094, 118.8519287109375], 'page': 2, 'span': [0, 205], '__ref_s3_data': None}], 'text': '- 4) We present and analyze the resulting scaling and response behavior in a benchmark setup on common cloud infrastructure for two alternative workload distribution strategies and deployment configurations.', 'type': 'paragraph', 'name': 'List-item', 'font': None}, {'prov': [{'bbox': [320.0240478515625, 495.8082580566406, 554.480712890625, 517.8453979492188], 'page': 2, 'span': [0, 68], '__ref_s3_data': None}], 'text': 'II. REQUIREMENTS AND OBJECTIVES FOR DOCUMENT CONVERSION IN THE CLOUD', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [311.1315002441406, 324.1973876953125, 563.376708984375, 489.4632873535156], 'page': 2, 'span': [0, 854], '__ref_s3_data': None}], 'text': 'As teased in the introduction, a cloud service for document conversion provides a particularly broad and challenging set of requirements, which are not trivial to satisfy all at once. Precisely this combination of real-world requirements make it an interesting target for a case-study on scaling a business process in the cloud. Broadly speaking, we can split the requirements into three categories, namely 1. the ability to process documents with a wide variety in characteristics at scale, 2. the ability to incorporate and consume several ML models for different tasks and 3. the ability to perform well in different user consumption modes ( bulk or ad-hoc ) which sit on opposite ends. It should be noted that only the first category is linked to the domain problem of document conversion. The latter two categories are shared by many cloud services.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.1156311035156, 73.162353515625, 563.4229736328125, 322.1588134765625], 'page': 2, 'span': [0, 1284], '__ref_s3_data': None}], 'text': 'PDF document processing: We work under the premise that a document conversion service must treat a single document as an atomic data-unit. For instance, a single output file in JavaScript Object Notation (JSON) needs to be produced for each PDF file that is submitted. Contrary to other data formats such as images or plain text, documents have an internal granularity in the shape of pages. This trait can be exploited, since many operations can be executed independently on each page. Examples of such operations are text-cell identification (either through parsing programmatic PDF code or through OCR), layout segmentation, table-structure recovery, figure classification, etc. Two critical constraints need to be recognized here. Firstly, the intra-page operations may have dependencies. For instance, table-structure recovery relies on text-cell identification and on layout segmentation to locate a table. Secondly, the results of each operation on each page needs to be ultimately merged into a single output document (see Figure 1, phase D). This merge operation ensures that the content of the document has the correct section-structure and ordering. It is therefore an invalid assumption that the conversion of a document can be simply treated as an embarrassingly parallel', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [47.93190002441406, 539.6549072265625, 300.59576416015625, 740.781494140625], 'page': 3, 'span': [0, 1025], '__ref_s3_data': None}], 'text': 'set of operations over its pages. Rather, to obtain the converted document, one has to execute a complex operation dependency graph which dynamically forks and merges on two levels (see Figure 1 phase B and C). While this provides opportunity for highly concurrent execution, it can create significant imbalance in the number and runtime of operations for a given page, due to their extremely varying complexity. Adding to this, the PDF format has inherent properties that make it very opaque . Therefore, there is no practical way to estimate the complexity and resource demand required for processing ahead of execution time. From an architecture perspective, the above is suggesting an approach which decomposes the full chain of operations and data into tasks . Such tasks can then be created and executed concurrently through an orchestration mechanism that is aware of their dependencies. We outline implementation options and examine the advantages and tradeoffs with different task granularity in Sections III and IV.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.09360885620117, 336.267822265625, 300.3430480957031, 537.7481689453125], 'page': 3, 'span': [0, 990], '__ref_s3_data': None}], 'text': 'Machine-learning models: ML models play a powerful role in the quality of document conversion. To date, they outcompete any rule-based algorithm in document conversion tasks and even approach human-like accuracy in particular tasks such as layout analysis [16]. From a systems perspective, ML models bring an own set of challenges. First, solving the conversion task requires many different types of ML models as outlined earlier. Each of these models has different runtime characteristics and resource requirements in terms of CPU cores, memory demand, and accelerators. Furthermore, ML models evolve at a rapid pace. Consequently, to quickly adopt state-of-the-art ML models in production, our cloud service needs to be flexible with regard to integration of new models. Encapsulation of ML models into separate microservices, each serving on an own endpoint, can provide for both resource isolation and easy integration. We will discuss choices for ML serving and scaling in Section III.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.03481674194336, 72.84271240234375, 300.5635070800781, 334.2709045410156], 'page': 3, 'span': [0, 1320], '__ref_s3_data': None}], 'text': 'User requirements: The requirements outlined above are all system-internal requirements. From a user experience perspective, there are strong requirements to be met regarding the conversion speed and the response time. Our goal is to provide a cloud service which supports both bulk-conversion of huge document repositories, as well as ad-hoc conversion of individual documents. In the former case, a key concern is to achieve the highest (sustained) document throughput and ensure it scales proportionally to the compute resources. Importantly, this should allow to set a time-budget for a large conversion workload, e.g., a full online library, and infer the compute resource cost, or conversely, estimate the conversion time with a given resource budget. In the latter case, the primary objective is to achieve the shortest time-tosolution (TTS), allowing for an interactive user experience. Here it is important to note that the smaller a workload is (e.g., one document), the more challenging it becomes to achieve proper workload balancing in a distributed processing scheme. Both goals are reflecting in the scaling behavior of the system, which we will examine in Section IV-E. Additionally, we want to enable multi-tenant operation for many users on one application instance. Therefore, the service must provide', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.06005859375, 683.1170654296875, 563.045166015625, 740.9441528320312], 'page': 3, 'span': [0, 259], '__ref_s3_data': None}], 'text': 'reasonable fairness when working at full capacity and multiple users each submit a conversion job. That means, submitted conversion jobs cannot be simply processed serially, but the service should allow to schedule multiple jobs concurrently with short delay.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [310.9884948730469, 563.0549926757812, 563.4898071289062, 681.1704711914062], 'page': 3, 'span': [0, 601], '__ref_s3_data': None}], 'text': 'Evaluation dimensions: In addition to functional, technical, and experience requirements, we also need to consider performance evaluation metrics, which typically fall into three dimensions: speed , resource cost , and quality . In this paper, we will not focus on conversion quality , since it is directly determined by the choice of models and their accuracy. Model performance for different tasks is backed by many peerreviewed results [16]. Instead, this paper will focus on speed and resource cost , and how they correlate given a fixed system configuration with selected state-of-the-art models.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [391.8093566894531, 543.4899291992188, 482.7892150878906, 552.8233642578125], 'page': 3, 'span': [0, 18], '__ref_s3_data': None}], 'text': 'III. SYSTEM DESIGN', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [311.1693420410156, 479.4771423339844, 563.0579833984375, 537.5883178710938], 'page': 3, 'span': [0, 294], '__ref_s3_data': None}], 'text': 'Having established the problem domain, requirements, and objectives, this section will outline and reflect on the fundamental application architecture of our document conversion service. Further, we establish a distributed task orchestration mechanism and discuss aspects of its implementation.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [310.40386962890625, 459.4205322265625, 423.4620361328125, 469.8033447265625], 'page': 3, 'span': [0, 27], '__ref_s3_data': None}], 'text': 'A. Application architecture', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [310.8529052734375, 324.1124267578125, 563.23583984375, 454.426513671875], 'page': 3, 'span': [0, 665], '__ref_s3_data': None}], 'text': 'Two fundamental decisions to be made prior to any implementation are the technology stack and architecture patterns to use. Given its clear dominance in virtually every contribution by the ML community, its proven utility in data-science tasks and the broad support in webservice frameworks [17]-[19], we chose python as the primary implementation language. Another priority we set is to work close to the bare kubernetes stack, in order to exercise lower-level control over the workload distribution, scaling, and resource creation. For this reason, we ruled out full-fledged data engines (e.g., Apache Spark [20]) and avoided proprietary or vendor-specific tools.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [310.9093017578125, 217.00413513183594, 563.3410034179688, 322.2803649902344], 'page': 3, 'span': [0, 478], '__ref_s3_data': None}], 'text': 'The other critical decision regards the application architecture. Here, one can find fundamentally different options, depending on the type and volume of data, their processing complexity, and the consumption modes. As outlined in section II, we expect high-volume workloads which can be partitioned horizontally and processed in parallel to a high degree. On the other hand, we want to satisfy expectations of short response times for ad-hoc conversion with small data volumes.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [310.9143981933594, 73.40312194824219, 563.4306640625, 214.8663330078125], 'page': 3, 'span': [0, 723], '__ref_s3_data': None}], 'text': 'For the latter case, an architecture optimized for real-time processing would appear to be a sensible choice. An example for this is a microservice-based application with a user-facing, synchronous REST API and a backend which implements the document processing through a chain of remote-procedurecalls (RPC) between these microservices. With microservice replication, backed by sufficient compute resources or elastic cloud infrastructure, responsive operation could be upheld even for several concurrent users. Still, this approach would certainly fail to scale to high-volume workloads such as document repositories or libraries, especially in the case where the service is used for ad-hoc conversion and bulk conversion', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.29470443725586, 658.69287109375, 300.327880859375, 740.9005126953125], 'page': 4, 'span': [0, 394], '__ref_s3_data': None}], 'text': 'at the same time. Given a bulk conversion workload in the order of hundreds of documents, the client requests would soon stack up and cause congestion, forcing the system to deny more requests. Dynamically, let alone spontaneously, scaling the microservice replication to a level which can absorb this would be extremely challenging in terms of resource management and also be cost-prohibitive.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.177391052246094, 417.8052978515625, 300.45684814453125, 489.2463684082031], 'page': 4, 'span': [0, 510], '__ref_s3_data': None}], 'text': 'Fig. 2. Architecture diagram of our conversion service. User requests for document conversion are handled by a REST API service, which can dispatch workloads asynchronously to the compute infrastructure through a queueing mechanism. Workers pick up queued tasks from a message broker and store the results for later retrieval on a document database and cloud object storage. The ML models are served through separate microservices, which are consumed by workers when executing the document conversion pipeline.', 'type': 'caption', 'name': 'Caption', 'font': None}, {'name': 'Picture', 'type': 'figure', '$ref': '#/figures/1'}, {'prov': [{'bbox': [47.96165084838867, 72.57586669921875, 300.37664794921875, 406.59539794921875], 'page': 4, 'span': [0, 1699], '__ref_s3_data': None}], 'text': \"For these reasons, we favored an asynchronous task-based queue-worker architecture suitable for high-volume batchprocessing, involving a task queue and containerized worker processes in the backend. Here, the user-facing API only accepts a document conversion request and enqueues a corresponding task on a message broker. Clients receive an immediate response with the task's identifier, which can be used to retrieve the conversion result once the processing is completed. The worker processes attach to a task queue, consume tasks competitively, and write status information and task output to a (transient) results-backend and a persistent database (see Figure 2). Since large conversion workloads can be partitioned horizontally, workers are not required to communicate to each other or share resources. Hence, worker processes can be run isolated in containers and trivially replicated both inside and across cluster nodes through kubernetes. Tasks produced in a conversion job can reside on the task queue or on a worker process and eventually persist their state on the results-backend. Together, this ensures that a task remains traceable over its whole lifetime. As such, a queue-worker architecture protects the system from compute congestion through request overload, promises reliability, and enables simple scaling, at the cost of potentially delayed processing. However, queue mechanisms may also add I/O overhead and latency to the system, even in idle state. Consequently, TTS in ad-hoc conversion may be handicapped. To minimize such adverse effects, we chose to use online workers, which boot at service deployment time. This makes their lifetime independent of any conversion job\", 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.16973876953125, 670.67041015625, 563.1245727539062, 740.8549194335938], 'page': 4, 'span': [0, 349], '__ref_s3_data': None}], 'text': 'and allows to pre-load the codebase, initialize resources, and keep internal state across tasks (such as cached connections and database drivers). Further, we chose to implement a lightweight library which enables low-latency task orchestration as well as fairness between multiple conversion jobs. Details are provided in the following subsections.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [310.9786071777344, 396.32208251953125, 563.35595703125, 669.229736328125], 'page': 4, 'span': [0, 1328], '__ref_s3_data': None}], 'text': 'We introduce a notable exception from the queue-worker pattern for serving ML models. Instead of executing ML inference codes inside the workers, we externalize ML models as microservices with a simple REST API. The slightly less efficient interface and added I/O compared to direct codebinding and shared memory inside a worker process has proven to be a reasonable sacrifice to the significantly lower integration effort for new models. Ease of integration is a key requirement in the fast-evolving ML model landscape. Additionally, model serving can be resource-budgeted and scaled out independently from workers. The latter is particularly relevant for resource efficiency since our ML inference is computationally far more expensive and at the same time far less dynamic in memory use than other operations (e.g., PDF splitting, parsing, and merging). Leveraging this benefit would otherwise demand several different worker types and queues (one for each model), therefore increasing the complexity in the core system. In our service implementation, we wrap ML model codes in a python aiohttp webserver runtime and rely on knative serving for scaling and load-balancing [21]. This compares well to the technology used in popular ML serving frameworks such as ray.io $^{7}$, Seldon Core $^{8}$, or TensorFlow Serving $^{9}$.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.2190856933594, 378.6492004394531, 451.3511657714844, 388.47711181640625], 'page': 4, 'span': [0, 34], '__ref_s3_data': None}], 'text': 'B. Task orchestration architecture', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [311.0847473144531, 255.86019897460938, 563.4871215820312, 373.316162109375], 'page': 4, 'span': [0, 572], '__ref_s3_data': None}], 'text': \"We define one conversion job as one (asynchronous) call to our service's API endpoint, which may request conversion of any number and length of PDF files. To organize the operations within a conversion job, we define tasks . A task is defined by a code function which performs an operation, by the data it processes, and an identifier which makes it traceable across the backend architecture. All tasks belong to a conversion job, which itself acts as the root-task. Tasks will be executed on the workers and cover all operations, with the exception of ML model inference.\", 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.08941650390625, 112.442138671875, 563.5247192382812, 253.7100830078125], 'page': 4, 'span': [0, 729], '__ref_s3_data': None}], 'text': 'Obviously, implementing the pipeline outlined in Figure 1 in a purely serial fashion, meaning as a single task for each conversion job, would not promise any reasonable efficiency, even if multiple jobs were processed in parallel on different workers. To use available cluster resources efficiently, we focus on two strategies in particular. First of all, we try to balance the workload distribution evenly. Therefore, we split the conversion jobs into smaller tasks, which can be processed independently on different workers. Secondly, we strive to avoid that workers are underutilizing CPU resources while waiting for I/O-bound operations, such as reading and writing files, database transactions, or network requests to the ML', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [319.5000915527344, 93.22607421875, 382.4632263183594, 102.552001953125], 'page': 4, 'span': [0, 24], '__ref_s3_data': None}], 'text': '$^{7}$https://www.ray.io', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [319.1501159667969, 83.304443359375, 509.3135986328125, 91.548828125], 'page': 4, 'span': [0, 63], '__ref_s3_data': None}], 'text': '$^{8}$https://www.seldon.io/solutions/open-source-projects/core', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [319.06390380859375, 73.2734375, 466.4934387207031, 81.6090087890625], 'page': 4, 'span': [0, 50], '__ref_s3_data': None}], 'text': '$^{9}$https://www.tensorflow.org/tfx/guide/serving', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [48.68336868286133, 718.9821166992188, 300.251708984375, 740.7255859375], 'page': 5, 'span': [0, 83], '__ref_s3_data': None}], 'text': 'models. Therefore, we enable concurrency between multiple tasks on the same worker.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.233646392822266, 346.260009765625, 300.12921142578125, 390.9906005859375], 'page': 5, 'span': [0, 395], '__ref_s3_data': None}], 'text': 'Fig. 3. Sketch of workload distribution schemes on the document-level (top) and page-level (bottom). A conversion job is submitted at time t$_{0}$. Tasks are produced and consumed by the workers through the task queue. The queue drains at time t$_{d}$. Sustained throughput is upheld until the first worker finds no more work (t$_{w}$). The job only finishes after completion of all tasks (TTS).', 'type': 'caption', 'name': 'Caption', 'font': None}, {'name': 'Picture', 'type': 'figure', '$ref': '#/figures/2'}, {'prov': [{'bbox': [48.275936126708984, 168.78912353515625, 300.3795166015625, 334.2211608886719], 'page': 5, 'span': [0, 800], '__ref_s3_data': None}], 'text': 'Data partitioning and workload distribution: We explore two schemes for partitioning data and forming tasks in a conversion job: a) per-document and b) in equal-sized batches of pages, across document boundaries. In the former case, each document contained in a conversion job is fully processed into the corresponding JSON output on its own subtask, which we further refer to as the document-level distribution scheme . In the latter case, we further increase granularity by producing tasks for equal-sized batches of pages, further referred to as page-level distribution scheme (see Fig. 3). As sketched out, we expect to see stronger workload imbalance in the document-level distribution scheme, leading to underutilization of workers in the tail part of a conversion job (from t$_{w}$ until TTS).', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.253902435302734, 72.8404541015625, 300.1933288574219, 166.87823486328125], 'page': 5, 'span': [0, 459], '__ref_s3_data': None}], 'text': 'A conversion job using the document-level distribution scheme consists of N + 1 tasks for N PDF documents, where the root task has the responsibility of iterating over the data source and submitting one task for every document to the queue. While the document-level scheme can be implemented rather straightforward without any dependencies between tasks, the page-level scheme introduces significantly more complexity. It becomes evident from Fig. 1 that some', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [310.8545837402344, 539.6549072265625, 563.4945068359375, 740.6552734375], 'page': 5, 'span': [0, 976], '__ref_s3_data': None}], 'text': 'stages in the pipeline require to work on the full document (e.g., merging pages into the full document JSON output and exporting to the target), while others can be run pageby page (e.g., PDF parsing and applying ML models). We therefore chose to define specialized tasks responsible for each processing stage, which depend on the results of tasks from the preceding stage. Hence, many tasks need to be produced on the fly and awaited at multiple points from other tasks. Their count depends on factors determined by the PDF content. Another consequence is that partial results from each task need to be stored intermediately for downstream tasks to be picked up. Since downstream tasks may schedule on any worker in any node, this incurs additional data transmission to and from a database or object storage. This particular requirement is not present in the document-level pipeline, because all intermediate data can remain worker-local until a document is fully converted.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [310.9823913574219, 323.7270812988281, 563.3364868164062, 536.9470825195312], 'page': 5, 'span': [0, 1032], '__ref_s3_data': None}], 'text': 'Task concurrency: Keeping allocated cluster resources well saturated, while also not overbooking them, is key to efficient resource usage and high throughput in document conversion. The operations in the conversion pipeline can be roughly divided into those primarily compute-bound and primarily I/Obound. Examples for the former are parsing PDF contents, rendering an image of a PDF page, applying ML models or OCR. Examples for the latter are retrieving a PDF file over the network, unpacking an archive, exporting to a target database, or storing and fetching intermediate task results. A feasible approach we found is to interleave I/O-bound and computebound operations on the same worker, while allowing only one compute-bound operation to execute at the same time. Thus, precisely saturating resources on a cluster node turns into a matter of monitoring resource usage of workers under load and adjusting the amount of worker processes deployed on each node accordingly. Concrete evidence of this is presented in section IV-D.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [310.9559020996094, 155.51666259765625, 563.2556762695312, 320.91015625], 'page': 5, 'span': [0, 825], '__ref_s3_data': None}], 'text': 'We evaluated and discarded multiple implementation options to achieve interleaving, such as threading and explicit callback-passing code. Eventually, we settled on implementing all task code in python asyncio 10 co-routines, which run in an asynchronous event loop. This cooperative concurrency scheme is more efficient compared to launching and managing threads, but it requires a different coding paradigm. Any active task arriving at code points which require to wait for I/O can choose to suspend and return control to the asyncio event loop, and resume at a later point after its waiting condition has been fulfilled. Compute-intense tasks may fill the waiting time and remain in control of execution until they are completed. As such, no second compute-intense task can compete for resources in the same worker process.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.0685729980469, 95.35310363769531, 563.1466674804688, 152.83831787109375], 'page': 5, 'span': [0, 308], '__ref_s3_data': None}], 'text': 'Dynamic task orchestration: Realising the dynamic traits of the proposed task distribution schemes prompted us to find an efficient mechanism to produce (sub-)tasks, submit these to the task queue, and wait for their completion during the pipeline execution. In the context outlined above, this demands for a', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [319.4994201660156, 73.4012451171875, 472.5478515625, 82.676025390625], 'page': 5, 'span': [0, 53], '__ref_s3_data': None}], 'text': '$^{10}$https://docs.python.org/3/library/asyncio.html', 'type': 'page-footer', 'name': 'Page-footer', 'font': None}, {'prov': [{'bbox': [48.14511489868164, 706.8296508789062, 300.26812744140625, 740.992919921875], 'page': 6, 'span': [0, 137], '__ref_s3_data': None}], 'text': 'task distribution code which can itself run inside an asyncio event-loop, such that in-worker concurrency between tasks can be leveraged.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.04045867919922, 371.0640869140625, 300.5950622558594, 704.4154052734375], 'page': 6, 'span': [0, 1685], '__ref_s3_data': None}], 'text': \"Several task distribution libraries have emerged in the community for different requirements and use-cases, for instance, ray.io, Celery$^{11}$, Dramatiq$^{12}$, or Apache Beam$^{13}$. To our surprise, none of the available options could meet our needs, since distribution and execution of asynchronous co-routines as tasks is widely unsupported. Few attempts to provide this capability have remained experimental and are poorly maintained or abandoned [22], [23]. Therefore, we decided to implement a custom library to orchestrate asynchronous tasks, which we make available as open source under the name Mognet $^{14}$. Fundamentally, it allows to create worker processes that initialize an asyncio event loop and attach to an amqp 15 message broker and a redis 16 instance for storage of every task's status. A worker process subscribes to a queue and fetches up to X tasks at once, which are then executed on the worker's event loop concurrently. To support the dynamic nature of our pipeline, we explicitly allow any task to produce (many) subtasks and await their completion. Subtasks are submitted to the task queue, like any task. Since all tasks are asynchronous co-routines, waiting for subtasks is a nonblocking and cheap operation. It is worth mentioning that waiting for subtasks creates a risk for deadlocks. A task may wait for subtasks forever while they cannot be scheduled on any worker, because all workers are occupied up to their limit X . To circumvent this situation, the local limit X of concurrently scheduling tasks on a worker is incremented by 1 each time a task produces any number of subtasks, and decremented by 1 again when the producing task completes.\", 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [47.953495025634766, 144.31390380859375, 300.4158630371094, 369.63275146484375], 'page': 6, 'span': [0, 1143], '__ref_s3_data': None}], 'text': 'Additionally to the orchestration capability outlined above, we exploit the property that such tasks which produce subtasks are very cheap to suspend and reactivate for another significant benefit. Instead of producing and enqueueing every possible subtask at once, the producing task may instead iteratively enqueue new subtasks only as previous subtasks finish. This allows precise control over how many subtasks are submitted from a producer task at once and therefore enables to keep the task queue short. A concrete example for this case is the root task of a conversion job, which produces one subtask for each document. Given a number of N workers, enqueueing much more than N tasks at once is to no benefit for distributed processing. Conversely, it can delay the scheduling of competing conversion jobs. Enqueueing the N +1 st document subtask only when the first document subtask finishes instead gives opportunity for better interleaving of tasks belonging to competing conversion jobs on the queue. The same principle translates to the level of pages inside one document. We demonstrate the effect of this strategy in section IV-F.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [56.923988342285156, 122.95361328125, 138.96153259277344, 132.4644775390625], 'page': 6, 'span': [0, 31], '__ref_s3_data': None}], 'text': '$^{11}$https://docs.celeryq.dev', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [56.82627868652344, 113.28555297851562, 122.49656677246094, 121.32855224609375], 'page': 6, 'span': [0, 26], '__ref_s3_data': None}], 'text': '$^{12}$https://dramatiq.io', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [56.90766906738281, 103.01483154296875, 139.6422119140625, 111.3055419921875], 'page': 6, 'span': [0, 30], '__ref_s3_data': None}], 'text': '$^{13}$https://beam.apache.org', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [56.93403625488281, 93.08990478515625, 198.8325958251953, 101.25537109375], 'page': 6, 'span': [0, 46], '__ref_s3_data': None}], 'text': '$^{14}$https://github.com/DS4SD/project-mognet', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [56.8766975402832, 82.97198486328125, 134.6250457763672, 91.07672119140625], 'page': 6, 'span': [0, 27], '__ref_s3_data': None}], 'text': '$^{15}$https://www.amqp.org', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [56.70504379272461, 73.50115966796875, 109.47432708740234, 81.83197021484375], 'page': 6, 'span': [0, 23], '__ref_s3_data': None}], 'text': '$^{16}$https://redis.io', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [400.010986328125, 730.9371337890625, 474.3590087890625, 740.70361328125], 'page': 6, 'span': [0, 14], '__ref_s3_data': None}], 'text': 'IV. EVALUATION', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [311.1755065917969, 667.5081787109375, 563.4439086914062, 724.8348388671875], 'page': 6, 'span': [0, 299], '__ref_s3_data': None}], 'text': 'To evaluate the real-world performance and behavior of our service, we outline our benchmark environment and characterize a representative test dataset used for conversion. In the following, measurements and observations regarding the scalability and multi-user fairness are presented and discussed.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [310.6365661621094, 648.5142211914062, 355.97283935546875, 658.2706909179688], 'page': 6, 'span': [0, 10], '__ref_s3_data': None}], 'text': 'A. Metrics', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [310.79632568359375, 345.7513122558594, 563.5660400390625, 643.2157592773438], 'page': 6, 'span': [0, 1525], '__ref_s3_data': None}], 'text': 'Following from the design goals, we establish several evaluation metrics to characterize the scaling behavior. The timeto-solution (TTS) for a particular conversion job is defined as the time difference between the submission of the first batch of documents and the retrieval of the last converted document. The throughput is defined as the number of processed pages per unit of time. Notice that the number of processed documents per unit of time is not a good metric as documents have a variable number of pages. The effective throughput is computed by determining the number of processed pages from the input dataset and dividing it by the TTS. The sustained throughput is defined as the number of processed pages from the input dataset divided by the time in which all compute resources are busy processing (from t$_{0}$ until t$_{w}$, see Fig. 3). Importantly, the effective throughput will be dependent on the data volume of the conversion job, while the sustained throughput is independent of the data volume of the conversion job. In the limit of a very large conversion job, the effective throughput will asymptotically reach the sustained throughput , as the time proportion of partially idle worker state at the end of a job will be negligible compared to the time in saturated worker state. Additionally, we observe the serial time of the benchmark conversion job. It is defined as the sum of all task runtimes in each conversion job and, in the ideal case, it is constant unless a resource bottleneck is present.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.34002685546875, 326.9412536621094, 381.1842346191406, 336.3915100097656], 'page': 6, 'span': [0, 17], '__ref_s3_data': None}], 'text': 'B. Infrastructure', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [310.9816589355469, 215.5965576171875, 563.470947265625, 320.7731018066406], 'page': 6, 'span': [0, 491], '__ref_s3_data': None}], 'text': 'For our benchmark environment, we created a managed OpenShift cluster (version 4.8, using kubernetes 1.21) hosted on IBM Cloud with default settings and we added a pool of virtualized dedicated cluster nodes. Each cluster node provides 16 CPU cores and 16 GB RAM. This choice of nodes falls well within the band of optimal resources-to-cost ratio on most cloud infrastructure providers. To enable ML model serving, we installed knative serving through the OpenShift serverless operator [21].', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.03338623046875, 155.8448486328125, 563.0993041992188, 213.60015869140625], 'page': 6, 'span': [0, 284], '__ref_s3_data': None}], 'text': 'Data blobs (i.e., source documents and conversion output) and intermediate results (e.g., model predictions) are stored on managed instances of IBM Cloud Object Storage (COS) and IBM Cloud MongoDB, respectively. All resources are hosted in an IBM Cloud location in Frankfurt, Germany.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.5181884765625, 136.80130004882812, 376.6218566894531, 146.42706298828125], 'page': 6, 'span': [0, 15], '__ref_s3_data': None}], 'text': 'C. Test Dataset', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [310.8953857421875, 72.79766845703125, 563.3804321289062, 130.986572265625], 'page': 6, 'span': [0, 291], '__ref_s3_data': None}], 'text': 'To obtain a realistic test dataset, we compiled a subset of 8053 PDF documents from arXiv (consecutive submissions 2106.00001 to 2106.08072, omitting those that do not provide a PDF). It contains 156 529 pages and measures 20.7 GB in binary size. We picked this dataset as it is available at', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.505126953125, 695.0720825195312, 300.1548767089844, 741.4332275390625], 'page': 7, 'span': [0, 252], '__ref_s3_data': None}], 'text': 'minimal cost 17 and fully reproducible. Furthermore, the arXiv is a representative dataset, as it is one of the main open-source libraries for disseminating scientific preprint articles. Detailed characteristics of the test dataset are shown in Fig. 4.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [59.584068298339844, 552.5806884765625, 60.722347259521484, 577.9385375976562], 'page': 7, 'span': [0, 1], '__ref_s3_data': None}], 'text': '#', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [102.52969360351562, 410.676513671875, 243.02830505371094, 418.9044494628906], 'page': 7, 'span': [0, 44], '__ref_s3_data': None}], 'text': 'Fig. 4. Characteristics of the test dataset.', 'type': 'caption', 'name': 'Caption', 'font': None}, {'name': 'Picture', 'type': 'figure', '$ref': '#/figures/3'}, {'prov': [{'bbox': [48.54422378540039, 291.52410888671875, 300.34613037109375, 396.6930847167969], 'page': 7, 'span': [0, 519], '__ref_s3_data': None}], 'text': 'It is worth noting that we did not cherry-pick the documents in the dataset in any way to best reflect a real-world situation. We have observed that a fraction of documents reproducibly fail to convert or cause outliers in task runtimes. Three documents consistently fail to convert because of PDF interpretation issues. In another 160 documents, a total of 504 pages could not be converted due to memory constraints or rendering problems, which is a typical fraction that we also observed with other types of datasets.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [47.948448181152344, 267.3865966796875, 134.76904296875, 277.25396728515625], 'page': 7, 'span': [0, 20], '__ref_s3_data': None}], 'text': 'D. Application setup', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [48.122955322265625, 94.83416748046875, 300.5075988769531, 260.14739990234375], 'page': 7, 'span': [0, 779], '__ref_s3_data': None}], 'text': 'We evaluate two different profiles for the deployment and configuration of our system components to understand the impact of concurrent task interleaving on resource efficiency. For both profiles, we take the resource budget of a cluster node as the basis. To decide how many pod replicas of workers and ML models one can safely schedule on a cluster node without overbooking resources, we elected their average memory footprint under load as the primary criterion. Our target was to occupy 75% of the node memory, leaving 25% as overhead. This choice was made since memory, in contrast to CPU cores, cannot be oversubscribed and therefore strongly determines the required cost. Average memory footprint and CPU usage of the components, observed under load, are shown in table I.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [56.61177062988281, 73.37420654296875, 174.19342041015625, 83.37066650390625], 'page': 7, 'span': [0, 42], '__ref_s3_data': None}], 'text': '$^{17}$https://arxiv.org/help/bulk data s3', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [330.09259033203125, 713.3770751953125, 544.55517578125, 739.2872314453125], 'page': 7, 'span': [0, 98], '__ref_s3_data': None}], 'text': 'TABLE I Typical resource usage of workers and ML models under load in deployment profiles A and B.', 'type': 'caption', 'name': 'Caption', 'font': None}, {'name': 'Table', 'type': 'table', '$ref': '#/tables/0'}, {'prov': [{'bbox': [311.1868896484375, 518.7265014648438, 563.3357543945312, 600.181884765625], 'page': 7, 'span': [0, 404], '__ref_s3_data': None}], 'text': 'In profile A, we simulate a service which is not capable of interleaving concurrent tasks in the same worker process. Thus, we create equal counts of worker replicas and ML model replicas, and effectively disable worker-internal task concurrency by fetching only one task at a time from the queue. We schedule 17-18 pods (8-9 each for worker and ML model) per cluster node within the given memory budget.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.173828125, 422.8310852050781, 563.3305053710938, 516.508056640625], 'page': 7, 'span': [0, 471], '__ref_s3_data': None}], 'text': 'In profile B, we intend to optimize the conversion throughput. With the prior knowledge that ML model inference is the computationally most expensive operation in the pipeline, we create worker replicas and ML model replicas at a ratio of 1:4, and allow four concurrent tasks per worker to interleave. This allows to fit 15 pods (3 workers, 12 ML models) per cluster node, since now workers require more memory on average to interleave four tasks at a time (see table I).', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.1134948730469, 361.9398193359375, 563.2164306640625, 420.5632019042969], 'page': 7, 'span': [0, 291], '__ref_s3_data': None}], 'text': \"Further, we do not enforce limits on CPU usage per worker or ML model replica through kubernetes. Worker processes are naturally bounded to a single core due to python's global interpreter lock [24]. Each replica of the ML models is configured to use only one thread to avoid CPU congestion.\", 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.0853576660156, 266.850341796875, 563.2699584960938, 360.21392822265625], 'page': 7, 'span': [0, 465], '__ref_s3_data': None}], 'text': 'As for the ML models, we deliberately set up a rather expensive object-detection network (Faster R-CNN with ResNet50 backbone, GLYPH 63M weights) [25] for layout segmentation, which we feed PDF pages rasterized to 1025 × 1025 pixels. Bare inference time using one CPU core is in the order of 3 seconds. The structure of identified tables is reconstructed by a secondary model, here using a very cheap topological algorithm with negligible resource demand.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.39276123046875, 245.30401611328125, 368.1570739746094, 254.8785400390625], 'page': 7, 'span': [0, 14], '__ref_s3_data': None}], 'text': 'E. Scalability', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [311.0267333984375, 181.4732666015625, 563.1416625976562, 238.85894775390625], 'page': 7, 'span': [0, 255], '__ref_s3_data': None}], 'text': 'To evaluate the scaling behavior of the service, we measure the runtime and throughput of exactly one conversion job at a time, repeated with increasing node pool sizes and matching application deployment scale on the benchmark environment outlined above.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [310.9446716308594, 73.07958984375, 563.3133544921875, 178.65673828125], 'page': 7, 'span': [0, 486], '__ref_s3_data': None}], 'text': 'Benchmark series and protocol: All evaluation metrics defined above are measured repeatedly in both deployment profiles after scaling the pool size to 12, 48, 96, and 192 cluster nodes (using 192, 768, 1536, and 3072 CPU cores). The scaling level of 12 cluster nodes acts as the baseline measurement to determine the speedup for the higher scales. On a second dimension, the whole test series is carried out separately for both task distribution schemes (document-level and page-level).', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'name': 'Picture', 'type': 'figure', '$ref': '#/figures/4'}, {'name': 'Picture', 'type': 'figure', '$ref': '#/figures/5'}, {'prov': [{'bbox': [48.3709602355957, 575.2971801757812, 563.189453125, 593.0181274414062], 'page': 8, 'span': [0, 261], '__ref_s3_data': None}], 'text': 'Fig. 5. Scaling of time-to-solution (A), effective throughput and sustained throughput on document-level distribution scheme (B), page-level distribution scheme (C) in both deployment profiles with node count. Gray diagonals indicate theoretical linear scaling.', 'type': 'caption', 'name': 'Caption', 'font': None}, {'name': 'Picture', 'type': 'figure', '$ref': '#/figures/6'}, {'prov': [{'bbox': [48.08693313598633, 256.1873474121094, 300.58367919921875, 552.7544555664062], 'page': 8, 'span': [0, 1457], '__ref_s3_data': None}], 'text': 'Observations in scaling behavior: Several observations can be made in the context of our scaling benchmark series, as shown in Fig. 5. First of all, we find that the TTS at the lowest scale (12 nodes) is very similar across both deployment profiles and task distribution schemes. As we scale up the number of cluster nodes, we first recognize that both distribution schemes achieve reasonable speedup in TTS. However, the TTS in the document-level distribution scheme beats the TTS in page-level distribution scheme by a growing margin at increasing scales. This observation also mirrors in the effective throughput. At the largest scale (192 nodes), the achieved speedup in TTS starts to tail off to different degrees in both schemes, irrespective of the deployment profile. Our test dataset converts within 17 minutes in the best case (document-level scheme, profile B) and 25 min in the worst case (page-level scheme, profile A). Contrary to our theoretical considerations, the loss in scaling efficiency affects the pagelevel scheme much stronger than the document-level scheme. Deeper investigation revealed that two different causes are responsible for the observed loss in scaling efficiency. In the document-level distribution scheme, long documents increasingly dominate the TTS of the full conversion job, some of which take over 14 minutes to convert alone (see Fig 6). Thus, TTS would be lower-bounded at 14 minutes even with infinite resources.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.17839431762695, 72.85797119140625, 300.47991943359375, 250.44354248046875], 'page': 8, 'span': [0, 907], '__ref_s3_data': None}], 'text': \"In the page-level distribution scheme, despite the finegrained workload balancing achieved through equal-sized batches of pages, we find that the amount of I/O created by storing and loading intermediate task results creates a scaling bottleneck. Our MongoDB instance cannot keep up with the concurrent transaction load. This bottleneck is completely absent in the document-level distribution scheme since it's implementation creates no database transactions at all. Further, interleaving tasks on the same worker (profile B) does not demonstrate significant benefit to TTS or effective throughput over the non-interleaving setup (profile A) in either of the distribution schemes. A different picture shows when looking at the sustained throughput. In the document-level distribution scheme, we are observing even slightly better-than-linear scaling, which is clearly distinct from the effective throughput.\", 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.075439453125, 387.6934509277344, 563.6383666992188, 552.6748657226562], 'page': 8, 'span': [0, 853], '__ref_s3_data': None}], 'text': 'At first glance, this artifact may seem questionable. However, deeper verification shows two contributing factors to this. The first is owed to small imperfections in the load-balancing of requests to our ML model replicas. The fewer ML model replicas exist, the more pronounced is the impact of an unbalanced distribution of requests to those. Relatively speaking, a larger fraction of requests will be subject to additional waittimes for the response. The second factor is a peculiarity of our worker implementation. To regularly clean up the excess memory allocated (but not used) in the python runtime, worker pods reboot after every 64 completed (document-level) tasks. This causes a delay of approximately 10 seconds each time. In each conversion job, such reboots occur more frequently when fewer workers need to consume more tasks. Therefore, we', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.0782470703125, 73.94544982910156, 563.5233154296875, 126.7979736328125], 'page': 8, 'span': [0, 381], '__ref_s3_data': None}], 'text': 'Fig. 6. Active tasks over job runtime (top) and time schedule in 70 of 834 workers (bottom), using document-level scheme, profile A on 96 nodes. Each red bar marks the runtime of one task, equal to one document. The job completes only 14 minutes after the first worker finds no more work due to few, long documents in the tail. An anomaly around 7 min is caused by worker restarts.', 'type': 'caption', 'name': 'Caption', 'font': None}, {'name': 'Picture', 'type': 'figure', '$ref': '#/figures/7'}, {'prov': [{'bbox': [48.542991638183594, 718.9821166992188, 300.4215087890625, 740.6807250976562], 'page': 9, 'span': [0, 69], '__ref_s3_data': None}], 'text': 'see sustained throughput improve overproportionally at higher scales.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.19097900390625, 551.491943359375, 300.4952697753906, 717.0626831054688], 'page': 9, 'span': [0, 812], '__ref_s3_data': None}], 'text': 'The significant difference between sustained and effective throughput in the document-level distribution scheme is clearly explained through the workload imbalance between workers, as shown in figure 6. Moreover, we find that deployment profile B, which exploits in-worker task concurrency, achieves consistently higher sustained throughput than profile A with the same total amount of resources. At the highest scale of 192 nodes, sustained throughput of 296 pages/second is achieved in profile B, compared to 220 pages/second in profile A. In the page-level distribution scheme, effective and sustained throughput remain much closer together and follow similar curves across scale levels. While this indicates better workload balancing, both metrics are equally impacted by the I/O bottleneck described before.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.219970703125, 360.1738586425781, 300.2220458984375, 376.9077453613281], 'page': 9, 'span': [0, 107], '__ref_s3_data': None}], 'text': 'Fig. 7. Measured serial times of operations in the document-le v el pipeline, profile A, across all scales.', 'type': 'caption', 'name': 'Caption', 'font': None}, {'name': 'Picture', 'type': 'figure', '$ref': '#/figures/8'}, {'prov': [{'bbox': [48.07781219482422, 204.2110595703125, 300.3043212890625, 345.63861083984375], 'page': 9, 'span': [0, 706], '__ref_s3_data': None}], 'text': 'An important conte xt for the interpretation of the observ ations abov e is pro vided by the serial time, dif ferentiated by processing stage (see figure 7). W e see consistent serial times on every tested scaling le v el in the document pipeline, which confirms that no resource congestion or bandwidth limitation is present. In the page-le v el pipeline (not sho wn), serial times, in particular for upload and mer ge operations, increase noticeably on the lar gest scale. Finally , we clearly recognize that applying ML models is the most time consuming operation, responsible for approximately 76% (page-le v el scheme) to 84% (document-le v el scheme) of the task runtimes in our test conversion job .', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.22081756591797, 184.77928161621094, 95.30005645751953, 193.93328857421875], 'page': 9, 'span': [0, 11], '__ref_s3_data': None}], 'text': 'F. Fairness', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [47.95774459838867, 73.27685546875, 300.39031982421875, 178.79351806640625], 'page': 9, 'span': [0, 536], '__ref_s3_data': None}], 'text': 'To understand both ho w f ast and ho w f air our service handles multiple concurrent con v ersion jobs, we analyze an extreme case. While saturating our service with the con version of our benchmark dataset, we submit a second con version job with a single a v erage-length document (15 pages) one, two, and three minutes later . W e measure the TTS of the single-document job repeatedly and compare it to the TTS of the same job submitted on an idle sys tem. This e xperiment is carried out at a scale of 48 nodes and repeated for both', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [325.976318359375, 713.4354858398438, 548.9337768554688, 730.4822387695312], 'page': 9, 'span': [0, 113], '__ref_s3_data': None}], 'text': 'TTS for a 15-page document in idle and busy system states. Values represent mean ± standard deviation in seconds.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [420.9889831542969, 731.3684692382812, 454.0250244140625, 738.4937744140625], 'page': 9, 'span': [0, 8], '__ref_s3_data': None}], 'text': 'TABLE II', 'type': 'caption', 'name': 'Caption', 'font': None}, {'name': 'Table', 'type': 'table', '$ref': '#/tables/1'}, {'prov': [{'bbox': [311.104248046875, 573.2300415039062, 563.035400390625, 594.8715209960938], 'page': 9, 'span': [0, 81], '__ref_s3_data': None}], 'text': 'deployment profiles and task distribution schemes. Results are shown in table II.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.1533508300781, 496.09405517578125, 563.3656616210938, 565.8314208984375], 'page': 9, 'span': [0, 295], '__ref_s3_data': None}], 'text': 'The single document converts with a reproducible TTS of 33s (document-level scheme) or 26s (page-level scheme). On a busy system, this increases by a factor of 3.5 (documentlevel scheme) or 7.5 (page-level scheme) on average, with a substantial standard deviation between 30% and 45% of the TTS.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.07275390625, 371.1371765136719, 563.5538330078125, 488.30364990234375], 'page': 9, 'span': [0, 609], '__ref_s3_data': None}], 'text': 'One can draw multiple conclusions from these results. First, we see good evidence that our strategy to keep the length of the task queue short (see section III-B) pays off. Concurrent conversion workloads of very different volume are processed in a reasonably fair manner, all while avoiding the complexity of job priorities or separate queues. Still, the TTS on a busy system is significantly higher in the page-level distribution scheme, since the single-document conversion job produces a total of 12 tasks, which pass through the task queue, compared to only one in the document-level distribution scheme.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.04754638671875, 138.58425903320312, 563.3732299804688, 363.76458740234375], 'page': 9, 'span': [0, 1077], '__ref_s3_data': None}], 'text': 'Second, we find that the page-level distribution scheme on this 15-page document yields 20% lower TTS (1.25x speedup) than the document-level scheme in an idle system. Disregarding all non-parallelizable operations (upload, merge, and export), the theoretical speedup would be bounded at 4x for 15 pages, because we chose to configure our system to create tasks for batches of up to four pages. Dynamically deciding the optimum page-batch size based on the page count could improve this aspect. In reality, we must consider two factors: On one hand, profiling the page-level distribution scheme reveals that approximately one quarter of the total conversion job run-time is spent in the non-parallelizable operations for this document. On the other hand, the pagelevel distribution pays a high price on additional I/O, as shown earlier. In the observed case, it requires 12 distinct store and retrieve operations, while the document-level scheme requires only two. As such, TTS in ad-hoc conversion can profit from the page-level distribution scheme only with longer documents.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [311.07318115234375, 73.40324401855469, 563.1641845703125, 130.89666748046875], 'page': 9, 'span': [0, 244], '__ref_s3_data': None}], 'text': 'Third, we find that the deployment profile (A or B) has no relevant impact on the measurements above. Processing 15 pages only utilizes a fraction of the available worker and ML model replicas here, which explains why no difference is observed.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [135.48716735839844, 730.9381103515625, 212.7260284423828, 740.87451171875], 'page': 10, 'span': [0, 14], '__ref_s3_data': None}], 'text': 'V. CONCLUSIONS', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [48.13857650756836, 618.2142333984375, 300.3543701171875, 723.6151733398438], 'page': 10, 'span': [0, 497], '__ref_s3_data': None}], 'text': 'We have carefully established a scalable cloud service design for the task of document conversion and presented the impact of different workload distribution schemes and implementation options on both absolute speed and scaling behavior. With the best-performing setup, we achieve a sustained conversion throughput scaling linearly with the resource budget up to 192 nodes (equal to 3072 CPU cores and 3072 GB of memory). In practice, this allows us to convert over one million PDF pages per hour.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.06001281738281, 498.42889404296875, 300.3832702636719, 615.6994018554688], 'page': 10, 'span': [0, 564], '__ref_s3_data': None}], 'text': 'An important lesson we learned is that distributing the workload more evenly at a page-level cannot compensate for the excess of I/O expenses and synchronization need it creates over document-level distribution. In terms of sustainable throughput, it ultimately steers into a scaling bottleneck. More efficient intermediate data storage and locality may push this bottleneck further up the scale, but it will eventually prevail. We find good potential in using page-level distribution, however, to achieve short response time in ad-hoc conversion of few documents.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.03425979614258, 330.3664855957031, 300.15289306640625, 495.8798522949219], 'page': 10, 'span': [0, 817], '__ref_s3_data': None}], 'text': 'To drive a production-grade deployment of our conversion service, we need to consider some important differences to the benchmark setup presented here. For cost reasons, it is highly advisable to rely on automatic scaling mechanisms. The ML model microservices can be easily transferred to a managed platform-as-a-service where the replicas can be dynamically scaled according to the request load without preallocating cluster infrastructure (e.g., Google App Engine$^{18}$, IBM Cloud Code Engine$^{19}$, or Amazon ECS$^{20}$). Additionally, worker pods and even cluster nodes may be automatically added and removed based on the length of the task queue. In deployments for open audiences, we enforce strict timeouts on tasks to ensure that very heavy or even poisonous PDF files are not locking up resources forever.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [47.99211883544922, 139.13909912109375, 300.3812255859375, 328.5460510253906], 'page': 10, 'span': [0, 923], '__ref_s3_data': None}], 'text': 'The investment of implementing all pipeline logic in asynchronous co-routines and developing a task distribution library which works natively within an event-loop has proven to be worthwhile. Distributing and executing code with dynamic subtasks and dependencies becomes very cheap and it helps to achieve fairness in multi-tenant usage with little effort and complexity. Additionally, it allows to write idiomatic code. It should be noted however that the efficiency advantage of interleaving tasks as proposed here depends significantly both on the amount of opportunity to avoid blocking I/O and on the proportion of compute-bound code executing in the workers. Externalizing the ML models as web-services has proven to be a sensible choice in this regard. Considering the large fraction of total compute-time spent in ML inference, investing effort into cheaper ML models will remain a top priority in our ongoing work.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [48.339088439941406, 114.763427734375, 300.40673828125, 136.94744873046875], 'page': 10, 'span': [0, 119], '__ref_s3_data': None}], 'text': 'In the landscape of data-driven business, several applications share important properties with document conversion. One', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [56.891944885253906, 92.89056396484375, 177.87632751464844, 102.00689697265625], 'page': 10, 'span': [0, 41], '__ref_s3_data': None}], 'text': '$^{18}$https://cloud.google.com/appengine', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [56.821067810058594, 83.01531982421875, 193.50592041015625, 91.6480712890625], 'page': 10, 'span': [0, 44], '__ref_s3_data': None}], 'text': '$^{19}$https://www.ibm.com/cloud/code-engine', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [56.264488220214844, 73.61920166015625, 152.8022003173828, 81.8521728515625], 'page': 10, 'span': [0, 33], '__ref_s3_data': None}], 'text': '$^{20}$https://aws.amazon.com/ecs', 'type': 'footnote', 'name': 'Footnote', 'font': None}, {'prov': [{'bbox': [310.8520812988281, 599.430908203125, 563.4625854492188, 740.8935546875], 'page': 10, 'span': [0, 671], '__ref_s3_data': None}], 'text': 'such case is automated knowledge-base construction, where several data processing pipelines have been developed for extraction, normalization, and analysis of knowledge [26], often targeting specific domains like material sciences, geology, or biomedicine [27]-[31]. They commonly process complex or unstructured input data in bulk (e.g., literature, experiment reports, and instrument data), employ machine-learning (e.g., language understanding), and often require complex dynamic pipelines. As such, the insights presented and conclusions drawn in this paper would be of value for the design and validation of future work on scalable processing pipelines in the cloud.', 'type': 'paragraph', 'name': 'Text', 'font': None}, {'prov': [{'bbox': [409.0566101074219, 577.389892578125, 465.4123840332031, 586.37548828125], 'page': 10, 'span': [0, 10], '__ref_s3_data': None}], 'text': 'REFERENCES', 'type': 'subtitle-level-1', 'name': 'Section-header', 'font': None}, {'prov': [{'bbox': [315.4861145019531, 515.8212890625, 563.2164306640625, 569.1727905273438], 'page': 10, 'span': [0, 364], '__ref_s3_data': None}], 'text': '- [1] C. Gopal, C. L. Marshall, D. Vesset, N. Ward-Dutton, J. Hamel, R. Jyoti, P. Rutten, C. W. Olofson, J. Rydning, S. Rau, and J. 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Detectron2 model zoo and baselines. [Online]. Available: https://github.com/facebookresearch/detectron2/ blob/main/MODEL ZOO.md', 'type': 'paragraph', 'name': 'List-item', 'font': None}, {'prov': [{'bbox': [48.410465240478516, 390.0207824707031, 300.456298828125, 416.18792724609375], 'page': 11, 'span': [0, 196], '__ref_s3_data': None}], 'text': '- [26] P. W. Staar, M. Dolfi, and C. Auer, \"Corpus processing service: A knowledge graph platform to perform deep data exploration on corpora,\" Applied AI Letters , vol. 1, no. 2, p. e20, Dec. 2020.', 'type': 'paragraph', 'name': 'List-item', 'font': None}, {'prov': [{'bbox': [48.504295349121094, 345.8144226074219, 300.5445556640625, 389.45355224609375], 'page': 11, 'span': [0, 314], '__ref_s3_data': None}], 'text': '- [27] T. J. Jacobsson, A. Hultqvist, A. Garc\\'ıa-Fern\\'andez, A. Anand, A. AlAshouri, A. Hagfeldt, A. Crovetto, A. Abate, A. G. Ricciardulli, A. Vijayan et al. , \"An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles,\" Nature Energy , vol. 7, no. 1, pp. 107-115, 2022.', 'type': 'paragraph', 'name': 'List-item', 'font': None}, {'prov': [{'bbox': [48.52201461791992, 309.53143310546875, 300.3795166015625, 344.7828063964844], 'page': 11, 'span': [0, 298], '__ref_s3_data': None}], 'text': '- [28] E. O. Pyzer-Knapp, J. W. Pitera, P. W. Staar, S. Takeda, T. Laino, D. P. Sanders, J. Sexton, J. R. Smith, and A. Curioni, \"Accelerating materials discovery using artificial intelligence, high performance computing and robotics,\" npj Computational Materials , vol. 8, no. 1, pp. 1-9, Apr. 2022.', 'type': 'paragraph', 'name': 'List-item', 'font': None}, {'prov': [{'bbox': [48.547607421875, 274.017822265625, 300.5572814941406, 308.9217224121094], 'page': 11, 'span': [0, 261], '__ref_s3_data': None}], 'text': '- [29] M. Piantanida, E. Bonamini, C. Caborni, F. Bergero, P. Staar, M. Dolfi, C. Auer, and V. Saturnino, \"Using knowledge graphs to navigate through geological concepts extracted from documents,\" in OMC Med Energy Conference and Exhibition . OnePetro, Sep. 2021.', 'type': 'paragraph', 'name': 'List-item', 'font': None}, {'prov': [{'bbox': [48.598052978515625, 238.21734619140625, 300.70361328125, 273.0455322265625], 'page': 11, 'span': [0, 231], '__ref_s3_data': None}], 'text': '- [30] V. D. Badal, D. Wright, Y. Katsis, H.-C. Kim, A. D. Swafford, R. Knight, and C.-N. Hsu, \"Challenges in the construction of knowledge bases for human microbiome-disease associations,\" Microbiome , vol. 7, no. 1, pp. 1-15, 2019.', 'type': 'paragraph', 'name': 'List-item', 'font': None}, {'prov': [{'bbox': [48.57107162475586, 193.02166748046875, 300.49127197265625, 237.1055908203125], 'page': 11, 'span': [0, 321], '__ref_s3_data': None}], 'text': '- [31] M. Manica, C. Auer, V. Weber, F. Zipoli, M. Dolfi, P. W. J. Staar, T. Laino, C. Bekas, A. Fujita, H. Toda, S. Hirose, and Y. Orii, \"An information extraction and knowledge graph platform for accelerating biochemical discoveries,\" CoRR , vol. abs/1907.08400, 2019. [Online]. Available: http://arxiv.org/abs/1907.08400', 'type': 'paragraph', 'name': 'List-item', 'font': None}], 'figures': [{'prov': [{'bbox': [50.88604736328125, 558.77099609375, 561.7911376953125, 742.2131958007812], 'page': 2, 'span': [0, 78], '__ref_s3_data': None}], 'text': 'Fig. 1. Sketch of operation dependency graph in a document conversion pipeline', 'type': 'figure', 'bounding-box': None}, {'prov': [{'bbox': [48.31047821044922, 502.1199951171875, 299.5975341796875, 643.0848388671875], 'page': 4, 'span': [0, 510], '__ref_s3_data': None}], 'text': 'Fig. 2. Architecture diagram of our conversion service. User requests for document conversion are handled by a REST API service, which can dispatch workloads asynchronously to the compute infrastructure through a queueing mechanism. Workers pick up queued tasks from a message broker and store the results for later retrieval on a document database and cloud object storage. The ML models are served through separate microservices, which are consumed by workers when executing the document conversion pipeline.', 'type': 'figure', 'bounding-box': None}, {'prov': [{'bbox': [47.52158737182617, 404.80792236328125, 300.072998046875, 710.0579223632812], 'page': 5, 'span': [0, 395], '__ref_s3_data': None}], 'text': 'Fig. 3. Sketch of workload distribution schemes on the document-level (top) and page-level (bottom). A conversion job is submitted at time t$_{0}$. Tasks are produced and consumed by the workers through the task queue. The queue drains at time t$_{d}$. Sustained throughput is upheld until the first worker finds no more work (t$_{w}$). The job only finishes after completion of all tasks (TTS).', 'type': 'figure', 'bounding-box': None}, {'prov': [{'bbox': [61.860626220703125, 431.9028015136719, 288.02362060546875, 679.3151245117188], 'page': 7, 'span': [0, 44], '__ref_s3_data': None}], 'text': 'Fig. 4. Characteristics of the test dataset.', 'type': 'figure', 'bounding-box': None}, {'prov': [{'bbox': [48.7349739074707, 604.4910278320312, 214.92897033691406, 741.4573974609375], 'page': 8, 'span': [0, 0], '__ref_s3_data': None}], 'text': '', 'type': 'figure', 'bounding-box': None}, {'prov': [{'bbox': [220.35333251953125, 604.5574951171875, 384.72698974609375, 741.0494384765625], 'page': 8, 'span': [0, 0], '__ref_s3_data': None}], 'text': '', 'type': 'figure', 'bounding-box': None}, {'prov': [{'bbox': [391.38787841796875, 604.5574951171875, 556.16259765625, 740.4262084960938], 'page': 8, 'span': [0, 261], '__ref_s3_data': None}], 'text': 'Fig. 5. Scaling of time-to-solution (A), effective throughput and sustained throughput on document-level distribution scheme (B), page-level distribution scheme (C) in both deployment profiles with node count. Gray diagonals indicate theoretical linear scaling.', 'type': 'figure', 'bounding-box': None}, {'prov': [{'bbox': [338.3061218261719, 138.53965759277344, 535.2787475585938, 364.2861328125], 'page': 8, 'span': [0, 381], '__ref_s3_data': None}], 'text': 'Fig. 6. Active tasks over job runtime (top) and time schedule in 70 of 834 workers (bottom), using document-level scheme, profile A on 96 nodes. Each red bar marks the runtime of one task, equal to one document. The job completes only 14 minutes after the first worker finds no more work due to few, long documents in the tail. An anomaly around 7 min is caused by worker restarts.', 'type': 'figure', 'bounding-box': None}, {'prov': [{'bbox': [62.99921798706055, 389.0814514160156, 281.9828796386719, 538.5746459960938], 'page': 9, 'span': [0, 107], '__ref_s3_data': None}], 'text': 'Fig. 7. Measured serial times of operations in the document-le v el pipeline, profile A, across all scales.', 'type': 'figure', 'bounding-box': None}], 'tables': [{'prov': [{'bbox': [339.1385803222656, 624.9138793945312, 523.4959716796875, 703.262939453125], 'page': 7, 'span': [0, 0], '__ref_s3_data': None}], 'text': 'TABLE I Typical resource usage of workers and ML models under load in deployment profiles A and B.', 'type': 'table', '#-cols': 3, '#-rows': 6, 'data': [[{'bbox': None, 'spans': [[0, 0]], 'text': '', 'type': 'body', 'col': 0, 'col-header': False, 'col-span': [0, 1], 'row': 0, 'row-header': False, 'row-span': [0, 1]}, {'bbox': [461.5480041503906, 694.345458984375, 496.9671325683594, 701.4707641601562], 'spans': [[0, 1], [0, 2]], 'text': 'component', 'type': 'col_header', 'col': 1, 'col-header': True, 'col-span': [1, 3], 'row': 0, 'row-header': False, 'row-span': [0, 1]}, {'bbox': [461.5480041503906, 694.345458984375, 496.9671325683594, 701.4707641601562], 'spans': [[0, 1], [0, 2]], 'text': 'component', 'type': 'col_header', 'col': 2, 'col-header': True, 'col-span': [1, 3], 'row': 0, 'row-header': False, 'row-span': [0, 1]}], [{'bbox': None, 'spans': [[1, 0]], 'text': '', 'type': 'body', 'col': 0, 'col-header': False, 'col-span': [0, 1], 'row': 1, 'row-header': False, 'row-span': [1, 2]}, {'bbox': [441.1610107421875, 684.482421875, 463.57293701171875, 691.6077270507812], 'spans': [[1, 1]], 'text': 'worker', 'type': 'col_header', 'col': 1, 'col-header': True, 'col-span': [1, 2], 'row': 1, 'row-header': False, 'row-span': [1, 2]}, {'bbox': [482.68524169921875, 684.482421875, 517.3551635742188, 691.6077270507812], 'spans': [[1, 2]], 'text': 'ML model', 'type': 'col_header', 'col': 2, 'col-header': True, 'col-span': [2, 3], 'row': 1, 'row-header': False, 'row-span': [1, 2]}], [{'bbox': [387.4590148925781, 664.7554931640625, 414.9080505371094, 681.7437133789062], 'spans': [[2, 0]], 'text': 'Memory CPU', 'type': 'row_header', 'col': 0, 'col-header': False, 'col-span': [0, 1], 'row': 2, 'row-header': True, 'row-span': [2, 3]}, {'bbox': [441.1610107421875, 664.7554321289062, 470.7300720214844, 681.7437744140625], 'spans': [[2, 1]], 'text': '450 MB 0.4 cores', 'type': 'body', 'col': 1, 'col-header': False, 'col-span': [1, 2], 'row': 2, 'row-header': False, 'row-span': [2, 3]}, {'bbox': [482.6852111816406, 664.7554321289062, 512.2542724609375, 681.7437744140625], 'spans': [[2, 2]], 'text': '500 MB 0.7 cores', 'type': 'body', 'col': 2, 'col-header': False, 'col-span': [2, 3], 'row': 2, 'row-header': False, 'row-span': [2, 3]}], [{'bbox': [387.4590148925781, 654.8924560546875, 429.2063903808594, 662.0177612304688], 'spans': [[3, 0]], 'text': 'Replica ratio', 'type': 'row_header', 'col': 0, 'col-header': False, 'col-span': [0, 1], 'row': 3, 'row-header': True, 'row-span': [3, 4]}, {'bbox': [441.1610107421875, 654.8924560546875, 445.14605712890625, 662.0177612304688], 'spans': [[3, 1]], 'text': '1', 'type': 'body', 'col': 1, 'col-header': False, 'col-span': [1, 2], 'row': 3, 'row-header': False, 'row-span': [3, 4]}, {'bbox': [482.68524169921875, 654.8924560546875, 486.6702880859375, 662.0177612304688], 'spans': [[3, 2]], 'text': '1', 'type': 'body', 'col': 2, 'col-header': False, 'col-span': [2, 3], 'row': 3, 'row-header': False, 'row-span': [3, 4]}], [{'bbox': [387.4590148925781, 635.1664428710938, 414.9080505371094, 652.1547241210938], 'spans': [[4, 0]], 'text': 'Memory CPU', 'type': 'row_header', 'col': 0, 'col-header': False, 'col-span': [0, 1], 'row': 4, 'row-header': True, 'row-span': [4, 5]}, {'bbox': [441.1610107421875, 635.1664428710938, 470.7300720214844, 652.1547241210938], 'spans': [[4, 1]], 'text': '700 MB 1.2 cores', 'type': 'body', 'col': 1, 'col-header': False, 'col-span': [1, 2], 'row': 4, 'row-header': False, 'row-span': [4, 5]}, {'bbox': [482.6852111816406, 635.1664428710938, 512.2542724609375, 652.1547241210938], 'spans': [[4, 2]], 'text': '500 MB 0.7 cores', 'type': 'body', 'col': 2, 'col-header': False, 'col-span': [2, 3], 'row': 4, 'row-header': False, 'row-span': [4, 5]}], [{'bbox': [387.4590148925781, 625.303466796875, 429.2063903808594, 632.4287719726562], 'spans': [[5, 0]], 'text': 'Replica ratio', 'type': 'row_header', 'col': 0, 'col-header': False, 'col-span': [0, 1], 'row': 5, 'row-header': True, 'row-span': [5, 6]}, {'bbox': [441.1610107421875, 625.303466796875, 445.14605712890625, 632.4287719726562], 'spans': [[5, 1]], 'text': '1', 'type': 'body', 'col': 1, 'col-header': False, 'col-span': [1, 2], 'row': 5, 'row-header': False, 'row-span': [5, 6]}, {'bbox': [482.68524169921875, 625.303466796875, 486.6702880859375, 632.4287719726562], 'spans': [[5, 2]], 'text': '4', 'type': 'body', 'col': 2, 'col-header': False, 'col-span': [2, 3], 'row': 5, 'row-header': False, 'row-span': [5, 6]}]], 'model': None, 'bounding-box': None}, {'prov': [{'bbox': [337.73248291015625, 632.7647094726562, 537.3443603515625, 700.9937744140625], 'page': 9, 'span': [0, 0], '__ref_s3_data': None}], 'text': 'TABLE II', 'type': 'table', '#-cols': 4, '#-rows': 5, 'data': [[{'bbox': None, 'spans': [[0, 0]], 'text': '', 'type': 'body', 'col': 0, 'col-header': False, 'col-span': [0, 1], 'row': 0, 'row-header': False, 'row-span': [0, 1]}, {'bbox': None, 'spans': [[0, 1]], 'text': '', 'type': 'body', 'col': 1, 'col-header': False, 'col-span': [1, 2], 'row': 0, 'row-header': False, 'row-span': [0, 1]}, {'bbox': [428.04998779296875, 681.4334716796875, 511.2825622558594, 700.2147827148438], 'spans': [[0, 2]], 'text': 'distribution scheme document-level', 'type': 'col_header', 'col': 2, 'col-header': True, 'col-span': [2, 3], 'row': 0, 'row-header': False, 'row-span': [0, 1]}, {'bbox': [489.26800537109375, 681.4334716796875, 522.1445922851562, 688.5587768554688], 'spans': [[0, 3]], 'text': 'page-level', 'type': 'col_header', 'col': 3, 'col-header': True, 'col-span': [3, 4], 'row': 0, 'row-header': False, 'row-span': [0, 1]}], [{'bbox': [343.7909851074219, 681.4334716796875, 364.6009216308594, 688.5587768554688], 'spans': [[1, 0]], 'text': 'profile', 'type': 'body', 'col': 0, 'col-header': False, 'col-span': [0, 1], 'row': 1, 'row-header': False, 'row-span': [1, 2]}, {'bbox': [376.5559997558594, 669.37841796875, 416.09564208984375, 688.5587768554688], 'spans': [[1, 1]], 'text': 'system state idle', 'type': 'body', 'col': 1, 'col-header': False, 'col-span': [1, 2], 'row': 1, 'row-header': False, 'row-span': [1, 2]}, {'bbox': [428.04998779296875, 669.37841796875, 462.0344543457031, 676.5037231445312], 'spans': [[1, 2]], 'text': '32.6 ± 1.1', 'type': 'body', 'col': 2, 'col-header': False, 'col-span': [2, 3], 'row': 1, 'row-header': False, 'row-span': [1, 2]}, {'bbox': [489.26800537109375, 669.37841796875, 523.2525024414062, 676.5037231445312], 'spans': [[1, 3]], 'text': '26.3 ± 0.9', 'type': 'body', 'col': 3, 'col-header': False, 'col-span': [3, 4], 'row': 1, 'row-header': False, 'row-span': [1, 2]}], [{'bbox': [351.3190002441406, 664.0884399414062, 357.07342529296875, 671.2137451171875], 'spans': [[2, 0]], 'text': 'A', 'type': 'row_header', 'col': 0, 'col-header': False, 'col-span': [0, 1], 'row': 2, 'row-header': True, 'row-span': [2, 3]}, {'bbox': [376.5559997558594, 657.722412109375, 391.4521179199219, 664.8477172851562], 'spans': [[2, 1]], 'text': 'busy', 'type': 'body', 'col': 1, 'col-header': False, 'col-span': [1, 2], 'row': 2, 'row-header': False, 'row-span': [2, 3]}, {'bbox': [428.04998779296875, 657.722412109375, 470.00457763671875, 664.8477172851562], 'spans': [[2, 2]], 'text': '114.9 ± 17.5', 'type': 'body', 'col': 2, 'col-header': False, 'col-span': [2, 3], 'row': 2, 'row-header': False, 'row-span': [2, 3]}, {'bbox': [489.26800537109375, 657.722412109375, 531.22265625, 664.8477172851562], 'spans': [[2, 3]], 'text': '248.1 ± 17.3', 'type': 'body', 'col': 3, 'col-header': False, 'col-span': [3, 4], 'row': 2, 'row-header': False, 'row-span': [2, 3]}], [{'bbox': [351.5379943847656, 640.37744140625, 356.85406494140625, 647.5027465820312], 'spans': [[3, 0], [4, 0]], 'text': 'B', 'type': 'row_header', 'col': 0, 'col-header': False, 'col-span': [0, 1], 'row': 3, 'row-header': True, 'row-span': [3, 5]}, {'bbox': [376.5559997558594, 645.6674194335938, 388.5111389160156, 652.792724609375], 'spans': [[3, 1]], 'text': 'idle', 'type': 'body', 'col': 1, 'col-header': False, 'col-span': [1, 2], 'row': 3, 'row-header': False, 'row-span': [3, 4]}, {'bbox': [428.04998779296875, 645.6674194335938, 462.0344543457031, 652.792724609375], 'spans': [[3, 2]], 'text': '32.6 ± 0.2', 'type': 'body', 'col': 2, 'col-header': False, 'col-span': [2, 3], 'row': 3, 'row-header': False, 'row-span': [3, 4]}, {'bbox': [489.26800537109375, 645.6674194335938, 523.2525024414062, 652.792724609375], 'spans': [[3, 3]], 'text': '25.7 ± 0.7', 'type': 'body', 'col': 3, 'col-header': False, 'col-span': [3, 4], 'row': 3, 'row-header': False, 'row-span': [3, 4]}], [{'bbox': [351.5379943847656, 640.37744140625, 356.85406494140625, 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" ] }, "metadata": {}, @@ -560,6 +554,7 @@ "all_results = []\n", "for result_page in tqdm(cursor):\n", " for row in result_page.outputs[\"data_outputs\"]:\n", + " print(row)\n", " metadata = row[\"_source\"].get(\n", " \"description\", {}\n", " ) # setting default, in case no title and authors are detected\n", @@ -593,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 32, "id": "fee869dd-6f23-4999-b262-b034d2161deb", "metadata": { "execution": { @@ -620,25 +615,25 @@ { "data": { "text/markdown": [ - "## DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis\n", + "## Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness\n", "\n", - "Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com\n", + "1 st Christoph Auer IBM Research Ruschlikon, Switzerland cau@zurich.ibm.com\n", "\n", - "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com\n", + "4 th Cesar Berrospi Ramis IBM Research Ruschlikon, Switzerland ceb@zurich.ibm.com\n", "\n", - "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com\n", + "2 nd Michele Dolfi IBM Research Ruschlikon, Switzerland dol@zurich.ibm.com\n", "\n", - "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com\n", + "5 th Peter W.J. Staar IBM Research Ruschlikon, Switzerland taa@zurich.ibm.com\n", "\n", - "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com\n", + "Abstract -Document understanding is a key business process in the data-driven economy since documents are central to knowledge discovery and business insights. Converting documents into a machine-processable format is a particular challenge here due to their huge variability in formats and complex structure. Accordingly, many algorithms and machine-learning methods emerged to solve particular tasks such as Optical Character Recognition (OCR), layout analysis, table-structure recovery, figure understanding, etc. We observe the adoption of such methods in document understanding solutions offered by all major cloud providers. Yet, publications outlining how such services are designed and optimized to scale in the cloud are scarce. In this paper, we focus on the case of document conversion to illustrate the particular challenges of scaling a complex data processing pipeline with a strong reliance on machine-learning methods on cloud infrastructure. Our key objective is to achieve high scalability and responsiveness for different workload profiles in a well-defined resource budget. We outline the requirements, design, and implementation choices of our document conversion service and reflect on the challenges we faced. Evidence for the scaling behavior and resource efficiency is provided for two alternative workload distribution strategies and deployment configurations. Our best-performing method achieves sustained throughput of over one million PDF pages per hour on 3072 CPU cores across 192 nodes.\n", "\n", - "## ABSTRACT\n", + "Index Terms -cloud applications, document understanding, distributed computing, artificial intelligence\n", "\n", - "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.\n", + "## I. INTRODUCTION\n", "\n", - "## CCS CONCEPTS\n", + "Over the past decade, many organizations have accelerated their transformation into data-driven businesses, as studies have shown its positive impact in efficiency, decision making, or financial performance [1], [2]. Leading companies are increasingly deploying workloads on public and private cloud infrastructure, including business intelligence processing and machine learning models in data analytics platforms [3]. This is owed to several factors such as high availability, lower cost for compute, and storage [4], as well as the flexibility to scale up or down a cloud-based business process to fit the operational needs. Workloads and services can be container-\n", "\n", - "· Information systems → Document structure; · Applied computing → Document analysis; · Computing methodologies → Machine learning; Computer vision; Object detection;\n", + "ized, deployed, and orchestrated through widely adopted and standardized platforms like Kubernetes [5], [6].\n", "\n" ], "text/plain": [ @@ -664,25 +659,25 @@ "data": { "text/markdown": [ "{\n", - " \"_name\": \"2206.01062.pdf\",\n", - " \"bitmaps\": [],\n", + " \"_name\": \"\",\n", + " \"type\": \"pdf-document\",\n", " \"description\": {\n", " \"logs\": [\n", " {\n", - " \"agent\": \"CCS\",\n", + " \"agent\": \"CPS Docling\",\n", " \"type\": \"parsing\",\n", - " \"comment\": \"CCS v10.5.0 parsing of documents\",\n", - " \"date\": \"2024-05-27T10:47:36.503487+00:00\"\n", + " \"comment\": \"Docling 2.7.1 parsing of documents\",\n", + " \"date\": \"2025-01-16T13:53:04.836922+00:00\"\n", " }\n", " ],\n", " \"collection\": {\n", " \"type\": \"Document\"\n", " }\n", " },\n", - " \"equations\": [],\n", - " \"figures\": [\n", - " {\n", - " \"confidence\": 0.0,\n", + " \"file-info\": {\n", + " \"filename\": \"2206.00785.pdf\",\n", + " \"document-hash\": \"6627d1b67955c51ff1aa8858de671bb5a62ad70c77e62e0ac57c153d0078b7ea\",\n", + " \"#-pages\": 11,\n", "" ], "text/plain": [ @@ -735,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 33, "id": "58c15840-b342-44e8-b654-32b3e35f3991", "metadata": { "execution": { @@ -753,7 +748,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 34, "id": "d477507f-bcbe-45b5-9557-dd0e3109c81c", "metadata": { "execution": { @@ -773,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 35, "id": "57ac3ecb-f411-4dd2-936e-4e73825da323", "metadata": { "execution": { @@ -789,7 +784,7 @@ "data": { "text/markdown": [ "#### Results\n", - "The data is now available. This file will now display the text from the scanned pages. Access it via the Deep Search UI at
https://cps.foc-deepsearch.zurich.ibm.com//projects/e74892839ff04df16a87cae3e96248a24eec7756/library/private/a9d5732282cacf822d94086f492992542a271692" + "The data is now available. This file will now display the text from the scanned pages. Access it via the Deep Search UI at
https://pr-516-cps-dev.deepsearch-dev.zurich.ibm.com/projects/468b81ffe515a99172f93b07dd20cd50d4c19a3b/library/private/ad7097d15b2793b975c4caf0a2cbd7472336344f" ], "text/plain": [ "" @@ -801,8 +796,8 @@ ], "source": [ "# Load conversion settings and enable OCR\n", - "cs = ConversionSettings.from_project(api, proj_key=PROJ_KEY)\n", - "cs.ocr.enabled = True # Enable or disable OCR\n", + "cs = ConversionSettings()\n", + "cs.ocr.do_ocr = True # Enable or disable OCR\n", "\n", "# Upload and convert documents with custom conversion settings\n", "data_indices_utils.upload_files(\n", @@ -841,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 36, "id": "16655fea-becc-4236-9ad3-ac1e7ba13c46", "metadata": { "execution": { @@ -859,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 37, "id": "4c3c8d9a-b06c-4966-98ee-335be9b659aa", "metadata": { "execution": { @@ -879,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 38, "id": "d5060e1e-f8da-4948-8d34-56ed86f5ddb3", "metadata": { "execution": { @@ -890,23 +885,34 @@ "shell.execute_reply.started": "2024-05-27T10:48:43.224475Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['SUCCESS']" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Set custom target settings with raw pdf cells enabled\n", - "tsettings = TargetSettings(add_raw_pages=True)\n", + "tsettings = TargetSettings(add_raw_pages=True, add_annotations=False)\n", "\n", "# Upload and convert documents with custom conversion settings\n", "data_indices_utils.upload_files(\n", " api=api,\n", " coords=data_index.source,\n", - " local_file=INPUT_FILES_FOLDER,\n", + " local_file=INPUT_FILE_PATH,\n", " target_settings=tsettings,\n", ")" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 39, "id": "8d6dc87a-1501-4564-9914-f2787a85508b", "metadata": { "execution": { @@ -922,8 +928,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Finished fetching all data. Total is 31 records.\n", - "Data downloaded in /var/folders/32/5m4rnj816kl4pxl4vm97_wbh0000kp/T/tmpzb6oosaa\n" + "Finished fetching all data. Total is 0 records.\n", + "Data downloaded in /tmp/tmphf8euhqf\n" ] }, { @@ -946,198 +952,9 @@ " \n", " \n", " \n", - " Filename\n", - " Page\n", - " RAW file\n", " \n", " \n", " \n", - " \n", - " 0\n", - " 2206.01062.pdf\n", - " 1\n", - " Link\n", - " \n", - " \n", - " 1\n", - " 2206.01062.pdf\n", - " 2\n", - " Link\n", - " \n", - " \n", - " 2\n", - " 2206.01062.pdf\n", - " 3\n", - " Link\n", - " \n", - " \n", - " 3\n", - " 2206.01062.pdf\n", - " 4\n", - " Link\n", - " \n", - " \n", - " 4\n", - " 2206.01062.pdf\n", - " 5\n", - " Link\n", - " \n", - " \n", - " 5\n", - " 2206.01062.pdf\n", - " 6\n", - " Link\n", - " \n", - " \n", - " 6\n", - " 2206.01062.pdf\n", - " 7\n", - " Link\n", - " \n", - " \n", - " 7\n", - " 2206.01062.pdf\n", - " 8\n", - " Link\n", - " \n", - " \n", - " 8\n", - " 2206.01062.pdf\n", - " 9\n", - " Link\n", - " \n", - " \n", - " 9\n", - " 2206.00785.pdf\n", - " 1\n", - " Link\n", - " \n", - " \n", - " 10\n", - " 2206.00785.pdf\n", - " 2\n", - " Link\n", - " \n", - " \n", - " 11\n", - " 2206.00785.pdf\n", - " 3\n", - " Link\n", - " \n", - " \n", - " 12\n", - " 2206.00785.pdf\n", - " 4\n", - " Link\n", - " \n", - " \n", - " 13\n", - " 2206.00785.pdf\n", - " 5\n", - " Link\n", - " \n", - " \n", - " 14\n", - " 2206.00785.pdf\n", - " 6\n", - " Link\n", - " \n", - " \n", - " 15\n", - " 2206.00785.pdf\n", - " 7\n", - " Link\n", - " \n", - " \n", - " 16\n", - " 2206.00785.pdf\n", - " 8\n", - " Link\n", - " \n", - " \n", - " 17\n", - " 2206.00785.pdf\n", - " 9\n", - " Link\n", - " \n", - " \n", - " 18\n", - " 2206.00785.pdf\n", - " 10\n", - " Link\n", - " \n", - " \n", - " 19\n", - " 2206.00785.pdf\n", - " 11\n", - " Link\n", - " \n", - " \n", - " 20\n", - " 20140197356.pdf\n", - " 1\n", - " Link\n", - " \n", - " \n", - " 21\n", - " 20140197356.pdf\n", - " 2\n", - " Link\n", - " \n", - " \n", - " 22\n", - " 20140197356.pdf\n", - " 3\n", - " Link\n", - " \n", - " \n", - " 23\n", - " 20140197356.pdf\n", - " 4\n", - " Link\n", - " \n", - " \n", - " 24\n", - " 20140197356.pdf\n", - " 5\n", - " Link\n", - " \n", - " \n", - " 25\n", - " 20140197356.pdf\n", - " 6\n", - " Link\n", - " \n", - " \n", - " 26\n", - " 20140197356.pdf\n", - " 7\n", - " Link\n", - " \n", - " \n", - " 27\n", - " 20140197356.pdf\n", - " 8\n", - " Link\n", - " \n", - " \n", - " 28\n", - " 20140197356.pdf\n", - " 9\n", - " Link\n", - " \n", - " \n", - " 29\n", - " 20140197356.pdf\n", - " 10\n", - " Link\n", - " \n", - " \n", - " 30\n", - " 20140197356.pdf\n", - " 11\n", - " Link\n", - " \n", " \n", "" ], @@ -1197,7 +1014,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 40, "id": "9a0b82b8", "metadata": { "execution": { @@ -1213,9 +1030,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Data index tmp_20240527124643 deleted\n", - "Data index tmp_20240527124643-ocr deleted\n", - "Data index tmp_20240527124643-raw deleted\n" + "Data index tmp_20250115104829 deleted\n", + "Data index tmp_20250115104829-ocr deleted\n", + "Data index tmp_20250115104829-raw deleted\n" ] } ], @@ -1238,7 +1055,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, diff --git a/examples/data_query_chemistry/chemistry.ipynb b/examples/data_query_chemistry/chemistry.ipynb index 395fc18..b2cac90 100644 --- a/examples/data_query_chemistry/chemistry.ipynb +++ b/examples/data_query_chemistry/chemistry.ipynb @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "id": "f915761b", "metadata": { "scrolled": false @@ -176,19 +176,162 @@ " \n", " \n", " 0\n", + " BioRxiv\n", + " Document\n", + " 357.76K\n", + " 2023-11-09\n", + " default/biorxiv\n", + " \n", + " \n", + " 1\n", + " Brenda\n", + " Record\n", + " 7.12K\n", + " 2023-01-03\n", + " default/brenda\n", + " \n", + " \n", + " 2\n", + " ChEMBL\n", + " Record\n", + " 2.42M\n", + " 2024-04-26\n", + " default/chembl\n", + " \n", + " \n", + " 3\n", + " ChEMBL (DEPRECATED)\n", + " Record\n", + " 2.11M\n", + " 2023-01-03\n", + " default/chembl-deprecated\n", + " \n", + " \n", + " 4\n", + " ChemRxiv\n", + " Document\n", + " 8.82K\n", + " 2023-11-23\n", + " default/chemrxiv\n", + " \n", + " \n", + " 5\n", + " COD\n", + " Record\n", + " 503.78K\n", + " 2023-07-24\n", + " default/cod\n", + " \n", + " \n", + " 6\n", + " DeepSearch materials\n", + " Record\n", + " 360.54K\n", + " 2023-01-03\n", + " default/ds4sd-material\n", + " \n", + " \n", + " 7\n", + " Material Components\n", + " Document\n", + " 16.32K\n", + " 2023-01-30\n", + " default/experiment\n", + " \n", + " \n", + " 8\n", + " NMRShift\n", + " Record\n", + " 44.33K\n", + " 2023-01-03\n", + " default/nmrshift\n", + " \n", + " \n", + " 9\n", + " PatCID\n", + " Record\n", + " 23.91M\n", + " 2024-10-04\n", + " default/patcid\n", + " \n", + " \n", + " 10\n", " PubChem\n", " Record\n", " 118.24M\n", - " 2023-06-23\n", + " 2023-07-06\n", " default/pubchem\n", " \n", + " \n", + " 11\n", + " RxNorm\n", + " Record\n", + " 374.18K\n", + " 2023-01-03\n", + " default/rxnorm\n", + " \n", + " \n", + " 12\n", + " SMILES from USPTO\n", + " Record\n", + " 116.48M\n", + " 2022-12-25\n", + " default/patent-uspto-smiles\n", + " \n", + " \n", + " 13\n", + " SMILES from USPTO (fingerprints)\n", + " Record\n", + " 85.81M\n", + " 2024-07-31\n", + " default/patent-uspto-smiles-fp\n", + " \n", + " \n", + " 14\n", + " UMLS\n", + " Record\n", + " 2.69M\n", + " 2023-01-03\n", + " default/umls\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " Name Type Num entries Date Coords\n", - "0 PubChem Record 118.24M 2023-06-23 default/pubchem" + " Name Type Num entries Date \\\n", + "0 BioRxiv Document 357.76K 2023-11-09 \n", + "1 Brenda Record 7.12K 2023-01-03 \n", + "2 ChEMBL Record 2.42M 2024-04-26 \n", + "3 ChEMBL (DEPRECATED) Record 2.11M 2023-01-03 \n", + "4 ChemRxiv Document 8.82K 2023-11-23 \n", + "5 COD Record 503.78K 2023-07-24 \n", + "6 DeepSearch materials Record 360.54K 2023-01-03 \n", + "7 Material Components Document 16.32K 2023-01-30 \n", + "8 NMRShift Record 44.33K 2023-01-03 \n", + "9 PatCID Record 23.91M 2024-10-04 \n", + "10 PubChem Record 118.24M 2023-07-06 \n", + "11 RxNorm Record 374.18K 2023-01-03 \n", + "12 SMILES from USPTO Record 116.48M 2022-12-25 \n", + "13 SMILES from USPTO (fingerprints) Record 85.81M 2024-07-31 \n", + "14 UMLS Record 2.69M 2023-01-03 \n", + "\n", + " Coords \n", + "0 default/biorxiv \n", + "1 default/brenda \n", + "2 default/chembl \n", + "3 default/chembl-deprecated \n", + "4 default/chemrxiv \n", + "5 default/cod \n", + "6 default/ds4sd-material \n", + "7 default/experiment \n", + "8 default/nmrshift \n", + "9 default/patcid \n", + "10 default/pubchem \n", + "11 default/rxnorm \n", + "12 default/patent-uspto-smiles \n", + "13 default/patent-uspto-smiles-fp \n", + "14 default/umls " ] }, "metadata": {}, @@ -196,13 +339,14 @@ } ], "source": [ + "from datetime import datetime\n", "# Visualize summary table\n", "results = [\n", " {\n", " \"Name\": c.name,\n", " \"Type\": c.metadata.type,\n", " \"Num entries\": numerize(c.documents),\n", - " \"Date\": c.metadata.created.strftime(\"%Y-%m-%d\"),\n", + " \"Date\": datetime.fromisoformat(c.metadata.created).strftime(\"%Y-%m-%d\"),\n", " \"Coords\": f\"{c.source.elastic_id}/{c.source.index_key}\",\n", " }\n", " for c in collections\n", @@ -225,14 +369,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "id": "239fdea6", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e57cd03fd5aa4db6aad7d911428561ff", + "model_id": "7dbd3af0d7474eb0a0321ce5f5a070cd", "version_major": 2, "version_minor": 0 }, @@ -247,7 +391,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Finished fetching all data. Total is 15 records.\n" + "Finished fetching all data. Total is 1 records.\n" ] } ], @@ -292,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "id": "bf08bbcc", "metadata": { "scrolled": false @@ -324,365 +468,80 @@ " SMILES\n", " ec_number\n", " cas_number\n", - " paragraph\n", - " boiling point\n", - " melting point\n", - " solubility\n", " molecular weight\n", - " dissociation constant\n", + " xlogp3\n", + " hydrogen bond donor count\n", + " hydrogen bond acceptor count\n", + " rotatable bond count\n", + " ...\n", + " monoisotopic mass\n", + " topological polar surface area\n", + " heavy atom count\n", + " formal charge\n", + " complexity\n", + " isotope atom count\n", + " defined atom stereocenter count\n", + " undefined atom stereocenter count\n", + " covalently-bonded unit count\n", + " compound is canonicalized\n", " \n", " \n", " \n", " \n", " 0\n", - " 6575\n", - " Trichloroethylene\n", - " C(=C(Cl)Cl)Cl\n", - " 201-167-4\n", - " 79-01-6\n", - " IDENTIFICATION AND USE: Trichloroethylene (TCE...\n", - " 1,1,2-trichloroethene has boiling point = 87.2 °C\n", - " 1,1,2-trichloroethene has melting point = -99°F\n", - " 1,1,2-trichloroethene has solubility = less th...\n", - " 131.38\n", - " NaN\n", - " \n", - " \n", - " 1\n", - " 73981\n", - " Magnesium hydroxide\n", - " [OH-].[OH-].[Mg+2]\n", - " 215-170-3\n", - " 1309-42-8\n", - " LD50=8500 mg/kg (rat, oral) Common side effe...\n", - " NaN\n", - " NaN\n", - " magnesium;dihydroxide has solubility = Practic...\n", - " 58.32\n", - " NaN\n", - " \n", - " \n", - " 2\n", - " 3825\n", - " Ketoprofen\n", - " CC(C1=CC(=CC=C1)C(=O)C2=CC=CC=C2)C(=O)O\n", - " 244-759-8\n", - " 22071-15-4\n", - " Prospective studies show that 1% to 2% of pati...\n", - " NaN\n", - " 2-(3-benzoylphenyl)propanoic acid has melting ...\n", - " 2-(3-benzoylphenyl)propanoic acid has solubili...\n", - " 254.28\n", - " NaN\n", - " \n", - " \n", - " 3\n", - " 2662\n", - " Celecoxib\n", - " CC1=CC=C(C=C1)C2=CC(=NN2C3=CC=C(C=C3)S(=O)(=O)...\n", - " \n", - " 169590-42-5\n", - " Likelihood score: B (highly likely cause of cl...\n", - " 4-[5-(4-methylphenyl)-3-(trifluoromethyl)pyraz...\n", - " 4-[5-(4-methylphenyl)-3-(trifluoromethyl)pyraz...\n", - " 4-[5-(4-methylphenyl)-3-(trifluoromethyl)pyraz...\n", - " 381.40\n", - " 4-[5-(4-methylphenyl)-3-(trifluoromethyl)pyraz...\n", - " \n", - " \n", - " 4\n", - " 154699635\n", - " \n", - " CC(CC1=CC=C(C=C1)C(C)C(=O)O)CO\n", - " \n", - " \n", - " 3-Hydroxyibuprofen is a known human metabolite...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 222.28\n", - " NaN\n", - " \n", - " \n", - " 5\n", - " 39912\n", - " Dexibuprofen\n", - " CC(C)CC1=CC=C(C=C1)C(C)C(=O)O\n", - " \n", - " 51146-56-6\n", - " Oral LD50 value in rats is 636 mg/kg.[A19261] ...\n", - " NaN\n", - " (2S)-2-[4-(2-methylpropyl)phenyl]propanoic aci...\n", - " (2S)-2-[4-(2-methylpropyl)phenyl]propanoic aci...\n", - " 206.28\n", - " NaN\n", - " \n", - " \n", - " 6\n", - " 154699636\n", - " \n", - " CC(CC1=CC=C(C=C1)C(C)C(=O)O)CO\n", - " \n", - " \n", - " R,S-3-Hydroxyibuprofen is a known human metabo...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 222.28\n", - " NaN\n", - " \n", - " \n", - " 7\n", - " 5284569\n", - " Hydrocodone\n", - " CN1CCC23C4C1CC5=C2C(=C(C=C5)OC)OC3C(=O)CC4\n", - " 204-733-9\n", - " 125-29-1\n", - " References on the safety and potential hepatot...\n", - " NaN\n", - " (4R,4aR,7aR,12bS)-9-methoxy-3-methyl-1,2,4,4a,...\n", - " (4R,4aR,7aR,12bS)-9-methoxy-3-methyl-1,2,4,4a,...\n", - " 299.40\n", - " (4R,4aR,7aR,12bS)-9-methoxy-3-methyl-1,2,4,4a,...\n", - " \n", - " \n", - " 8\n", - " 1775\n", - " Phenytoin\n", - " C1=CC=C(C=C1)C2(C(=O)NC(=O)N2)C3=CC=CC=C3\n", - " 211-148-2\n", - " 630-93-3\n", - " Prospective studies indicate that a fairly hig...\n", - " NaN\n", - " 5,5-diphenylimidazolidine-2,4-dione has meltin...\n", - " 5,5-diphenylimidazolidine-2,4-dione has solubi...\n", - " 252.27\n", - " 5,5-diphenylimidazolidine-2,4-dione has logari...\n", - " \n", - " \n", - " 9\n", " 3672\n", " Ibuprofen\n", " CC(C)CC1=CC=C(C=C1)C(C)C(=O)O\n", " 239-784-6\n", " 79261-49-7 (potassium salt)\n", - " The appearance of clinically apparent liver in...\n", - " 2-[4-(2-methylpropyl)phenyl]propanoic acid has...\n", - " 2-[4-(2-methylpropyl)phenyl]propanoic acid has...\n", - " 2-[4-(2-methylpropyl)phenyl]propanoic acid has...\n", " 206.28\n", - " 2-[4-(2-methylpropyl)phenyl]propanoic acid has...\n", - " \n", - " \n", - " 10\n", - " 3394\n", - " \n", - " CC(C1=CC(=C(C=C1)C2=CC=CC=C2)F)C(=O)O\n", - " 257-262-6\n", - " 51543-38-5\n", - " Prospective studies show that mild elevations ...\n", - " NaN\n", - " 2-(3-fluoro-4-phenylphenyl)propanoic acid has ...\n", - " 2-(3-fluoro-4-phenylphenyl)propanoic acid has ...\n", - " 244.26\n", - " NaN\n", - " \n", - " \n", - " 11\n", - " 7028\n", - " Pseudoephedrine\n", - " CC(C(C1=CC=CC=C1)O)NC\n", - " 202-018-6\n", - " 90-82-4\n", - " Common adverse reactions include nervousness, ...\n", - " NaN\n", - " (1S,2S)-2-(methylamino)-1-phenylpropan-1-ol ha...\n", - " (1S,2S)-2-(methylamino)-1-phenylpropan-1-ol ha...\n", - " 165.23\n", - " (1S,2S)-2-(methylamino)-1-phenylpropan-1-ol ha...\n", - " \n", - " \n", - " 12\n", - " 156391\n", - " Naproxen\n", - " CC(C1=CC2=C(C=C1)C=C(C=C2)OC)C(=O)O\n", - " 247-485-7\n", - " 26159-31-9\n", - " Serum aminotransferase levels can be elevated ...\n", - " NaN\n", - " (2S)-2-(6-methoxynaphthalen-2-yl)propanoic aci...\n", - " (2S)-2-(6-methoxynaphthalen-2-yl)propanoic aci...\n", - " 230.26\n", - " (2S)-2-(6-methoxynaphthalen-2-yl)propanoic aci...\n", - " \n", - " \n", - " 13\n", - " 2581\n", - " Carprofen\n", - " CC(C1=CC2=C(C=C1)C3=C(N2)C=CC(=C3)Cl)C(=O)O\n", - " 258-712-4\n", - " 53716-49-7\n", - " Symptoms of NSAID overdose include dizziness a...\n", - " NaN\n", - " 2-(6-chloro-9H-carbazol-2-yl)propanoic acid ha...\n", - " 2-(6-chloro-9H-carbazol-2-yl)propanoic acid ha...\n", - " 273.71\n", - " NaN\n", - " \n", - " \n", - " 14\n", - " 154699469\n", - " \n", - " CC(C)CC1=CC=C(C=C1)C(C)C(=O)OC2C(C(C(C(O2)C(=O...\n", - " \n", - " \n", - " (2S,3S,4S,5R)-3,4,5-trihydroxy-6-[(2R)-2-[4-(2...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 382.40\n", - " NaN\n", + " 3.5\n", + " 1.0\n", + " 2.0\n", + " 4.0\n", + " ...\n", + " 206.13068\n", + " 37.3\n", + " 15.0\n", + " 0.0\n", + " 203.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " 1.0\n", + " Yes\n", " \n", " \n", "\n", + "

1 rows × 21 columns

\n", "" ], "text/plain": [ - " cid chemical_name \\\n", - "0 6575 Trichloroethylene \n", - "1 73981 Magnesium hydroxide \n", - "2 3825 Ketoprofen \n", - "3 2662 Celecoxib \n", - "4 154699635 \n", - "5 39912 Dexibuprofen \n", - "6 154699636 \n", - "7 5284569 Hydrocodone \n", - "8 1775 Phenytoin \n", - "9 3672 Ibuprofen \n", - "10 3394 \n", - "11 7028 Pseudoephedrine \n", - "12 156391 Naproxen \n", - "13 2581 Carprofen \n", - "14 154699469 \n", - "\n", - " SMILES ec_number \\\n", - "0 C(=C(Cl)Cl)Cl 201-167-4 \n", - "1 [OH-].[OH-].[Mg+2] 215-170-3 \n", - "2 CC(C1=CC(=CC=C1)C(=O)C2=CC=CC=C2)C(=O)O 244-759-8 \n", - "3 CC1=CC=C(C=C1)C2=CC(=NN2C3=CC=C(C=C3)S(=O)(=O)... \n", - "4 CC(CC1=CC=C(C=C1)C(C)C(=O)O)CO \n", - "5 CC(C)CC1=CC=C(C=C1)C(C)C(=O)O \n", - "6 CC(CC1=CC=C(C=C1)C(C)C(=O)O)CO \n", - "7 CN1CCC23C4C1CC5=C2C(=C(C=C5)OC)OC3C(=O)CC4 204-733-9 \n", - "8 C1=CC=C(C=C1)C2(C(=O)NC(=O)N2)C3=CC=CC=C3 211-148-2 \n", - "9 CC(C)CC1=CC=C(C=C1)C(C)C(=O)O 239-784-6 \n", - "10 CC(C1=CC(=C(C=C1)C2=CC=CC=C2)F)C(=O)O 257-262-6 \n", - "11 CC(C(C1=CC=CC=C1)O)NC 202-018-6 \n", - "12 CC(C1=CC2=C(C=C1)C=C(C=C2)OC)C(=O)O 247-485-7 \n", - "13 CC(C1=CC2=C(C=C1)C3=C(N2)C=CC(=C3)Cl)C(=O)O 258-712-4 \n", - "14 CC(C)CC1=CC=C(C=C1)C(C)C(=O)OC2C(C(C(C(O2)C(=O... \n", - "\n", - " cas_number \\\n", - "0 79-01-6 \n", - "1 1309-42-8 \n", - "2 22071-15-4 \n", - "3 169590-42-5 \n", - "4 \n", - "5 51146-56-6 \n", - "6 \n", - "7 125-29-1 \n", - "8 630-93-3 \n", - "9 79261-49-7 (potassium salt) \n", - "10 51543-38-5 \n", - "11 90-82-4 \n", - "12 26159-31-9 \n", - "13 53716-49-7 \n", - "14 \n", - "\n", - " paragraph \\\n", - "0 IDENTIFICATION AND USE: Trichloroethylene (TCE... \n", - "1 LD50=8500 mg/kg (rat, oral) Common side effe... \n", - "2 Prospective studies show that 1% to 2% of pati... \n", - "3 Likelihood score: B (highly likely cause of cl... \n", - "4 3-Hydroxyibuprofen is a known human metabolite... \n", - "5 Oral LD50 value in rats is 636 mg/kg.[A19261] ... \n", - "6 R,S-3-Hydroxyibuprofen is a known human metabo... \n", - "7 References on the safety and potential hepatot... \n", - "8 Prospective studies indicate that a fairly hig... \n", - "9 The appearance of clinically apparent liver in... \n", - "10 Prospective studies show that mild elevations ... \n", - "11 Common adverse reactions include nervousness, ... \n", - "12 Serum aminotransferase levels can be elevated ... \n", - "13 Symptoms of NSAID overdose include dizziness a... \n", - "14 (2S,3S,4S,5R)-3,4,5-trihydroxy-6-[(2R)-2-[4-(2... \n", - "\n", - " boiling point \\\n", - "0 1,1,2-trichloroethene has boiling point = 87.2 °C \n", - "1 NaN \n", - "2 NaN \n", - "3 4-[5-(4-methylphenyl)-3-(trifluoromethyl)pyraz... \n", - "4 NaN \n", - "5 NaN \n", - "6 NaN \n", - "7 NaN \n", - "8 NaN \n", - "9 2-[4-(2-methylpropyl)phenyl]propanoic acid has... \n", - "10 NaN \n", - "11 NaN \n", - "12 NaN \n", - "13 NaN \n", - "14 NaN \n", - "\n", - " melting point \\\n", - "0 1,1,2-trichloroethene has melting point = -99°F \n", - "1 NaN \n", - "2 2-(3-benzoylphenyl)propanoic acid has melting ... \n", - "3 4-[5-(4-methylphenyl)-3-(trifluoromethyl)pyraz... \n", - "4 NaN \n", - "5 (2S)-2-[4-(2-methylpropyl)phenyl]propanoic aci... \n", - "6 NaN \n", - "7 (4R,4aR,7aR,12bS)-9-methoxy-3-methyl-1,2,4,4a,... \n", - "8 5,5-diphenylimidazolidine-2,4-dione has meltin... \n", - "9 2-[4-(2-methylpropyl)phenyl]propanoic acid has... \n", - "10 2-(3-fluoro-4-phenylphenyl)propanoic acid has ... \n", - "11 (1S,2S)-2-(methylamino)-1-phenylpropan-1-ol ha... \n", - "12 (2S)-2-(6-methoxynaphthalen-2-yl)propanoic aci... \n", - "13 2-(6-chloro-9H-carbazol-2-yl)propanoic acid ha... \n", - "14 NaN \n", - "\n", - " solubility molecular weight \\\n", - "0 1,1,2-trichloroethene has solubility = less th... 131.38 \n", - "1 magnesium;dihydroxide has solubility = Practic... 58.32 \n", - "2 2-(3-benzoylphenyl)propanoic acid has solubili... 254.28 \n", - "3 4-[5-(4-methylphenyl)-3-(trifluoromethyl)pyraz... 381.40 \n", - "4 NaN 222.28 \n", - "5 (2S)-2-[4-(2-methylpropyl)phenyl]propanoic aci... 206.28 \n", - "6 NaN 222.28 \n", - "7 (4R,4aR,7aR,12bS)-9-methoxy-3-methyl-1,2,4,4a,... 299.40 \n", - "8 5,5-diphenylimidazolidine-2,4-dione has solubi... 252.27 \n", - "9 2-[4-(2-methylpropyl)phenyl]propanoic acid has... 206.28 \n", - "10 2-(3-fluoro-4-phenylphenyl)propanoic acid has ... 244.26 \n", - "11 (1S,2S)-2-(methylamino)-1-phenylpropan-1-ol ha... 165.23 \n", - "12 (2S)-2-(6-methoxynaphthalen-2-yl)propanoic aci... 230.26 \n", - "13 2-(6-chloro-9H-carbazol-2-yl)propanoic acid ha... 273.71 \n", - "14 NaN 382.40 \n", - "\n", - " dissociation constant \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 4-[5-(4-methylphenyl)-3-(trifluoromethyl)pyraz... \n", - "4 NaN \n", - "5 NaN \n", - "6 NaN \n", - "7 (4R,4aR,7aR,12bS)-9-methoxy-3-methyl-1,2,4,4a,... \n", - "8 5,5-diphenylimidazolidine-2,4-dione has logari... \n", - "9 2-[4-(2-methylpropyl)phenyl]propanoic acid has... \n", - "10 NaN \n", - "11 (1S,2S)-2-(methylamino)-1-phenylpropan-1-ol ha... \n", - "12 (2S)-2-(6-methoxynaphthalen-2-yl)propanoic aci... \n", - "13 NaN \n", - "14 NaN " + " cid chemical_name SMILES ec_number \\\n", + "0 3672 Ibuprofen CC(C)CC1=CC=C(C=C1)C(C)C(=O)O 239-784-6 \n", + "\n", + " cas_number molecular weight xlogp3 \\\n", + "0 79261-49-7 (potassium salt) 206.28 3.5 \n", + "\n", + " hydrogen bond donor count hydrogen bond acceptor count \\\n", + "0 1.0 2.0 \n", + "\n", + " rotatable bond count ... monoisotopic mass \\\n", + "0 4.0 ... 206.13068 \n", + "\n", + " topological polar surface area heavy atom count formal charge \\\n", + "0 37.3 15.0 0.0 \n", + "\n", + " complexity isotope atom count defined atom stereocenter count \\\n", + "0 203.0 0.0 0.0 \n", + "\n", + " undefined atom stereocenter count covalently-bonded unit count \\\n", + "0 1.0 1.0 \n", + "\n", + " compound is canonicalized \n", + "0 Yes \n", + "\n", + "[1 rows x 21 columns]" ] }, "metadata": {}, @@ -750,14 +609,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "id": "dd3ed56e", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8b7e4dd82e9a49b69a1683a50f49f68c", + "model_id": "da0483e59a58496f9a6d44fd7d7e0e66", "version_major": 2, "version_minor": 0 }, @@ -771,373 +630,1474 @@ { "data": { "text/html": [ + "\n", + "\n", "" ], "text/plain": [ "" ] }, - "execution_count": 8, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1450,392 +2625,1485 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "id": "b3de6455", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "87531afa29214ba9b4fd0ec8a92a5a24", + "model_id": "9bedd0aee2e241719e9359282f0ab390", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
cidchemical_nameSMILESec_numbercas_numbermolecular weightxlogp3hydrogen bond donor counthydrogen bond acceptor countrotatable bond count...monoisotopic masstopological polar surface areaheavy atom countformal chargecomplexityisotope atom countdefined atom stereocenter countundefined atom stereocenter countcovalently-bonded unit countcompound is canonicalized
03672IbuprofenCC(C)CC1=CC=C(C=C1)C(C)C(=O)O239-784-679261-49-7 (potassium salt)206.283.51.02.04.0...206.1306837.315.00.0203.00.00.01.01.0Yes
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1 rows × 21 columns

\n", + "" + ], + "text/plain": [ + " cid chemical_name SMILES ec_number \\\n", + "0 3672 Ibuprofen CC(C)CC1=CC=C(C=C1)C(C)C(=O)O 239-784-6 \n", + "\n", + " cas_number molecular weight xlogp3 \\\n", + "0 79261-49-7 (potassium salt) 206.28 3.5 \n", + "\n", + " hydrogen bond donor count hydrogen bond acceptor count \\\n", + "0 1.0 2.0 \n", + "\n", + " rotatable bond count ... monoisotopic mass \\\n", + "0 4.0 ... 206.13068 \n", + "\n", + " topological polar surface area heavy atom count formal charge \\\n", + "0 37.3 15.0 0.0 \n", + "\n", + " complexity isotope atom count defined atom stereocenter count \\\n", + "0 203.0 0.0 0.0 \n", + "\n", + " undefined atom stereocenter count covalently-bonded unit count \\\n", + "0 1.0 1.0 \n", + "\n", + " compound is canonicalized \n", + "0 Yes \n", + "\n", + "[1 rows x 21 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "91b3896a3aeb40068a700e28196c2072", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/1 [00:00\n", - "\n", + "\n", + "" ], "text/plain": [ "" ] }, - "execution_count": 10, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -2341,14 +4858,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "id": "10f3937d", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3535a3e242f64515a8420e80aa852ff0", + "model_id": "b94c2c00070c47db99dd4ce1fab7eb23", "version_major": 2, "version_minor": 0 }, @@ -2409,7 +4926,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "id": "df310ef1", "metadata": {}, "outputs": [ @@ -2440,7 +4957,21 @@ " ec_number\n", " cas_number\n", " molecular weight\n", - " melting point\n", + " xlogp3\n", + " hydrogen bond donor count\n", + " hydrogen bond acceptor count\n", + " rotatable bond count\n", + " ...\n", + " monoisotopic mass\n", + " topological polar surface area\n", + " heavy atom count\n", + " formal charge\n", + " complexity\n", + " isotope atom count\n", + " defined atom stereocenter count\n", + " undefined atom stereocenter count\n", + " covalently-bonded unit count\n", + " compound is canonicalized\n", " \n", " \n", " \n", @@ -2452,7 +4983,21 @@ " \n", " \n", " 166.23\n", - " NaN\n", + " 1.8\n", + " 2.0\n", + " 1.0\n", + " 2.0\n", + " ...\n", + " 166.115448\n", + " 36.0\n", + " 12.0\n", + " 0.0\n", + " 149.0\n", + " 5.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " Yes\n", " \n", " \n", " 1\n", @@ -2462,7 +5007,21 @@ " \n", " \n", " 165.22\n", - " NaN\n", + " 1.8\n", + " 2.0\n", + " 1.0\n", + " 2.0\n", + " ...\n", + " 165.109171\n", + " 36.0\n", + " 12.0\n", + " 0.0\n", + " 149.0\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " Yes\n", " \n", " \n", " 2\n", @@ -2472,10 +5031,25 @@ " 208-393-2\n", " 526-55-6\n", " 161.20\n", - " 2-(1H-indol-3-yl)ethanol has melting point = 59°C\n", + " 1.8\n", + " 2.0\n", + " 1.0\n", + " 2.0\n", + " ...\n", + " 161.084064\n", + " 36.0\n", + " 12.0\n", + " 0.0\n", + " 149.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " Yes\n", " \n", " \n", "\n", + "

3 rows × 21 columns

\n", "" ], "text/plain": [ @@ -2484,10 +5058,37 @@ "1 101132237 C1=CC=C2C(=C1)C(=CN2)CCO \n", "2 10685 Tryptophol C1=CC=C2C(=C1)C(=CN2)CCO 208-393-2 526-55-6 \n", "\n", - " molecular weight melting point \n", - "0 166.23 NaN \n", - "1 165.22 NaN \n", - "2 161.20 2-(1H-indol-3-yl)ethanol has melting point = 59°C " + " molecular weight xlogp3 hydrogen bond donor count \\\n", + "0 166.23 1.8 2.0 \n", + "1 165.22 1.8 2.0 \n", + "2 161.20 1.8 2.0 \n", + "\n", + " hydrogen bond acceptor count rotatable bond count ... monoisotopic mass \\\n", + "0 1.0 2.0 ... 166.115448 \n", + "1 1.0 2.0 ... 165.109171 \n", + "2 1.0 2.0 ... 161.084064 \n", + "\n", + " topological polar surface area heavy atom count formal charge \\\n", + "0 36.0 12.0 0.0 \n", + "1 36.0 12.0 0.0 \n", + "2 36.0 12.0 0.0 \n", + "\n", + " complexity isotope atom count defined atom stereocenter count \\\n", + "0 149.0 5.0 0.0 \n", + "1 149.0 4.0 0.0 \n", + "2 149.0 0.0 0.0 \n", + "\n", + " undefined atom stereocenter count covalently-bonded unit count \\\n", + "0 0.0 1.0 \n", + "1 0.0 1.0 \n", + "2 0.0 1.0 \n", + "\n", + " compound is canonicalized \n", + "0 Yes \n", + "1 Yes \n", + "2 Yes \n", + "\n", + "[3 rows x 21 columns]" ] }, "metadata": {}, @@ -2496,7 +5097,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5d1c2710e63c47938f348ed0b69bddd2", + "model_id": "3e7a6666be5c4fc38baa0352c6a3b70f", "version_major": 2, "version_minor": 0 }, @@ -2510,201 +5111,1292 @@ { "data": { "text/html": [ + "\n", + "\n", "" ], "text/plain": [ "" ] }, - "execution_count": 12, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -3201,7 +7142,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -3215,7 +7156,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.4" } }, "nbformat": 4, diff --git a/examples/data_query_chemistry_patcid/chemistry_patcid.ipynb b/examples/data_query_chemistry_patcid/chemistry_patcid.ipynb index df2dd2a..e0e31e7 100644 --- a/examples/data_query_chemistry_patcid/chemistry_patcid.ipynb +++ b/examples/data_query_chemistry_patcid/chemistry_patcid.ipynb @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "id": "4a9b4de7-bace-4c39-a4e7-b563292a2a30", "metadata": {}, "outputs": [], @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "1ea3cd26", "metadata": {}, "outputs": [], @@ -95,6 +95,15 @@ " DocumentsByIds,\n", " DocumentsHaving,\n", ")\n", + "from deepsearch.chemistry.queries import (\n", + " query_chemistry,\n", + " CompoundsBySubstructure,\n", + " CompoundsBySimilarity,\n", + " CompoundsBySmarts,\n", + " CompoundsIn,\n", + " DocumentsByIds,\n", + " DocumentsHaving,\n", + ")\n", "from deepsearch.chemistry.queries.molecules import MolId, MolIdType, MolQueryType\n", "from deepsearch.cps.client.components.elastic import ElasticDataCollectionSource\n", "from deepsearch.documents.core.lookup import EntitiesLookup\n", @@ -114,10 +123,38 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 10, "id": "fae4dee3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "The API Key or User is invalid.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mApiException\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Documents/dev/deepsearch-examples/venv/lib/python3.10/site-packages/deepsearch/cps/client/api.py:99\u001b[0m, in \u001b[0;36mCpsApiClient._authenticate_with_api_key\u001b[0;34m(host, username, api_key)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m access_token \u001b[38;5;241m=\u001b[39m \u001b[43mapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_access_token\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43madmin\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39maccess_token\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m deepsearch\u001b[38;5;241m.\u001b[39mcps\u001b[38;5;241m.\u001b[39mapis\u001b[38;5;241m.\u001b[39muser\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mApiException \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "File \u001b[0;32m~/Documents/dev/deepsearch-examples/venv/lib/python3.10/site-packages/deepsearch/cps/apis/user/api/users_api.py:167\u001b[0m, in \u001b[0;36mUsersApi.get_access_token\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 166\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_return_http_data_only\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_access_token_with_http_info\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/dev/deepsearch-examples/venv/lib/python3.10/site-packages/deepsearch/cps/apis/user/api/users_api.py:242\u001b[0m, in \u001b[0;36mUsersApi.get_access_token_with_http_info\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 240\u001b[0m auth_settings \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBearer\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUserAPIKey\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;66;03m# noqa: E501\u001b[39;00m\n\u001b[0;32m--> 242\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapi_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_api\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 243\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/user/token\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPOST\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 244\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 245\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 246\u001b[0m \u001b[43m \u001b[49m\u001b[43mheader_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 247\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mform_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 249\u001b[0m \u001b[43m \u001b[49m\u001b[43mfiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_var_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 250\u001b[0m \u001b[43m \u001b[49m\u001b[43mresponse_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mAccessToken\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# noqa: E501\u001b[39;49;00m\n\u001b[1;32m 251\u001b[0m \u001b[43m \u001b[49m\u001b[43mauth_settings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth_settings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 252\u001b[0m \u001b[43m 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_host)\u001b[0m\n\u001b[1;32m 187\u001b[0m e\u001b[38;5;241m.\u001b[39mbody \u001b[38;5;241m=\u001b[39m e\u001b[38;5;241m.\u001b[39mbody\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m six\u001b[38;5;241m.\u001b[39mPY3 \u001b[38;5;28;01melse\u001b[39;00m e\u001b[38;5;241m.\u001b[39mbody\n\u001b[0;32m--> 188\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 190\u001b[0m content_type \u001b[38;5;241m=\u001b[39m response_data\u001b[38;5;241m.\u001b[39mgetheader(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontent-type\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/Documents/dev/deepsearch-examples/venv/lib/python3.10/site-packages/deepsearch/cps/apis/user/api_client.py:181\u001b[0m, in \u001b[0;36mApiClient.__call_api\u001b[0;34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host)\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 180\u001b[0m \u001b[38;5;66;03m# perform request and return response\u001b[39;00m\n\u001b[0;32m--> 181\u001b[0m response_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 182\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheader_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 183\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 184\u001b[0m \u001b[43m \u001b[49m\u001b[43m_preload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_preload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[43m \u001b[49m\u001b[43m_request_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_request_timeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ApiException \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "File \u001b[0;32m~/Documents/dev/deepsearch-examples/venv/lib/python3.10/site-packages/deepsearch/cps/apis/user/api_client.py:407\u001b[0m, in \u001b[0;36mApiClient.request\u001b[0;34m(self, method, url, query_params, headers, post_params, body, _preload_content, _request_timeout)\u001b[0m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPOST\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 407\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrest_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPOST\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 408\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 409\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 410\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 411\u001b[0m \u001b[43m \u001b[49m\u001b[43m_preload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_preload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 412\u001b[0m \u001b[43m \u001b[49m\u001b[43m_request_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_request_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 413\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPUT\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[0;32m~/Documents/dev/deepsearch-examples/venv/lib/python3.10/site-packages/deepsearch/cps/apis/user/rest.py:265\u001b[0m, in \u001b[0;36mRESTClientObject.POST\u001b[0;34m(self, url, headers, query_params, post_params, body, _preload_content, _request_timeout)\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mPOST\u001b[39m(\u001b[38;5;28mself\u001b[39m, url, headers\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, query_params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, post_params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 264\u001b[0m body\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, _preload_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, _request_timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 265\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPOST\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43m_preload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_preload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[43m \u001b[49m\u001b[43m_request_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_request_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/dev/deepsearch-examples/venv/lib/python3.10/site-packages/deepsearch/cps/apis/user/rest.py:224\u001b[0m, in \u001b[0;36mRESTClientObject.request\u001b[0;34m(self, method, url, query_params, headers, body, post_params, _preload_content, _request_timeout)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;241m200\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m299\u001b[39m:\n\u001b[0;32m--> 224\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ApiException(http_resp\u001b[38;5;241m=\u001b[39mr)\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r\n", + "\u001b[0;31mApiException\u001b[0m: (503)\nReason: Service Unavailable\nHTTP response headers: HTTPHeaderDict({'pragma': 'no-cache', 'cache-control': 'private, max-age=0, no-cache, no-store', 'content-type': 'text/html'})\nHTTP response body: \r\n \r\n \r\n\r\n \r\n \r\n \r\n
\r\n

Application is not available

\r\n

The application is currently not serving requests at this endpoint. It may not have been started or is still starting.

\r\n\r\n
\r\n

\r\n Possible reasons you are seeing this page:\r\n

\r\n
    \r\n
  • \r\n The host doesn't exist.\r\n Make sure the hostname was typed correctly and that a route matching this hostname exists.\r\n
    • \r\n
  • \r\n The host exists, but doesn't have a matching path.\r\n Check if the URL path was typed correctly and that the route was created using the desired path.\r\n
    • \r\n
  • \r\n Route and path matches, but all pods are down.\r\n Make sure that the resources exposed by this route (pods, services, deployment configs, etc) have at least one pod running.\r\n
    • \r\n
\r\n
\r\n
\r\n \r\n\r\n\n", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m api \u001b[38;5;241m=\u001b[39m \u001b[43mCpsApi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_env\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprofile_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mPROFILE_NAME\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/dev/deepsearch-examples/venv/lib/python3.10/site-packages/deepsearch/cps/client/api.py:182\u001b[0m, in \u001b[0;36mCpsApi.from_env\u001b[0;34m(cls, profile_name)\u001b[0m\n\u001b[1;32m 180\u001b[0m settings_mgr \u001b[38;5;241m=\u001b[39m SettingsManager()\n\u001b[1;32m 181\u001b[0m settings \u001b[38;5;241m=\u001b[39m settings_mgr\u001b[38;5;241m.\u001b[39mget_profile_settings(profile_name\u001b[38;5;241m=\u001b[39mprofile_name)\n\u001b[0;32m--> 182\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_settings\u001b[49m\u001b[43m(\u001b[49m\u001b[43msettings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msettings\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/dev/deepsearch-examples/venv/lib/python3.10/site-packages/deepsearch/cps/client/api.py:203\u001b[0m, in \u001b[0;36mCpsApi.from_settings\u001b[0;34m(cls, settings)\u001b[0m\n\u001b[1;32m 194\u001b[0m auth \u001b[38;5;241m=\u001b[39m DeepSearchKeyAuth(\n\u001b[1;32m 195\u001b[0m username\u001b[38;5;241m=\u001b[39msettings\u001b[38;5;241m.\u001b[39musername,\n\u001b[1;32m 196\u001b[0m api_key\u001b[38;5;241m=\u001b[39msettings\u001b[38;5;241m.\u001b[39mapi_key\u001b[38;5;241m.\u001b[39mget_secret_value(),\n\u001b[1;32m 197\u001b[0m )\n\u001b[1;32m 198\u001b[0m config \u001b[38;5;241m=\u001b[39m DeepSearchConfig(\n\u001b[1;32m 199\u001b[0m host\u001b[38;5;241m=\u001b[39msettings\u001b[38;5;241m.\u001b[39mhost,\n\u001b[1;32m 200\u001b[0m auth\u001b[38;5;241m=\u001b[39mauth,\n\u001b[1;32m 201\u001b[0m verify_ssl\u001b[38;5;241m=\u001b[39msettings\u001b[38;5;241m.\u001b[39mverify_ssl,\n\u001b[1;32m 202\u001b[0m )\n\u001b[0;32m--> 203\u001b[0m client \u001b[38;5;241m=\u001b[39m \u001b[43mCpsApiClient\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(client)\n", + "File \u001b[0;32m~/Documents/dev/deepsearch-examples/venv/lib/python3.10/site-packages/deepsearch/cps/client/api.py:37\u001b[0m, in \u001b[0;36mCpsApiClient.__init__\u001b[0;34m(self, config)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig \u001b[38;5;241m=\u001b[39m config\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mauth, DeepSearchKeyAuth):\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbearer_token_auth \u001b[38;5;241m=\u001b[39m DeepSearchBearerTokenAuth(\n\u001b[0;32m---> 37\u001b[0m bearer_token\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_authenticate_with_api_key\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 38\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43musername\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 40\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapi_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 41\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 42\u001b[0m )\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# config.auth is of type DeepSearchAuth, which is either DeepSearchKeyAuth or DeepSearchBearerTokenAuth\u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbearer_token_auth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mauth\n", + "File \u001b[0;32m~/Documents/dev/deepsearch-examples/venv/lib/python3.10/site-packages/deepsearch/cps/client/api.py:101\u001b[0m, in \u001b[0;36mCpsApiClient._authenticate_with_api_key\u001b[0;34m(host, username, api_key)\u001b[0m\n\u001b[1;32m 99\u001b[0m access_token \u001b[38;5;241m=\u001b[39m api\u001b[38;5;241m.\u001b[39mget_access_token(options\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124madmin\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m})\u001b[38;5;241m.\u001b[39maccess_token\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m deepsearch\u001b[38;5;241m.\u001b[39mcps\u001b[38;5;241m.\u001b[39mapis\u001b[38;5;241m.\u001b[39muser\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mApiException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 101\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe API Key or User is invalid.\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m access_token\n", + "\u001b[0;31mRuntimeError\u001b[0m: The API Key or User is invalid." + ] + } + ], "source": [ "api = CpsApi.from_env(profile_name=PROFILE_NAME)" ] diff --git a/examples/data_query_quick_start/quick_start.ipynb b/examples/data_query_quick_start/quick_start.ipynb index 0d3aa3a..a724f70 100644 --- a/examples/data_query_quick_start/quick_start.ipynb +++ b/examples/data_query_quick_start/quick_start.ipynb @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "id": "1ea3cd26", "metadata": { "execution": { @@ -92,6 +92,7 @@ "source": [ "# Import standard dependenices\n", "from copy import deepcopy\n", + "from datetime import datetime\n", "import pandas as pd\n", "from numerize.numerize import numerize\n", "from tqdm.notebook import tqdm\n", @@ -185,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "f915761b", "metadata": { "execution": { @@ -231,7 +232,7 @@ " AAAI\n", " Document\n", " 16.02K\n", - " 2024-01-23\n", + " 2023-08-29\n", " default/aaai\n", " \n", " \n", @@ -239,7 +240,7 @@ " ACL Anthology\n", " Document\n", " 55.28K\n", - " 2023-10-05\n", + " 2023-08-22\n", " default/acl\n", " \n", " \n", @@ -247,193 +248,106 @@ " Annual Reports\n", " Document\n", " 107.38K\n", - " 2024-01-23\n", + " 2024-04-15\n", " default/annual-report\n", " \n", " \n", " 3\n", " arXiv abstracts\n", " Document\n", - " 2.35M\n", - " 2023-10-24\n", + " 2.48M\n", + " 2024-05-22\n", " default/arxiv-abstract\n", " \n", " \n", " 4\n", - " arXiv full documents\n", - " Document\n", - " 2.29M\n", - " 2024-01-27\n", - " default/arxiv\n", - " \n", - " \n", - " 5\n", - " ClinicalTrials\n", - " Document\n", - " 426.42K\n", - " 2023-06-01\n", - " default/clinical-trials\n", - " \n", - " \n", - " 6\n", - " Cord19\n", - " Document\n", - " 655.45K\n", - " 2023-04-14\n", - " default/cord19\n", - " \n", - " \n", - " 7\n", - " Crossref\n", - " Document\n", - " 131.86M\n", - " 2023-04-15\n", - " default/crossref\n", - " \n", - " \n", - " 8\n", - " ESG Reports\n", - " Document\n", - " 17.36K\n", - " 2024-01-22\n", - " default/esg-report\n", - " \n", - " \n", - " 9\n", - " IBM Redbooks\n", - " Document\n", - " 2.75K\n", - " 2023-08-02\n", - " default/ibm-redbooks\n", - " \n", - " \n", - " 10\n", - " IPCC\n", - " Document\n", - " 819\n", - " 2023-06-15\n", - " default/ipcc\n", - " \n", - " \n", - " 11\n", - " PatCID\n", + " arXiv category taxonomy\n", " Record\n", - " 13.03M\n", - " 2023-09-05\n", - " default/patcid\n", + " 155\n", + " 2024-05-22\n", + " default/arxiv-category\n", " \n", " \n", - " 12\n", - " Patent SMILES\n", - " Document\n", - " 2.84M\n", - " 2024-01-21\n", - " default/patent-smiles\n", - " \n", - " \n", - " 13\n", - " Patents from USPTO\n", - " Document\n", - " 15.76M\n", - " 2023-09-07\n", - " default/patent-uspto\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 14\n", - " PLOS\n", - " Document\n", - " 340.28K\n", - " 2024-01-22\n", - " default/plos\n", - " \n", - " \n", - " 15\n", - " Press Release\n", - " Document\n", - " 43.04K\n", - " 2024-02-27\n", - " default/press-release\n", + " 56\n", + " UMLS\n", + " Record\n", + " 2.69M\n", + " 2023-01-03\n", + " default/umls\n", " \n", " \n", - " 16\n", - " PubChem\n", + " 57\n", + " UniProt\n", " Record\n", - " 118.24M\n", - " 2023-10-06\n", - " default/pubchem\n", + " 567.48K\n", + " 2023-01-03\n", + " default/uniprot\n", " \n", " \n", - " 17\n", - " PubMed Central\n", + " 58\n", + " USPTO patents for NER\n", " Document\n", - " 5.08M\n", - " 2023-04-12\n", - " default/pubmed\n", + " 2.64K\n", + " 2023-03-20\n", + " default/uspto-for-ner\n", " \n", " \n", - " 18\n", - " Red Hat\n", + " 59\n", + " VHDL articles\n", " Document\n", - " 6.21K\n", - " 2023-07-14\n", - " default/redhat\n", + " 215\n", + " 2024-04-23\n", + " default/vhdl\n", " \n", " \n", - " 19\n", - " Semantic Scholar Academic Graph\n", + " 60\n", + " Wikipedia\n", " Document\n", - " 212.11M\n", - " 2023-05-28\n", - " default/semantic-scholar\n", + " 6.45M\n", + " 2024-02-26\n", + " default/wikipedia\n", " \n", " \n", "\n", + "

61 rows × 5 columns

\n", "" ], "text/plain": [ - " Name Type Num entries Date \\\n", - "0 AAAI Document 16.02K 2024-01-23 \n", - "1 ACL Anthology Document 55.28K 2023-10-05 \n", - "2 Annual Reports Document 107.38K 2024-01-23 \n", - "3 arXiv abstracts Document 2.35M 2023-10-24 \n", - "4 arXiv full documents Document 2.29M 2024-01-27 \n", - "5 ClinicalTrials Document 426.42K 2023-06-01 \n", - "6 Cord19 Document 655.45K 2023-04-14 \n", - "7 Crossref Document 131.86M 2023-04-15 \n", - "8 ESG Reports Document 17.36K 2024-01-22 \n", - "9 IBM Redbooks Document 2.75K 2023-08-02 \n", - "10 IPCC Document 819 2023-06-15 \n", - "11 PatCID Record 13.03M 2023-09-05 \n", - "12 Patent SMILES Document 2.84M 2024-01-21 \n", - "13 Patents from USPTO Document 15.76M 2023-09-07 \n", - "14 PLOS Document 340.28K 2024-01-22 \n", - "15 Press Release Document 43.04K 2024-02-27 \n", - "16 PubChem Record 118.24M 2023-10-06 \n", - "17 PubMed Central Document 5.08M 2023-04-12 \n", - "18 Red Hat Document 6.21K 2023-07-14 \n", - "19 Semantic Scholar Academic Graph Document 212.11M 2023-05-28 \n", + " Name Type Num entries Date \\\n", + "0 AAAI Document 16.02K 2023-08-29 \n", + "1 ACL Anthology Document 55.28K 2023-08-22 \n", + "2 Annual Reports Document 107.38K 2024-04-15 \n", + "3 arXiv abstracts Document 2.48M 2024-05-22 \n", + "4 arXiv category taxonomy Record 155 2024-05-22 \n", + ".. ... ... ... ... \n", + "56 UMLS Record 2.69M 2023-01-03 \n", + "57 UniProt Record 567.48K 2023-01-03 \n", + "58 USPTO patents for NER Document 2.64K 2023-03-20 \n", + "59 VHDL articles Document 215 2024-04-23 \n", + "60 Wikipedia Document 6.45M 2024-02-26 \n", + "\n", + " Coords \n", + "0 default/aaai \n", + "1 default/acl \n", + "2 default/annual-report \n", + "3 default/arxiv-abstract \n", + "4 default/arxiv-category \n", + ".. ... \n", + "56 default/umls \n", + "57 default/uniprot \n", + "58 default/uspto-for-ner \n", + "59 default/vhdl \n", + "60 default/wikipedia \n", "\n", - " Coords \n", - "0 default/aaai \n", - "1 default/acl \n", - "2 default/annual-report \n", - "3 default/arxiv-abstract \n", - "4 default/arxiv \n", - "5 default/clinical-trials \n", - "6 default/cord19 \n", - "7 default/crossref \n", - "8 default/esg-report \n", - "9 default/ibm-redbooks \n", - "10 default/ipcc \n", - "11 default/patcid \n", - "12 default/patent-smiles \n", - "13 default/patent-uspto \n", - "14 default/plos \n", - "15 default/press-release \n", - "16 default/pubchem \n", - "17 default/pubmed \n", - "18 default/redhat \n", - "19 default/semantic-scholar " + "[61 rows x 5 columns]" ] }, "metadata": {}, @@ -447,7 +361,7 @@ " \"Name\": c.name,\n", " \"Type\": c.metadata.type,\n", " \"Num entries\": numerize(c.documents),\n", - " \"Date\": c.metadata.created.strftime(\"%Y-%m-%d\"),\n", + " \"Date\": datetime.fromisoformat(c.metadata.created).strftime(\"%Y-%m-%d\"),\n", " \"Coords\": f\"{c.source.elastic_id}/{c.source.index_key}\",\n", " }\n", " for c in collections\n", @@ -469,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "81df4c38", "metadata": { "execution": { @@ -484,12 +398,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ebe3e6e6cd604219bccebb0b6d93f30c", + "model_id": "c1eafade180c44bd8f392727d20b0ed5", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/20 [00:00\n", " \n", " 0\n", - " Semantic Scholar Academic Graph\n", - " 10939\n", + " Patents from USPTO\n", + " 11755\n", " \n", " \n", " 1\n", - " Patents from USPTO\n", - " 10487\n", + " Semantic Scholar Academic Graph\n", + " 11648\n", " \n", " \n", " 2\n", @@ -538,103 +452,229 @@ " \n", " \n", " 3\n", + " Patents from EPO\n", + " 2929\n", + " \n", + " \n", + " 4\n", " arXiv full documents\n", " 2031\n", " \n", " \n", - " 4\n", + " 5\n", + " News\n", + " 1478\n", + " \n", + " \n", + " 6\n", " PLOS\n", " 570\n", " \n", " \n", - " 5\n", + " 7\n", + " Wikipedia\n", + " 477\n", + " \n", + " \n", + " 8\n", + " BioRxiv\n", + " 447\n", + " \n", + " 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25\n", " IPCC\n", " 7\n", " \n", " \n", - " 12\n", - " Press Release\n", - " 4\n", + " 26\n", + " OpenCVF\n", + " 6\n", " \n", " \n", - " 13\n", + " 27\n", " AAAI\n", " 2\n", " \n", " \n", - " 14\n", + " 28\n", " ClinicalTrials\n", " 1\n", " \n", " \n", - " 15\n", + " 29\n", + " OpenStax\n", + " 1\n", + " \n", + " \n", + " 30\n", " Crossref\n", " 0\n", " \n", " \n", - " 16\n", + " 31\n", + " FDA Adverse Event Reporting System (FAERS)\n", + " 0\n", + " \n", + " \n", + " 32\n", + " HBCP Open Access Corpus\n", + " 0\n", + " \n", + " \n", + " 33\n", + " Material Components\n", + " 0\n", + " \n", + " \n", + " 34\n", + " NeurIPS\n", + " 0\n", + " \n", + " \n", + " 35\n", " Patent SMILES\n", " 0\n", " \n", " \n", - " 17\n", + " 36\n", + " Patents from CNIPR\n", + " 0\n", + " \n", + " \n", + " 37\n", " Red Hat\n", " 0\n", " \n", + " \n", + " 38\n", + " VHDL articles\n", + " 0\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " name matches\n", - "0 Semantic Scholar Academic Graph 10939\n", - "1 Patents from USPTO 10487\n", - "2 PubMed Central 6050\n", - "3 arXiv full documents 2031\n", - "4 PLOS 570\n", - "5 Annual Reports 194\n", - "6 Cord19 93\n", - "7 arXiv abstracts 70\n", - "8 ESG Reports 33\n", - "9 IBM Redbooks 20\n", - "10 ACL Anthology 9\n", - "11 IPCC 7\n", - "12 Press Release 4\n", - "13 AAAI 2\n", - "14 ClinicalTrials 1\n", - "15 Crossref 0\n", - "16 Patent SMILES 0\n", - "17 Red Hat 0" + " name matches\n", + "0 Patents from USPTO 11755\n", + "1 Semantic Scholar Academic Graph 11648\n", + "2 PubMed Central 6050\n", + "3 Patents from EPO 2929\n", + "4 arXiv full documents 2031\n", + "5 News 1478\n", + "6 PLOS 570\n", + "7 Wikipedia 477\n", + "8 BioRxiv 447\n", + "9 Patents from KIPO 212\n", + "10 Annual Reports 194\n", + "11 IEEE 187\n", + "12 Cord19 93\n", + "13 ChemRxiv 79\n", + "14 arXiv abstracts 75\n", + "15 PubMed Central (PDF) 70\n", + "16 DOAB 62\n", + "17 ESG Reports 33\n", + "18 Patents from JPO 31\n", + "19 SEC Edgar filings 26\n", + "20 MedRxiv 24\n", + "21 IBM Redbooks 20\n", + "22 USPTO patents for NER 17\n", + "23 engrXiv 12\n", + "24 ACL Anthology 9\n", + "25 IPCC 7\n", + "26 OpenCVF 6\n", + "27 AAAI 2\n", + "28 ClinicalTrials 1\n", + "29 OpenStax 1\n", + "30 Crossref 0\n", + "31 FDA Adverse Event Reporting System (FAERS) 0\n", + "32 HBCP Open Access Corpus 0\n", + "33 Material Components 0\n", + "34 NeurIPS 0\n", + "35 Patent SMILES 0\n", + "36 Patents from CNIPR 0\n", + "37 Red Hat 0\n", + "38 VHDL articles 0" ] }, "metadata": {}, @@ -666,7 +706,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "115b4ee0", "metadata": { "execution": { @@ -681,16 +721,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1126,7 +1166,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -1140,7 +1180,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.4" } }, "nbformat": 4, diff --git a/examples/data_query_snippets/snippets.ipynb b/examples/data_query_snippets/snippets.ipynb index a1c9710..5b972a0 100644 --- a/examples/data_query_snippets/snippets.ipynb +++ b/examples/data_query_snippets/snippets.ipynb @@ -52,12 +52,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Import standard dependencies\n", "import pandas as pd\n", + "from datetime import datetime\n", "from numerize.numerize import numerize\n", "from tqdm import tqdm\n", "\n", @@ -121,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -181,7 +182,7 @@ " \"Name\": c.name,\n", " \"Type\": c.metadata.type,\n", " \"Num entries\": numerize(c.documents),\n", - " \"Date\": c.metadata.created.strftime(\"%Y-%m-%d\"),\n", + " \"Date\": datetime.fromisoformat(c.metadata.created).strftime(\"%Y-%m-%d\"),\n", " \"Coords\": f\"{c.source.elastic_id}/{c.source.index_key}\",\n", " }\n", " for c in collections\n", diff --git a/examples/document_conversion_custom_settings/convert_documents_custom.ipynb b/examples/document_conversion_custom_settings/convert_documents_custom.ipynb index 387b556..247e130 100644 --- a/examples/document_conversion_custom_settings/convert_documents_custom.ipynb +++ b/examples/document_conversion_custom_settings/convert_documents_custom.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "72ad8a8e", "metadata": {}, "outputs": [], @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "502cdef8", "metadata": { "ExecuteTime": { @@ -67,11 +67,6 @@ "import deepsearch as ds\n", "from deepsearch.documents.core.models import (\n", " ConversionSettings,\n", - " DefaultConversionModel,\n", - " ProjectConversionModel,\n", - " OCRSettings,\n", - " AlpineOcrEngine,\n", - " AlpineOcrLanguage,\n", ")" ] }, @@ -85,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "391cb5ee", "metadata": {}, "outputs": [], @@ -103,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "ec83eb0b", "metadata": { "ExecuteTime": { @@ -111,37 +106,16 @@ "start_time": "2022-08-02T12:14:25.380757Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:00<00:00, 29.94it/s]\u001b[38;2;15;98;254m \u001b[0m\n", - "Submitting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:09<00:00, 9.70s/it]\u001b[38;2;15;98;254m \u001b[0m\n", - "Converting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:51<00:00, 51.34s/it]\u001b[38;2;15;98;254m \u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Total documents': 1, 'Successfully converted documents': 1}\n" - ] - } - ], + "outputs": [], "source": [ "## Modify conversion pipeline\n", - "cs = ConversionSettings.from_project(api, proj_key=PROJ_KEY)\n", + "cs = ConversionSettings()\n", "\n", "# OCR\n", - "cs.ocr.enabled = True # Enable or disable OCR\n", - "# cs.ocr.merge_mode = \"prioritize-ocr\" # Pick how OCR cells are treated when mixed with programmatic content\n", + "cs.ocr.do_ocr = True # Enable or disable OCR\n", "\n", - "# backends = OCRSettings.get_backends(api) # list OCR backends\n", - "cs.ocr.engine = AlpineOcrEngine() # Pick OCR backend\n", + "cs.ocr.kind = \"tesserocr\" # Pick OCR backend\n", "\n", - "# enable OCR languages\n", - "# cs.ocr.engine = AlpineOcrEngine(languages=[AlpineOcrLanguage.Japanese])\n", "\n", "documents = ds.convert_documents(\n", " api=api,\n", @@ -158,7 +132,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -172,7 +146,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.4" }, "toc": { "base_numbering": 1, @@ -186,11 +160,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": false - }, - "vscode": { - "interpreter": { - "hash": "23663f76e1e243f0a6319b8ef58f504b6b45c83666dfefd3138ba8cf69ab01fa" - } } }, "nbformat": 4, diff --git a/examples/document_conversion_extract_tables/extract_tables.ipynb b/examples/document_conversion_extract_tables/extract_tables.ipynb index 08bd481..7452dda 100644 --- a/examples/document_conversion_extract_tables/extract_tables.ipynb +++ b/examples/document_conversion_extract_tables/extract_tables.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "efd12ce7", "metadata": {}, "outputs": [], @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "f34f9dc1", "metadata": {}, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "246f6ac7", "metadata": {}, "outputs": [], @@ -146,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "2718becb", "metadata": { "scrolled": false @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "a8454a53", "metadata": {}, "outputs": [], @@ -196,20 +196,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "5fc7d427", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:00<00:00, 124.56it/s]\u001b[38;2;15;98;254m\u001b[0m\n", - "Submitting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:11<00:00, 11.18s/it]\u001b[38;2;15;98;254m\u001b[0m\n", - "Converting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:23<00:00, 23.61s/it]\u001b[38;2;15;98;254m\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# Launch the docucment conversion and download the results\n", "documents = ds.convert_documents(\n", @@ -219,135 +209,28 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "3ac07b8c", "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Downloading result: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:00<00:00, 1.23it/s]\u001b[38;2;15;98;254m\u001b[0m\n" - ] - }, - { - "data": { - "text/markdown": [ - "## Table 1 on page 7" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
  component
  worker ML model
Profile A Memory CPU Replica ratio 450 MB 0.4 cores 1 500 MB 0.7 cores 1
Profile B Memory CPU Replica ratio 700 MB 1.2 cores 1 500 MB 0.7 cores 4
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "## Table 1 on page 9" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
profile system state distribution scheme document-level page-level
A idle busy 32.6 ± 1.1 114.9 ± 17.5 26.3 ± 0.9 248.1 ± 17.3
B idle busy 32.6 ± 0.2 109.6 ± 47.7 25.7 ± 0.7 220.4 ± 34.2
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "output_dir = (\n", - " tempfile.mkdtemp()\n", - ") # TODO: switch to tempfile.TemporaryDirectory() and use `with`\n", - "\n", - "documents.download_all(result_dir=output_dir, progress_bar=True)\n", + "with tempfile.TemporaryDirectory() as output_dir:\n", + " documents.download_all(result_dir=output_dir, progress_bar=True)\n", "\n", - "for output_file in Path(output_dir).rglob(\"json*.zip\"):\n", - " with ZipFile(output_file) as archive:\n", - " all_files = archive.namelist()\n", - " for name in all_files:\n", - " if not name.endswith(\".json\"):\n", - " continue\n", - "\n", - " basename = name.rstrip(\".json\")\n", - " doc_jsondata = json.loads(archive.read(f\"{basename}.json\"))\n", + " # group output files and visualize the output\n", + " json_file = list(Path(output_dir).glob(\"*.json\"))[0]\n", "\n", - " visualize_document_tables(doc_jsondata)" + " with open(json_file) as f:\n", + " doc_jsondata = json.loads(f.read())\n", + " visualize_document_tables(doc_jsondata)\n" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -361,12 +244,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "vscode": { - "interpreter": { - "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" - } + "version": "3.10.4" } }, "nbformat": 4, diff --git a/examples/document_conversion_quick_start/convert_documents.ipynb b/examples/document_conversion_quick_start/convert_documents.ipynb index 7598702..f8e014c 100644 --- a/examples/document_conversion_quick_start/convert_documents.ipynb +++ b/examples/document_conversion_quick_start/convert_documents.ipynb @@ -30,18 +30,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "id": "b01a4fd1", "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Project key: 1234567890abcdefghijklmnopqrstvwyz123456\n" - ] - } - ], + "outputs": [], "source": [ "from dsnotebooks.settings import ProjectNotebookSettings\n", "\n", @@ -64,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "id": "502cdef8", "metadata": { "ExecuteTime": { @@ -75,13 +67,10 @@ "outputs": [], "source": [ "import deepsearch as ds\n", - "import json\n", "\n", "from pathlib import Path\n", - "from zipfile import ZipFile\n", "\n", - "from deepsearch.documents.core.export import export_to_markdown, export_to_html\n", - "from IPython.display import display, Markdown, HTML, display_html" + "from IPython.display import display, Markdown" ] }, { @@ -94,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "id": "f44fbf08", "metadata": {}, "outputs": [], @@ -104,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "ec83eb0b", "metadata": { "ExecuteTime": { @@ -112,24 +101,7 @@ "start_time": "2022-08-02T12:14:25.380757Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:00<00:00, 39.65it/s]\u001b[38;2;15;98;254m \u001b[0m\n", - "Submitting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:04<00:00, 4.48s/it]\u001b[38;2;15;98;254m \u001b[0m\n", - "Converting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:36<00:00, 36.90s/it]\u001b[38;2;15;98;254m \u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Total documents': 1, 'Successfully converted documents': 1}\n" - ] - } - ], + "outputs": [], "source": [ "output_dir = Path(\"./converted_docs\")\n", "\n", @@ -138,6 +110,7 @@ " proj_key=PROJ_KEY,\n", " source_path=\"../../data/samples/2206.01062.pdf\",\n", " progress_bar=True,\n", + " export_md=True,\n", ")\n", "documents.download_all(result_dir=output_dir)\n", "info = documents.generate_report(result_dir=output_dir)\n", @@ -146,46 +119,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "id": "382c4869-cca9-43fc-8052-c0ab7e9c175d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "writing converted_docs/2206.01062.md\n", - "writing converted_docs/2206.01062.html\n" - ] - } - ], + "outputs": [], "source": [ - "# Iterare output files and visualize the output\n", - "for output_file in output_dir.rglob(\"json*.zip\"):\n", - " with ZipFile(output_file) as archive:\n", - " all_files = archive.namelist()\n", - " for name in all_files:\n", - " if not name.endswith(\".json\"):\n", - " continue\n", - "\n", - " basename = name.rstrip(\".json\")\n", - " doc_json = json.loads(archive.read(f\"{basename}.json\"))\n", - "\n", - " doc_md = export_to_markdown(doc_json)\n", - "\n", - " ofile = output_dir / f\"{basename}.md\"\n", - " print(f\"writing {ofile}\")\n", - "\n", - " with ofile.open(\"w\") as fw:\n", - " fw.write(doc_md)\n", - "\n", - " doc_html = export_to_html(doc_json)\n", - "\n", - " ofile = output_dir / f\"{basename}.html\"\n", - " print(f\"writing {ofile}\")\n", - "\n", - " with ofile.open(\"w\") as fw:\n", - " fw.write(doc_html)" + "# group output files and visualize the output\n", + "md_files = list(output_dir.glob(\"*.md\"))\n", + "json_files = list(output_dir.glob(\"*.json\"))" ] }, { @@ -196,7 +137,7 @@ "outputs": [], "source": [ "# display last document\n", - "# display(Markdown(doc_md))" + "# display(Markdown(md_files[-1]))" ] }, { @@ -263,7 +204,7 @@ "documents = ds.convert_documents(\n", " api=api,\n", " proj_key=PROJ_KEY,\n", - " urls=\"https://arxiv.org/pdf/2206.00785.pdf\",\n", + " url=\"https://arxiv.org/pdf/2206.00785.pdf\",\n", " progress_bar=True,\n", ")" ] @@ -324,7 +265,7 @@ "outputs": [], "source": [ "# the documents object stores some additional info like:\n", - "documents.statuses, documents.task_ids" + "documents.result, documents.task_id" ] }, { @@ -347,43 +288,7 @@ }, "outputs": [], "source": [ - "# let's create a list of urls we want to convert:\n", - "urls = [\"https://arxiv.org/pdf/2206.00785.pdf\", \"https://arxiv.org/pdf/2206.01062.pdf\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7126c7b1", - "metadata": { - "ExecuteTime": { - "end_time": "2022-08-02T12:16:22.641187Z", - "start_time": "2022-08-02T12:15:19.026607Z" - } - }, - "outputs": [], - "source": [ - "# Process multiple urls\n", - "documents = ds.convert_documents(\n", - " api=api, proj_key=PROJ_KEY, urls=urls, progress_bar=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e630e1e3", - "metadata": { - "ExecuteTime": { - "end_time": "2022-08-02T12:16:28.414101Z", - "start_time": "2022-08-02T12:16:22.643995Z" - } - }, - "outputs": [], - "source": [ - "# as before we can use the documents object to download all jsons. We can also iterate over them individually.\n", - "for doc in documents:\n", - " doc.download(result_dir=result_dir, progress_bar=True)" + "# please use deepsearch UI for multiple conversion" ] }, { @@ -434,41 +339,7 @@ }, "outputs": [], "source": [ - "documents = ds.convert_documents(\n", - " api=api, proj_key=PROJ_KEY, source_path=\"../../data/samples\", progress_bar=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "03d11356", - "metadata": { - "ExecuteTime": { - "end_time": "2022-08-02T12:17:15.703537Z", - "start_time": "2022-08-02T12:17:14.639699Z" - } - }, - "outputs": [], - "source": [ - "info = documents.generate_report(result_dir)\n", - "print(info)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "192ea2e7", - "metadata": { - "ExecuteTime": { - "end_time": "2022-08-02T12:17:19.794439Z", - "start_time": "2022-08-02T12:17:15.705771Z" - } - }, - "outputs": [], - "source": [ - "# let's download all the converted documents:\n", - "documents.download_all(result_dir=result_dir, progress_bar=True)" + "# please use deepsearch UI for multiple conversion" ] }, { @@ -504,7 +375,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -518,7 +389,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.10.4" }, "toc": { "base_numbering": 1, @@ -532,11 +403,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": false - }, - "vscode": { - "interpreter": { - "hash": "23663f76e1e243f0a6319b8ef58f504b6b45c83666dfefd3138ba8cf69ab01fa" - } } }, "nbformat": 4, diff --git a/examples/document_conversion_visualize_bbox/README.md b/examples/document_conversion_visualize_bbox/README.md deleted file mode 100644 index ef64dd9..0000000 --- a/examples/document_conversion_visualize_bbox/README.md +++ /dev/null @@ -1,15 +0,0 @@ -# Document conversion - Visualize bboxes - -In this example we will use the output of the converted document and create an image of elements detected on each page. - -:point_right: Run the [visualize_bbox.ipynb](./visualize_bbox.ipynb) example. - - -### Additional dependencies - -The PDF to image conversion relies on the `pdftoppm` executable of the Poppler library (GPL license) -https://poppler.freedesktop.org/ -The Poppler library can be installed from the most common packaging systems, for example -- On macOS, `brew install poppler` -- On Debian (and Ubuntu), `apt-get install poppler-utils` -- On RHEL, `yum install poppler-utils` diff --git a/examples/document_conversion_visualize_bbox/visualize_bbox.ipynb b/examples/document_conversion_visualize_bbox/visualize_bbox.ipynb deleted file mode 100644 index 08d6b2e..0000000 --- a/examples/document_conversion_visualize_bbox/visualize_bbox.ipynb +++ /dev/null @@ -1,531 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1a5e95e0", - "metadata": {}, - "source": [ - "# Convert and visualize text bounding boxes\n", - "\n", - "\n", - "In this example we will use the output of the converted document and create an image of elements detected on each page.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "5fdc4d28", - "metadata": {}, - "source": [ - "### System dependencies\n", - "This script is converting a PDF document with Deep Search and exports the figures into PNG files.\n", - "\n", - "The PDF to image conversion relies on the `pdftoppm` executable of the Poppler library (GPL license)\n", - "https://poppler.freedesktop.org/\n", - "The Poppler library can be installed from the most common packaging systems, for example\n", - "- On macOS, `brew install poppler`\n", - "- On Debian (and Ubuntu), `apt-get install poppler-utils`\n", - "- On RHEL, `yum install poppler-utils`\n" - ] - }, - { - "cell_type": "markdown", - "id": "142d6129", - "metadata": {}, - "source": [ - "### Notebooks parameters\n", - "\n", - "The following block defines the parameters used to execute the notebook\n", - "\n", - "- `INPUT_FILE`: the input PDF to converted and analyzed\n", - "- `SHOW_PDF_IMAGE`: if enabled, the background will contain the rendered PDF page\n", - "- `SHOW_CLUSTER_BOXES`: if enabled, the cluster boxes will be visualized\n", - "- `SHOW_TEXT_CELLS_BOXES`: if enabled, the PDF raw text cells will be visualized\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "efd12ce7", - "metadata": {}, - "outputs": [], - "source": [ - "from dsnotebooks.settings import ProjectNotebookSettings\n", - "from pathlib import Path\n", - "\n", - "# notebook settings auto-loaded from .env / env vars\n", - "notebook_settings = ProjectNotebookSettings()\n", - "\n", - "PROFILE_NAME = notebook_settings.profile # the profile to use\n", - "PROJ_KEY = notebook_settings.proj_key # the project to use\n", - "# INPUT_FILE = Path(\"../../data/samples/2206.01062.pdf\")\n", - "INPUT_FILE = Path(\"../../data/samples/2206.00785.pdf\")\n", - "\n", - "# Visualization options. Which layers should be included in the visualization\n", - "SHOW_PDF_IMAGE = True\n", - "SHOW_CLUSTER_BOXES = True\n", - "SHOW_TEXT_CELLS_BOXES = True" - ] - }, - { - "cell_type": "markdown", - "id": "d12e55af", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "The following blocks define the helper functions used for the visualization of the output" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f34f9dc1", - "metadata": {}, - "outputs": [], - "source": [ - "# Import standard dependenices\n", - "import json\n", - "import tempfile\n", - "import typing\n", - "from zipfile import ZipFile\n", - "\n", - "# Dependencies for the visualization\n", - "import math\n", - "from PIL import Image, ImageDraw\n", - "from IPython.display import display, Markdown, HTML, display_html\n", - "import base64\n", - "import io\n", - "from subprocess import check_call, CalledProcessError\n", - "\n", - "# Import the deepsearch-toolkit\n", - "import deepsearch as ds" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "659da821", - "metadata": {}, - "outputs": [], - "source": [ - "def resolve_item(raw_item, doc):\n", - " \"\"\"\n", - " Resolved jsonref used in the main-text document items\n", - " \"\"\"\n", - " if not \"$ref\" in raw_item:\n", - " return raw_item\n", - " parts = raw_item[\"$ref\"].split(\"/\")\n", - " item = doc\n", - " for i, k in enumerate(parts[1:]):\n", - " if isinstance(item, typing.Mapping) and not k in item:\n", - " print(f\"k={k} not found\")\n", - " return {}\n", - "\n", - " if isinstance(item, typing.List):\n", - " try:\n", - " k = int(k)\n", - " except:\n", - " print(f\"cannot cast {k} to int\")\n", - " return {}\n", - " item = item[k]\n", - " return item" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5e27677d", - "metadata": {}, - "outputs": [], - "source": [ - "def page_elements_from_json_document(doc_jsondata):\n", - " \"\"\"\n", - " Collect all the document elements with a bounding box\n", - " and organize them per page.\n", - "\n", - " Parameters\n", - " ----------\n", - " doc_jsondata :\n", - " Converted document\n", - "\n", - " Returns\n", - " -------\n", - " Dict[PageNumber, List[BoxDescription]]\n", - " A dictionary mapping the page number to a list of the\n", - " document boxes on that page.\n", - " BoxDescription will contain the page, type, and bbox.\n", - " \"\"\"\n", - " clusters = {}\n", - " for raw_item in doc_jsondata[\"main-text\"]:\n", - " item = resolve_item(raw_item, doc_jsondata)\n", - " if not \"prov\" in item:\n", - " print(\"PROV not found\")\n", - " print(item)\n", - " continue\n", - " page = item[\"prov\"][0][\"page\"]\n", - " item_type = item[\"type\"]\n", - " clusters.setdefault(page, []).append(\n", - " {\n", - " \"page\": page,\n", - " \"type\": item_type,\n", - " \"bbox\": item[\"prov\"][0][\"bbox\"],\n", - " }\n", - " )\n", - " return clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "74d6c68d", - "metadata": {}, - "outputs": [], - "source": [ - "def page_elements_from_text_cells(doc_cellsdata):\n", - " \"\"\"\n", - " Collect all the text cells with a bounding box and\n", - " organize them per page.\n", - "\n", - " Parameters\n", - " ----------\n", - " doc_cellsdata :\n", - " Cells document provided by the Deep Search conversion\n", - "\n", - " Returns\n", - " -------\n", - " Dict[PageNumber, List[BoxDescription]]\n", - " A dictionary mapping the page number to a list of the\n", - " document boxes on that page.\n", - " BoxDescription will contain the page, type, and bbox.\n", - " \"\"\"\n", - "\n", - " cells = {}\n", - " for item in doc_cellsdata[\"cells\"][\"data\"]:\n", - " page = item[0] + 1\n", - " item_type = item[5]\n", - " cells.setdefault(page, []).append(\n", - " {\n", - " \"page\": page,\n", - " \"type\": item_type,\n", - " \"bbox\": item[1:5],\n", - " }\n", - " )\n", - "\n", - " return cells" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7d0aa65c", - "metadata": {}, - "outputs": [], - "source": [ - "# The\n", - "labels_colors_clusters = {\n", - " \"table\": ((240, 128, 128, 100), (255, 0, 0)),\n", - " \"caption\": ((243, 156, 18, 100), (255, 0, 0)),\n", - " \"citation\": ((14, 210, 234, 100), (255, 0, 0)),\n", - " \"picture\": ((255, 236, 204, 100), (255, 0, 0)),\n", - " \"formula\": ((128, 139, 150, 100), (255, 0, 0)),\n", - " \"subtitle-level-1\": ((204, 51, 102, 100), (255, 0, 0)),\n", - " \"paragraph\": ((234, 234, 43, 100), (255, 0, 0)),\n", - "}\n", - "\n", - "labels_colors_cells = {\n", - " \"table\": ((240, 128, 128, 100), (0, 0, 0, 0)),\n", - " \"caption\": ((243, 156, 18, 100), (0, 0, 0, 0)),\n", - " \"citation\": ((14, 210, 234, 100), (0, 0, 0, 0)),\n", - " \"picture\": ((255, 236, 204, 100), (0, 0, 0, 0)),\n", - " \"formula\": ((128, 139, 150, 100), (0, 0, 0, 0)),\n", - " \"subtitle-level-1\": ((204, 51, 102, 100), (0, 0, 0, 0)),\n", - " \"paragraph\": ((234, 234, 43, 100), (0, 0, 0, 0)),\n", - "}\n", - "\n", - "\n", - "def draw_boxes(img, dims, boxes, colors_map={}):\n", - " \"\"\"\n", - " Draw bounding boxes on the input PIL Image `img`\n", - "\n", - " Parameters\n", - " ----------\n", - " img : Image\n", - " PIL image on which the boxes are drawn.\n", - " dims : List[int]\n", - " Page dimensions as `dims=(width, height)`.\n", - " boxes : List[dict]\n", - " List of boxes to draw, in the format of `{type: str, bbox: List[int]`}.\n", - " colors_map : Dict[str, Tuple[RGBA, RGBA]]\n", - " Dictionary with the color to use for the bbox, the map value is a tuple\n", - " of style for the fill color and for the line color.\n", - " Colors can be defined as a list of RGB or RGBA, with values in the range\n", - " 0 to 255.\n", - " \"\"\"\n", - " drw = ImageDraw.Draw(img, \"RGBA\")\n", - " for cluster in boxes:\n", - "\n", - " bbox = cluster[\"bbox\"]\n", - " rect = [\n", - " round(bbox[0]),\n", - " round(dims[1] - bbox[3]),\n", - " round(bbox[2]),\n", - " round(dims[1] - bbox[1]),\n", - " ]\n", - "\n", - " c_fill, c_outline = colors_map.get(\n", - " cluster[\"type\"].lower(), ((128, 128, 128, 100), (0, 0, 0, 0))\n", - " )\n", - " drw.rectangle(rect, outline=c_outline, fill=c_fill)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "20e546cb", - "metadata": {}, - "outputs": [], - "source": [ - "def pdf_to_page_image(pdf_filename, page, resolution=72):\n", - " \"\"\"\n", - " Convert the page number `page` of the PDF document to an image\n", - "\n", - " Parameters\n", - " ----------\n", - " pdf_filename : Path\n", - " Input PDF file.\n", - " page : int\n", - " Page number where the bounding box is located.\n", - " resolution : int, Default=72\n", - " Resolution of the extracted image.\n", - "\n", - " Returns\n", - " -------\n", - " Image\n", - " PIL Image of the PDF page.\n", - " \"\"\"\n", - "\n", - " with tempfile.NamedTemporaryFile() as output_file:\n", - " output_filename = output_file.name\n", - " cmd = [\n", - " \"pdftoppm\",\n", - " \"-png\",\n", - " \"-singlefile\",\n", - " \"-f\",\n", - " str(page),\n", - " \"-l\",\n", - " str(page),\n", - " \"-cropbox\",\n", - " \"-r\",\n", - " str(resolution),\n", - " pdf_filename,\n", - " output_filename,\n", - " ]\n", - " try:\n", - " check_call(cmd)\n", - " except CalledProcessError as cpe:\n", - " raise RuntimeError(\n", - " f\"PDFTOPPM PROCESSING ERROR. Exited with: {cpe.returncode}\"\n", - " ) from cpe\n", - " png_file = output_filename + \".png\"\n", - " img = Image.open(png_file)\n", - " return img" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2718becb", - "metadata": {}, - "outputs": [], - "source": [ - "def visualize_document_bboxes(doc_jsondata, doc_cellsdata, ncols=3):\n", - " \"\"\"\n", - " Visualize the document pages overlaying the PDF image with the\n", - " bounding boxes of the text cells and the segmentation clusters.\n", - "\n", - " Parameters\n", - " ----------\n", - " doc_jsondata :\n", - " Converted document\n", - " doc_cellsdata :\n", - " Cells document provided by the Deep Search conversion\n", - " ncols : int, Default=3\n", - " Number of columns in the display table.\n", - " \"\"\"\n", - "\n", - " clusters = page_elements_from_json_document(doc_jsondata)\n", - " cells = page_elements_from_text_cells(doc_cellsdata)\n", - " pages_to_dims = {dims[\"page\"]: dims for dims in doc_jsondata[\"page-dimensions\"]}\n", - "\n", - " output_html = \"\"\n", - " for i, page in enumerate(sorted(clusters.keys())):\n", - " dims = pages_to_dims[page][\"width\"], pages_to_dims[page][\"height\"]\n", - " page_cells = cells[page]\n", - " page_clusters = clusters[page]\n", - "\n", - " if SHOW_PDF_IMAGE:\n", - " img = pdf_to_page_image(INPUT_FILE, page=page)\n", - " img = img.resize((math.ceil(dims[0]), math.ceil(dims[1])))\n", - " else:\n", - " img = Image.new(\n", - " \"RGB\", (math.ceil(dims[0]), math.ceil(dims[1])), (255, 255, 255)\n", - " )\n", - " img = img.resize((math.ceil(dims[0]), math.ceil(dims[1])))\n", - "\n", - " # Draw page rectangle\n", - " drw = ImageDraw.Draw(img)\n", - " drw.rectangle([0, 0, dims[0] - 1, dims[1] - 1], outline=(0, 0, 0))\n", - "\n", - " # Draw bounding boxes\n", - " if SHOW_TEXT_CELLS_BOXES:\n", - " draw_boxes(img, dims, page_cells, labels_colors_cells)\n", - " if SHOW_CLUSTER_BOXES:\n", - " draw_boxes(img, dims, page_clusters, labels_colors_clusters)\n", - "\n", - " if i == 0:\n", - " output_html += \"\"\n", - " elif i % ncols == 0:\n", - " output_html += \"\"\n", - "\n", - " buffer = io.BytesIO()\n", - " img.save(buffer, format=\"PNG\")\n", - " img_str = base64.b64encode(buffer.getvalue()).decode(\"utf8\")\n", - "\n", - " output_html += \"\"\n", - "\n", - " output_html += \"
\"\n", - " output_html += f\"Page {page}
\"\n", - " output_html += f\"\"\n", - " output_html += \"
\"\n", - " display(HTML(output_html))" - ] - }, - { - "cell_type": "markdown", - "id": "499aad65", - "metadata": {}, - "source": [ - "## Document conversion and visualization with Deep Search" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a8454a53", - "metadata": {}, - "outputs": [], - "source": [ - "# Connect to Deep Search\n", - "api = ds.CpsApi.from_env(profile_name=PROFILE_NAME)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5fc7d427", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:00<00:00, 232.18it/s]\u001b[38;2;15;98;254m \u001b[0m\n", - "Submitting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:05<00:00, 5.16s/it]\u001b[38;2;15;98;254m \u001b[0m\n", - "Converting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:23<00:00, 23.38s/it]\u001b[38;2;15;98;254m \u001b[0m\n" - ] - } - ], - "source": [ - "# Launch the document conversion\n", - "documents = ds.convert_documents(\n", - " api=api, proj_key=PROJ_KEY, source_path=INPUT_FILE, progress_bar=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "fba35c81-d20d-4109-8fda-41a796313610", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Downloading result: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:00<00:00, 1.40it/s]\u001b[38;2;15;98;254m \u001b[0m\n" - ] - } - ], - "source": [ - "# Download results\n", - "output_dir = (\n", - " tempfile.mkdtemp()\n", - ") # TODO: switch to tempfile.TemporaryDirectory() and use `with`\n", - "documents.download_all(result_dir=output_dir, progress_bar=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "bea81486-7159-4360-9845-54716c96b262", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Iterare output files and visualize the output\n", - "for output_file in Path(output_dir).rglob(\"json*.zip\"):\n", - " with ZipFile(output_file) as archive:\n", - " all_files = archive.namelist()\n", - " for name in all_files:\n", - " if not name.endswith(\".json\"):\n", - " continue\n", - "\n", - " basename = name.rstrip(\".json\")\n", - " doc_jsondata = json.loads(archive.read(f\"{basename}.json\"))\n", - " doc_cellsdata = json.loads(archive.read(f\"{basename}.cells\"))\n", - "\n", - " visualize_document_bboxes(doc_jsondata, doc_cellsdata)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "vscode": { - "interpreter": { - "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/integration_argilla/argilla_upload.ipynb b/examples/integration_argilla/argilla_upload.ipynb index 8497f08..c2c4b89 100644 --- a/examples/integration_argilla/argilla_upload.ipynb +++ b/examples/integration_argilla/argilla_upload.ipynb @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "83bf44a3", "metadata": {}, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "e4fef9b2", "metadata": {}, "outputs": [], @@ -95,61 +95,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "d6caf04b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting en-core-web-sm==3.6.0\n", - 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"\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", - "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n", - "You can now load the package via spacy.load('en_core_web_sm')\n" - ] - } - ], + "outputs": [], "source": [ "# Download the spaCy model\n", "!python -m spacy download {SPACY_MODEL}" @@ -157,15 +106,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "8c815fe1", "metadata": {}, "outputs": [], "source": [ + "from typing import Optional\n", + "\n", "class DocTextSegment(BaseModel):\n", " page: int # page number\n", " idx: int # index of text segment in the document\n", - " title: str # title of the document\n", + " title: Optional[str] = None # title of the document\n", " name: str # flavour of text segment\n", " type: str # type of text segment\n", " text: str # content of the text segment\n", @@ -187,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "904944e4", "metadata": {}, "outputs": [], @@ -198,20 +149,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "e88e025b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:00<00:00, 214.55it/s]\u001b[38;2;15;98;254m \u001b[0m\n", - "Submitting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:04<00:00, 4.34s/it]\u001b[38;2;15;98;254m \u001b[0m\n", - "Converting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:17<00:00, 17.77s/it]\u001b[38;2;15;98;254m \u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# Launch the docucment conversion and download the results\n", "documents = ds.convert_documents(\n", @@ -221,47 +162,21 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "ded25c58", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Downloading result: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:00<00:00, 1.46it/s]\u001b[38;2;15;98;254m \u001b[0m" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 documents have been loaded after conversion.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "output_dir = tempfile.mkdtemp()\n", "\n", "documents.download_all(result_dir=output_dir, progress_bar=True)\n", "\n", "converted_docs = {}\n", - "for output_file in Path(output_dir).rglob(\"json*.zip\"):\n", - " with ZipFile(output_file) as archive:\n", - " all_files = archive.namelist()\n", - " for name in all_files:\n", - " if not name.endswith(\".json\"):\n", - " continue\n", - "\n", - " doc_jsondata = json.loads(archive.read(name))\n", - " converted_docs[f\"{output_file}//{name}\"] = doc_jsondata\n", + "# group output files and visualize the output\n", + "for output_file in Path(output_dir).rglob(\"*.json\"):\n", + " with open(output_file, 'r') as file:\n", + " doc_jsondata = json.loads(file.read())\n", + " converted_docs[f\"{output_file}//{output_file.name}\"] = doc_jsondata\n", "\n", "print(f\"{len(converted_docs)} documents have been loaded after conversion.\")" ] @@ -276,18 +191,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "325b3f75", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "133 text segments got extracted from the document\n" - ] - } - ], + "outputs": [], "source": [ "text_segments = []\n", "for doc in converted_docs.values():\n", @@ -329,7 +236,7 @@ "outputs": [], "source": [ "# Initialize the Argilla SDK\n", - "rg.init(api_url=ARGILLA_API_URL, api_key=ARGILLA_API_KEY)\n", + "client = rg.Argilla(api_url=ARGILLA_API_URL, api_key=ARGILLA_API_KEY)\n", "\n", "# Initialize the spaCy NLP model for the tokenization of the text\n", "nlp = spacy.load(\"en_core_web_sm\")" @@ -347,12 +254,12 @@ "records_text_classificaiton = []\n", "for segment in text_segments:\n", " records_text_classificaiton.append(\n", - " rg.TextClassificationRecord(\n", - " text=segment.text,\n", + " rg.Record(\n", + " fields={\"text\":segment.text},\n", " vectors={},\n", - " prediction=segment.text_classification,\n", + " suggestions=segment.text_classification,\n", " metadata=segment.dict(\n", - " exclude={\"text\", \"text_classification\", \"token_classification\"}\n", + " exclude={\"text\", \"text_classification\", \"token_classification\", \"idx\", \"title\", \"name\"}\n", " ),\n", " )\n", " )" @@ -366,7 +273,24 @@ "outputs": [], "source": [ "# Submit text for classification\n", - "rg.log(records_text_classificaiton, name=f\"{ARGILLA_DATASET}-text\")" + "settings = rg.Settings(\n", + " fields=[\n", + " rg.TextField(name=\"text\"),\n", + " ],\n", + " questions=[\n", + " rg.TextQuestion(name=\"text_generation\"),\n", + " ],\n", + " metadata=[\n", + " rg.TermsMetadataProperty(name=\"type\"),\n", + " rg.TermsMetadataProperty(name=\"page\"),\n", + " ],\n", + " vectors=[\n", + " rg.VectorField(name='mini-lm-sentence-transformers', dimensions=384),\n", + " ],\n", + ")\n", + "dataset = rg.Dataset(name=f\"{ARGILLA_DATASET}-text\", workspace=\"argilla\", settings=settings)\n", + "dataset.create()\n", + "dataset.records.log(records_text_classificaiton)" ] }, { @@ -380,14 +304,14 @@ "\n", "records_token_classificaiton = []\n", "for segment in text_segments:\n", + " text = [\" \".join(token.text) for token in nlp(segment.text)]\n", " records_token_classificaiton.append(\n", - " rg.TokenClassificationRecord(\n", - " text=segment.text,\n", - " tokens=[token.text for token in nlp(segment.text)],\n", - " prediction=segment.token_classification,\n", + " rg.Record(\n", + " fields={\"text\": \" \".join(str(x) for x in text)},\n", " vectors={},\n", + " suggestions=segment.token_classification,\n", " metadata=segment.dict(\n", - " exclude={\"text\", \"text_classification\", \"token_classification\"}\n", + " exclude={\"text\", \"text_classification\", \"token_classification\", \"idx\", \"title\", \"name\"}\n", " ),\n", " )\n", " )" @@ -401,7 +325,25 @@ "outputs": [], "source": [ "# Submit tokens for classification\n", - "rg.log(records_token_classificaiton, name=f\"{ARGILLA_DATASET}-token\")" + "# Submit text for classification\n", + "settings = rg.Settings(\n", + " fields=[\n", + " rg.TextField(name=\"text\"),\n", + " ],\n", + " questions=[\n", + " rg.TextQuestion(name=\"text_generation\"),\n", + " ],\n", + " metadata=[\n", + " rg.TermsMetadataProperty(name=\"type\"),\n", + " rg.TermsMetadataProperty(name=\"page\"),\n", + " ],\n", + " vectors=[\n", + " rg.VectorField(name='mini-lm-sentence-transformers', dimensions=384),\n", + " ],\n", + ")\n", + "dataset = rg.Dataset(name=f\"{ARGILLA_DATASET}-token\", workspace=\"argilla\", settings=settings)\n", + "dataset.create()\n", + "dataset.records.log(records_token_classificaiton)" ] }, { @@ -419,7 +361,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -433,7 +375,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.4" } }, "nbformat": 4, diff --git a/examples/kg_download_quick_start/kg_download_quick_start.ipynb b/examples/kg_download_quick_start/kg_download_quick_start.ipynb old mode 100644 new mode 100755 index bcdc525..a864c4c --- a/examples/kg_download_quick_start/kg_download_quick_start.ipynb +++ b/examples/kg_download_quick_start/kg_download_quick_start.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "01767c40", "metadata": {}, "outputs": [], @@ -42,7 +42,7 @@ "from dsnotebooks.settings import KGProjectNotebookSettings\n", "\n", "# notebook settings auto-loaded from .env / env vars\n", - "notebook_settings = KGProjectNotebookSettings()\n", + "notebook_settings = KGProjectNotebookSettings(kg_key=\"\")\n", "\n", "PROFILE_NAME = notebook_settings.profile # the profile to use\n", "PROJECT_KEY = notebook_settings.proj_key\n", @@ -60,19 +60,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "1b897f31", "metadata": {}, "outputs": [], "source": [ "# Import standard dependencies\n", "import os\n", - "import ssl\n", "import json\n", "import pandas as pd\n", "from tqdm import tqdm\n", "import tarfile\n", - "from urllib.request import urlopen\n", + "import requests\n", "\n", "# Dependencies related to PyTorch Geometric\n", "import torch\n", @@ -83,13 +82,14 @@ "from IPython.display import display\n", "\n", "# Import deepsearch-toolkit\n", + "import deepsearch as ds\n", "from deepsearch.core.client import DeepSearchConfig\n", "from deepsearch.cps.client.api import CpsApi, CpsApiClient" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "a282398b", "metadata": {}, "outputs": [], @@ -124,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "004e7de9", "metadata": {}, "outputs": [], @@ -146,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "d5c38ba8", "metadata": {}, "outputs": [], @@ -166,31 +166,19 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "f8b24fe9", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌| 99.79631035475828/100 [00:41<00:00, 2.60it/s]/Users/shubham/Experiments/KGDownload-Example/deepsearch-examples/.venv/lib/python3.10/site-packages/tqdm/std.py:533: TqdmWarning: clamping frac to range [0, 1]\n", - " full_bar = Bar(frac,\n", - "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100.00000000004945/100 [00:41<00:00, 2.39it/s]\n" - ] - } - ], - "source": [ - "# Download the knowledge graph using urlopen\n", + "outputs": [], + "source": [ + "# Download the knowledge graph using requests\n", "zipped_file_path = os.path.join(BASE_DIR, \"kg_data.tar.gz\")\n", - "context = ssl.create_default_context()\n", - "context.check_hostname = False\n", - "context.verify_mode = ssl.CERT_NONE\n", "\n", - "with open(zipped_file_path, \"wb+\") as download_file, urlopen(\n", - " download_url, context=context\n", + "\n", + "with open(zipped_file_path, \"wb+\") as download_file, requests.get(\n", + " download_url\n", ") as response:\n", - " content_length = int(response.getheader(\"content-length\"))\n", + " content_length = int(response.headers.get(\"content-length\"))\n", " with tqdm(total=100, position=0) as pbar:\n", " for line in response:\n", " download_file.write(line)\n", @@ -208,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "c1dada55", "metadata": {}, "outputs": [], @@ -231,50 +219,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "3f9cb932", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['_edges.jsonl',\n", - " 'abbreviation.jsonl',\n", - " 'affiliations.jsonl',\n", - " 'authors.jsonl',\n", - " 'classifications.jsonl',\n", - " 'documents.jsonl',\n", - " 'identifiers.jsonl',\n", - " 'images.jsonl',\n", - " 'material-attribute.jsonl',\n", - " 'material-class.jsonl',\n", - " 'material-part.jsonl',\n", - " 'material-to-abbreviation.jsonl',\n", - " 'material-to-material-class.jsonl',\n", - " 'material-to-property.jsonl',\n", - " 'material.jsonl',\n", - " 'model-type.jsonl',\n", - " 'property-to-property-value.jsonl',\n", - " 'property-value-type.jsonl',\n", - " 'property-value.jsonl',\n", - " 'property.jsonl',\n", - " 'publishers.jsonl',\n", - " 'smiles.jsonl',\n", - " 'statement.jsonl',\n", - " 'tables.jsonl',\n", - " 'terms.jsonl',\n", - " 'text-type.jsonl',\n", - " 'texts.jsonl',\n", - " 'unit-si.jsonl',\n", - " 'unit.jsonl',\n", - " 'year-month.jsonl',\n", - " 'year.jsonl']" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Get a list of all the files in the unzipped data\n", "files = list(os.walk(os.path.join(BASE_DIR, \"unzipped_data\")))[0][2]\n", @@ -291,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "7408bb61", "metadata": {}, "outputs": [], @@ -310,37 +258,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "b6293224", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "_id 6411a23382b445f4a85ab6b9\n", - "_hash 6f9f25a0a0ef3246314db75f7184489c\n", - "_name Pb iodide\n", - "_synonyms [Pb iodide]\n", - "_type material\n", - "identifiers [{'_name': 'ent_id#64efbff3', 'type': 'ent_id'...\n", - "labels [non-atomic]\n", - "model [IUPACNameAnnotator]\n", - "names [{'_name': 'chemical_name#lead iodide', 'type'...\n", - "prov [text, data]\n", - "flags {'aggregations': {'@smi': True, 'model': True}}\n", - "coor [0, 8, 13, 15, 17, 21, 24, 31, 2, 1]\n", - "doping-parameter NaN\n", - "element-parameter NaN\n", - "Name: 0, dtype: object" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Show the first record in the materials file\n", - "materials = jsonl2df(os.path.join(BASE_DIR, \"unzipped_data\", \"material.jsonl\"))\n", + "outputs": [], + "source": [ + "# Show the first record in the documents file\n", + "materials = jsonl2df(os.path.join(BASE_DIR, \"unzipped_data\", \"documents.jsonl\"))\n", "display(materials.iloc[0])" ] }, @@ -359,116 +283,24 @@ }, { "cell_type": "code", - 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" - ], - "text/plain": [ - " source_collection target_collection source_hash \\\n", - "0 documents texts c4d297ab8e33f5419e2bf081cf8b9f2c \n", - "1 documents texts 6db1ccc0bdb23fe0256571ca6e7020b0 \n", - "2 documents texts 6db1ccc0bdb23fe0256571ca6e7020b0 \n", - "3 documents texts 6db1ccc0bdb23fe0256571ca6e7020b0 \n", - "4 documents texts c4d297ab8e33f5419e2bf081cf8b9f2c \n", - "\n", - " target_hash symmetric \n", - "0 ac6753cb167f782854d66f49ef8a47fc True \n", - "1 d8843473fe754654733260c16936f051 True \n", - "2 91d5b26959546063b595ea75f7b99bde True \n", - "3 4435cbe3ce0b1c8feea9a593f1e31c1d True \n", - "4 8c3f5d93479d56f7a204c65fe1a3daaf True " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Show the first few edges\n", "edges = jsonl2df(os.path.join(BASE_DIR, \"unzipped_data\", \"_edges.jsonl\"))\n", - "edges = edges[\n", - " [\n", - " \"source_collection\",\n", - " \"target_collection\",\n", - " \"source_hash\",\n", - " \"target_hash\",\n", - " \"symmetric\",\n", + "if len(edges) > 0:\n", + " edges = edges[\n", + " [\n", + " \"source_collection\",\n", + " \"target_collection\",\n", + " \"source_hash\",\n", + " \"target_hash\",\n", + " \"symmetric\",\n", + " ]\n", " ]\n", - "]\n", - "display(edges.head())" + " display(edges.head())" ] }, { @@ -493,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "396ab9c2", "metadata": {}, "outputs": [], @@ -513,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "02fc20f4", "metadata": {}, "outputs": [], @@ -524,12 +356,13 @@ "}\n", "\n", "for nodetype in nodetypes:\n", - " data = jsonl2df(nodetypes[nodetype])\n", - " hetero_kg[nodetype].x = torch.eye(data.shape[0])\n", - " hetero_kg[nodetype][\"_hash\"] = dict(\n", - " (_hash, _idx) for _idx, _hash in enumerate(data[\"_hash\"].to_list())\n", - " )\n", - " hetero_kg[nodetype][\"_name\"] = data[\"_name\"].to_list()" + " if os.path.isfile(nodetypes[nodetype]):\n", + " data = jsonl2df(nodetypes[nodetype])\n", + " hetero_kg[nodetype].x = torch.eye(data.shape[0])\n", + " hetero_kg[nodetype][\"_hash\"] = dict(\n", + " (_hash, _idx) for _idx, _hash in enumerate(data[\"_hash\"].to_list())\n", + " )\n", + " hetero_kg[nodetype][\"_name\"] = data[\"_name\"].to_list()" ] }, { @@ -544,35 +377,36 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "21fd8c27", "metadata": {}, "outputs": [], "source": [ "# Find the relevant edges\n", "edges = jsonl2df(os.path.join(BASE_DIR, \"unzipped_data\", \"_edges.jsonl\"))\n", - "edges = edges[\n", - " (edges.source_collection == \"material\") & (edges.target_collection == \"property\")\n", - "]\n", - "edges = [edges[\"source_hash\"].to_list(), edges[\"target_hash\"].to_list()]\n", - "\n", - "# Create the edge index\n", - "edge_index = []\n", - "for hash_mat, hash_prop in zip(*edges):\n", - " edge_index.append(\n", - " [\n", - " hetero_kg[\"material\"][\"_hash\"][hash_mat],\n", - " hetero_kg[\"property\"][\"_hash\"][hash_prop],\n", - " ]\n", + "if len(edges)>0:\n", + " edges = edges[\n", + " (edges.source_collection == \"material\") & (edges.target_collection == \"property\")\n", + " ]\n", + " edges = [edges[\"source_hash\"].to_list(), edges[\"target_hash\"].to_list()]\n", + "\n", + " # Create the edge index\n", + " edge_index = []\n", + " for hash_mat, hash_prop in zip(*edges):\n", + " edge_index.append(\n", + " [\n", + " hetero_kg[\"material\"][\"_hash\"][hash_mat],\n", + " hetero_kg[\"property\"][\"_hash\"][hash_prop],\n", + " ]\n", + " )\n", + "\n", + " # Add edge index to the KG\n", + " hetero_kg[\"material\", \"mat2prop\", \"property\"].edge_index = (\n", + " torch.tensor(edge_index).long().t()\n", " )\n", "\n", - "# Add edge index to the KG\n", - "hetero_kg[\"material\", \"mat2prop\", \"property\"].edge_index = (\n", - " torch.tensor(edge_index).long().t()\n", - ")\n", - "\n", - "# Make the graph undirected\n", - "hetero_kg = ToUndirected()(hetero_kg)" + " # Make the graph undirected\n", + " hetero_kg = ToUndirected()(hetero_kg)" ] }, { @@ -585,26 +419,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "6e1e2e48", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of nodes\n", - "\tmaterial -> 9040\n", - "\tproperty -> 16581\n", - "Total number of nodes: 25621\n", - "\n", - "Number of edges\n", - "\t('material', 'mat2prop', 'property') -> 12752\n", - "\t('property', 'rev_mat2prop', 'material') -> 12752\n", - "Total number of edges: 25504\n" - ] - } - ], + "outputs": [], "source": [ "# Summarize the KG\n", "print(\"Number of nodes\")\n", @@ -638,127 +456,45 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "90d1ec78", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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materialProperty0Property1Property2Property3
0perovskite/Siband gapenergy yieldrough surfacetemperature
1O(2) Ti(1)optical bandgappH valueambient temperatureelectrical conductivity
2A(1) I(3) M(1) Pb(1)free energy surfacekinetic energydomain sizeoptical bandgap
3O(1) Zn(1)density-of-stateshigh temperaturework functionsymmetry-class
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" - ], - "text/plain": [ - " material Property0 Property1 \\\n", - "0 perovskite/Si band gap energy yield \n", - "1 O(2) Ti(1) optical bandgap pH value \n", - "2 A(1) I(3) M(1) Pb(1) free energy surface kinetic energy \n", - "3 O(1) Zn(1) density-of-states high temperature \n", - "\n", - " Property2 Property3 \n", - "0 rough surface temperature \n", - "1 ambient temperature electrical conductivity \n", - "2 domain size optical bandgap \n", - "3 work function symmetry-class " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Select materials to display\n", - "materials = [\"perovskite/Si\", \"O(2) Ti(1)\", \"A(1) I(3) M(1) Pb(1)\", \"O(1) Zn(1)\"]\n", - "mat_idx = [hetero_kg[\"material\"][\"_name\"].index(mat) for mat in materials]\n", + "if hetero_kg[\"material\"]:\n", + " materials = [\"perovskite/Si\", \"O(2) Ti(1)\", \"A(1) I(3) M(1) Pb(1)\", \"O(1) Zn(1)\"]\n", + " mat_idx = [hetero_kg[\"material\"][\"_name\"].index(mat) for mat in materials]\n", + "\n", + " # Get properties corresponding to each material\n", + " properties = dict()\n", + " for m_idx, material in zip(mat_idx, materials):\n", + " current_edges = (\n", + " hetero_kg[\"material\", \"mat2prop\", \"property\"].edge_index[0, :] == m_idx\n", + " )\n", + " prop_idx = hetero_kg[\"material\", \"mat2prop\", \"property\"].edge_index[\n", + " 1, current_edges\n", + " ]\n", + " properties[material] = [\n", + " hetero_kg[\"property\"][\"_name\"][idx] for idx in prop_idx.tolist()\n", + " ]\n", "\n", - "# Get properties corresponding to each material\n", - "properties = dict()\n", - "for m_idx, material in zip(mat_idx, materials):\n", - " current_edges = (\n", - " hetero_kg[\"material\", \"mat2prop\", \"property\"].edge_index[0, :] == m_idx\n", - " )\n", - " prop_idx = hetero_kg[\"material\", \"mat2prop\", \"property\"].edge_index[\n", - " 1, current_edges\n", - " ]\n", - " properties[material] = [\n", - " hetero_kg[\"property\"][\"_name\"][idx] for idx in prop_idx.tolist()\n", - " ]\n", + " # Show up to four randomly chosen properties for each material\n", + " df = pd.DataFrame()\n", + " for mat, prop in properties.items():\n", + " # Restrict to four properties\n", + " if len(prop) > 4:\n", + " prop = [prop[idx] for idx in torch.randperm(len(prop)).tolist()[:4]]\n", "\n", - "# Show up to four randomly chosen properties for each material\n", - "df = pd.DataFrame()\n", - "for mat, prop in properties.items():\n", - " # Restrict to four properties\n", - " if len(prop) > 4:\n", - " prop = [prop[idx] for idx in torch.randperm(len(prop)).tolist()[:4]]\n", - "\n", - " # Add the row to the dataframe\n", - " curr_dict = dict(\n", - " [(\"material\", [mat])]\n", - " + [(f\"Property{p_idx}\", [p]) for p_idx, p in enumerate(prop)]\n", - " )\n", - " curr_df = pd.DataFrame(curr_dict)\n", - " df = pd.concat([df, curr_df]).reset_index(drop=True)\n", + " # Add the row to the dataframe\n", + " curr_dict = dict(\n", + " [(\"material\", [mat])]\n", + " + [(f\"Property{p_idx}\", [p]) for p_idx, p in enumerate(prop)]\n", + " )\n", + " curr_df = pd.DataFrame(curr_dict)\n", + " df = pd.concat([df, curr_df]).reset_index(drop=True)\n", "\n", - "display(df)" + " display(df)" ] }, { @@ -771,33 +507,25 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "fc57fd5f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Is perovskite/Si linked to power conversion efficiency: True\n", - "Is perovskite/Si linked to band gap: True\n" - ] - } - ], - "source": [ - "# Find properties linked to perovskite/Si\n", - "m_idx = hetero_kg[\"material\"][\"_name\"].index(\"perovskite/Si\")\n", - "perovskite_edges = (\n", - " hetero_kg[\"material\", \"mat2prop\", \"property\"].edge_index[0, :] == m_idx\n", - ")\n", - "prop_idx = hetero_kg[\"material\", \"mat2prop\", \"property\"].edge_index[1, perovskite_edges]\n", - "properties = [hetero_kg[\"property\"][\"_name\"][idx] for idx in prop_idx.tolist()]\n", - "\n", - "# Check if the desired properties are linked\n", - "print(\n", - " f'Is perovskite/Si linked to power conversion efficiency: {\"power conversion efficiency\" in properties}'\n", - ")\n", - "print(f'Is perovskite/Si linked to band gap: {\"band gap\" in properties}')" + "outputs": [], + "source": [ + "if hetero_kg[\"material\"]:\n", + " # Find properties linked to perovskite/Si\n", + " m_idx = hetero_kg[\"material\"][\"_name\"].index(\"perovskite/Si\")\n", + " perovskite_edges = (\n", + " hetero_kg[\"material\", \"mat2prop\", \"property\"].edge_index[0, :] == m_idx\n", + " )\n", + " prop_idx = hetero_kg[\"material\", \"mat2prop\", \"property\"].edge_index[1, perovskite_edges]\n", + " properties = [hetero_kg[\"property\"][\"_name\"][idx] for idx in prop_idx.tolist()]\n", + "\n", + " # Check if the desired properties are linked\n", + " print(\n", + " f'Is perovskite/Si linked to power conversion efficiency: {\"power conversion efficiency\" in properties}'\n", + " )\n", + " print(f'Is perovskite/Si linked to band gap: {\"band gap\" in properties}')" ] }, { @@ -832,7 +560,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -846,7 +574,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.4" } }, "nbformat": 4, diff --git a/examples/nlp_for_materials/nlp_for_materials.ipynb b/examples/nlp_for_materials/nlp_for_materials.ipynb index 189d0cf..9e436e3 100644 --- a/examples/nlp_for_materials/nlp_for_materials.ipynb +++ b/examples/nlp_for_materials/nlp_for_materials.ipynb @@ -33,15 +33,7 @@ "execution_count": 1, "id": "b01a4fd1", "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Project key: 1234567890abcdefghijklmnopqrstvwyz123456\n" - ] - } - ], + "outputs": [], "source": [ "from dsnotebooks.settings import ProjectNotebookSettings\n", "\n", @@ -129,16 +121,24 @@ "name": "stderr", "output_type": "stream", "text": [ - "Processing input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:00<00:00, 206.31it/s]\u001b[38;2;15;98;254m \u001b[0m\n", - "Submitting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:03<00:00, 3.48s/it]\u001b[38;2;15;98;254m \u001b[0m\n", - "Converting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:23<00:00, 23.54s/it]\u001b[38;2;15;98;254m \u001b[0m\n" + "Submitting input: : 0%|\u001b[38;2;15;98;254m \u001b[0m| 0/1 [00:00Annual Reports\n", " Document\n", " 107.38K\n", - " 2024-01-12\n", + " 2024-04-15\n", " default/annual-report\n", " \n", " \n", " 3\n", " arXiv abstracts\n", " Document\n", - " 2.37M\n", - " 2023-12-07\n", + " 2.48M\n", + " 2024-05-22\n", " default/arxiv-abstract\n", " \n", " \n", @@ -407,7 +381,7 @@ " arXiv category taxonomy\n", " Record\n", " 155\n", - " 2023-12-05\n", + " 2024-05-22\n", " default/arxiv-category\n", " \n", " \n", @@ -438,17 +412,17 @@ " 8\n", " ChEMBL\n", " Record\n", - " 2.11M\n", - " 2023-01-03\n", + " 2.42M\n", + " 2024-04-26\n", " default/chembl\n", " \n", " \n", " 9\n", - " ChemRxiv\n", - " Document\n", - " 8.82K\n", - " 2023-11-23\n", - " default/chemrxiv\n", + " ChEMBL (DEPRECATED)\n", + " Record\n", + " 2.11M\n", + " 2023-01-03\n", + " default/chembl-deprecated\n", " \n", " \n", "\n", @@ -458,26 +432,26 @@ " Name Type Num entries Date \\\n", "0 AAAI Document 16.02K 2023-08-29 \n", "1 ACL Anthology Document 55.28K 2023-08-22 \n", - "2 Annual Reports Document 107.38K 2024-01-12 \n", - "3 arXiv abstracts Document 2.37M 2023-12-07 \n", - "4 arXiv category taxonomy Record 155 2023-12-05 \n", + "2 Annual Reports Document 107.38K 2024-04-15 \n", + "3 arXiv abstracts Document 2.48M 2024-05-22 \n", + "4 arXiv category taxonomy Record 155 2024-05-22 \n", "5 arXiv full documents Document 2.29M 2023-10-29 \n", "6 BioRxiv Document 357.76K 2023-11-09 \n", "7 Brenda Record 7.12K 2023-01-03 \n", - "8 ChEMBL Record 2.11M 2023-01-03 \n", - "9 ChemRxiv Document 8.82K 2023-11-23 \n", + "8 ChEMBL Record 2.42M 2024-04-26 \n", + "9 ChEMBL (DEPRECATED) Record 2.11M 2023-01-03 \n", "\n", - " Coords \n", - "0 default/aaai \n", - "1 default/acl \n", - "2 default/annual-report \n", - "3 default/arxiv-abstract \n", - "4 default/arxiv-category \n", - "5 default/arxiv \n", - "6 default/biorxiv \n", - "7 default/brenda \n", - "8 default/chembl \n", - "9 default/chemrxiv " + " Coords \n", + "0 default/aaai \n", + "1 default/acl \n", + "2 default/annual-report \n", + "3 default/arxiv-abstract \n", + "4 default/arxiv-category \n", + "5 default/arxiv \n", + "6 default/biorxiv \n", + "7 default/brenda \n", + "8 default/chembl \n", + "9 default/chembl-deprecated " ] }, "metadata": {}, @@ -486,6 +460,7 @@ ], "source": [ "from numerize.numerize import numerize\n", + "from datetime import datetime\n", "\n", "# Fetch list of all data collections\n", "collections = api.elastic.list()\n", @@ -497,7 +472,7 @@ " \"Name\": c.name,\n", " \"Type\": c.metadata.type,\n", " \"Num entries\": numerize(c.documents),\n", - " \"Date\": c.metadata.created.strftime(\"%Y-%m-%d\"),\n", + " \"Date\": datetime.fromisoformat(c.metadata.created).strftime(\"%Y-%m-%d\"),\n", " \"Coords\": f\"{c.source.elastic_id}/{c.source.index_key}\",\n", " }\n", " for c in collections\n", @@ -507,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "d5394460-3427-4a89-a6e3-d655b28b0412", "metadata": {}, "outputs": [ @@ -515,14 +490,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "#-found documents: RunQueryResult(outputs={'data_outputs': [], 'data_count': 2, 'data_aggs': {'deepsearch_total_size': {'value': 713388.0}}}, next_pages={}, timings=RunQueryResult.QueryTimings(overall=0.28376056300476193, tasks={'0_ElasticQuery': RunQueryResult.QueryTimings.TaskTimings(overall=0.2827732330188155, details={})}))\n" + "#-found documents: RunQueryResult(outputs={'data_outputs': [], 'data_count': 2, 'data_aggs': {'deepsearch_total_size': {'value': 780743.0}}}, next_pages={}, timings=RunQueryResult.QueryTimings(overall=3.036691223969683, tasks={'0_ElasticQuery': RunQueryResult.QueryTimings.TaskTimings(overall=3.035835279035382, details={})}))\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "3it [00:02, 1.13it/s] " + "3it [00:02, 1.26it/s] " ] }, { @@ -588,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 12, "id": "8717419b-6d96-4848-bbbf-3d4149406872", "metadata": {}, "outputs": [ @@ -736,19 +711,11 @@ "metadata": {}, "outputs": [], "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "adf0256c-f8f9-42d8-957d-a5c54f388a75", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -762,7 +729,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.10.4" }, "toc": { "base_numbering": 1, @@ -776,11 +743,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": false - }, - "vscode": { - "interpreter": { - "hash": "23663f76e1e243f0a6319b8ef58f504b6b45c83666dfefd3138ba8cf69ab01fa" - } } }, "nbformat": 4, diff --git a/examples/nlp_on_documents/nlp_on_documents.ipynb b/examples/nlp_on_documents/nlp_on_documents.ipynb index 507f193..3955a56 100644 --- a/examples/nlp_on_documents/nlp_on_documents.ipynb +++ b/examples/nlp_on_documents/nlp_on_documents.ipynb @@ -30,18 +30,10 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 10, "id": "b01a4fd1", "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Project key: 1234567890abcdefghijklmnopqrstvwyz123456\n" - ] - } - ], + "outputs": [], "source": [ "from dsnotebooks.settings import ProjectNotebookSettings\n", "\n", @@ -64,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "id": "502cdef8", "metadata": { "ExecuteTime": { @@ -98,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 12, "id": "f44fbf08", "metadata": {}, "outputs": [], @@ -116,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 13, "id": "ec83eb0b", "metadata": { "ExecuteTime": { @@ -129,16 +121,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Processing input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:00<00:00, 126.92it/s]\u001b[38;2;15;98;254m \u001b[0m\n", - "Submitting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:03<00:00, 3.65s/it]\u001b[38;2;15;98;254m \u001b[0m\n", - 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"2024-02-20 12:55:03.453 (8967.111s) [ 6E06DF] utils.h:77 INFO| initialising model: LINK\n", - "2024-02-20 12:55:03.453 (8967.111s) [ 6E06DF] utils.h:77 INFO| initialising model: NAME\n", - "2024-02-20 12:55:03.463 (8967.121s) [ 6E06DF] utils.h:77 INFO| initialising model: QUOTE\n", - "2024-02-20 12:55:03.463 (8967.121s) [ 6E06DF] utils.h:77 INFO| initialising model: PARENTHESIS\n", - "2024-02-20 12:55:03.463 (8967.121s) [ 6E06DF] utils.h:77 INFO| initialising model: EXPRESSION\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:77 INFO| initialising model: SENTENCE\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:301 INFO| [0] SPM\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:301 INFO| [1] LANGUAGE\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:301 INFO| [2] NUMVAL\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:301 INFO| [3] CITE\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:301 INFO| [4] LINK\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:301 INFO| [5] NAME\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:301 INFO| [6] QUOTE\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:301 INFO| [7] PARENTHESIS\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:301 INFO| [8] EXPRESSION\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:301 INFO| [9] SENTENCE\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:301 INFO| [10] LAPOS\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] utils.h:301 INFO| [11] TERM\n", - "2024-02-20 12:55:03.463 (8967.122s) [ 6E06DF] glm.h:96 INFO| #-models: 12\n", - "2024-02-20 12:55:03.464 (8967.122s) [ 6E06DF] base_producer.h:263 INFO| item: ./converted_docs/20140197356.json\n", - "2024-02-20 12:55:03.464 (8967.122s) [ 6E06DF] base_producer.h:300 INFO| found 1 files to ingest!\n", - "2024-02-20 12:55:03.464 (8967.122s) [ 6E06DF] base_producer.h:263 INFO| item: ./converted_docs/20140197356.json\n", - "2024-02-20 12:55:03.464 (8967.122s) [ 6E06DF] base_producer.h:300 INFO| found 1 files to ingest!\n", - "2024-02-20 12:55:03.464 (8967.122s) [ 6E06DF] create.h:150 INFO| initialise glm: \n", - "2024-02-20 12:55:03.464 (8967.122s) [ 6E06DF] create.h:151 INFO| -> reserve nodes: 10000000\n", - "2024-02-20 12:55:03.464 (8967.122s) [ 6E06DF] create.h:154 INFO| -> reserve edges: 100000000\n", - "2024-02-20 12:55:03.493 (8967.151s) [ 6E06DF] create.h:175 INFO| start creating glm (#-threads: 4) ...\n", - "2024-02-20 12:55:03.787 (8967.446s) [ 6E06DF] edges.h:132 INFO| show_bucket_distribution (bucket-size versus count): 32\n", - "2024-02-20 12:55:03.787 (8967.446s) [ 6E06DF] create.h:219 INFO| launching 4 threads ...\n", - "2024-02-20 12:55:05.734 (8969.392s) [ 701072] document.h:217 INFO| reading: ./converted_docs/20140197356.json\n", - "2024-02-20 12:55:05.776 (8969.434s) [ 701070] logger.h:169 INFO| id, tot-text (loc-text), #-nodes, #-edges, #-token, #-terms: [#-nodes %, #-edges %, #-token %, #-terms %]\n", - "2024-02-20 12:55:05.776 (8969.434s) [ 701070] logger.h:188 INFO| 1, 0.00e+00 (0.00e+00), 1.00e+01, 0.00e+00, 4.00e+00, 0.00e+00: [1.00e+02 %, 1.00e+02 %, 1.00e+02 %, 1.00e+02 %]\n", - "2024-02-20 12:55:05.806 (8969.464s) [ 70106F] logger.h:188 INFO| 0, 0.00e+00 (0.00e+00), 1.00e+01, 0.00e+00, 4.00e+00, 0.00e+00: [0.00e+00 %, 0.00e+00 %, 0.00e+00 %, 0.00e+00 %]\n", - "2024-02-20 12:55:05.807 (8969.465s) [ 701071] logger.h:188 INFO| 2, 0.00e+00 (0.00e+00), 1.00e+01, 0.00e+00, 4.00e+00, 0.00e+00: [0.00e+00 %, 0.00e+00 %, 0.00e+00 %, 0.00e+00 %]\n", - "2024-02-20 12:55:06.192 (8969.850s) [ 701072] logger.h:188 INFO| 3, 1.00e+00 (1.00e+00), 3.76e+03, 3.47e+04, 1.20e+03, 7.69e+02: [3.75e+04 %, 3.47e+12 %, 2.98e+04 %, 7.69e+10 %]\n", - "2024-02-20 12:55:06.202 (8969.860s) [ 6E06DF] create.h:258 INFO| total text read: 1\n", - "2024-02-20 12:55:06.202 (8969.860s) [ 6E06DF] create.h:260 INFO| final GLM: #-nodes: 3758, #-edges: 34733\n", - "2024-02-20 12:55:06.203 (8969.861s) [ 6E06DF] nodes.h:143 INFO| show_bucket_distribution (bucket-size versus count): 32\n", - "2024-02-20 12:55:06.203 (8969.861s) [ 6E06DF] nodes.h:148 INFO| \t1: 3724\n", - "2024-02-20 12:55:06.203 (8969.861s) [ 6E06DF] nodes.h:148 INFO| \t2: 17\n", - "2024-02-20 12:55:06.208 (8969.866s) [ 6E06DF] edges.h:132 INFO| show_bucket_distribution (bucket-size versus count): 32\n", - "2024-02-20 12:55:06.208 (8969.866s) [ 6E06DF] edges.h:137 INFO| \t1: 34373\n", - "2024-02-20 12:55:06.208 (8969.866s) [ 6E06DF] edges.h:137 INFO| \t2: 177\n", - "2024-02-20 12:55:06.208 (8969.866s) [ 6E06DF] edges.h:137 INFO| \t3: 2\n", - "2024-02-20 12:55:06.789 (8970.447s) [ 6E06DF] create.h:165 INFO| finalise glm\n", - "2024-02-20 12:55:06.789 (8970.447s) [ 6E06DF] singplur.h:50 INFO| keeping terms up to length: 2\n", - "2024-02-20 12:55:06.790 (8970.448s) [ 6E06DF] singplur.h:131 INFO| add_singplur --> new nodes: 49\n", - "2024-02-20 12:55:06.790 (8970.448s) [ 6E06DF] edges.h:325 INFO| sorting edge [ to-singular]: 170\n", - "2024-02-20 12:55:06.790 (8970.448s) [ 6E06DF] edges.h:325 INFO| sorting edge [ to-plural]: 170\n", - "2024-02-20 12:55:06.790 (8970.448s) [ 6E06DF] taxtree.h:52 INFO| add_taxupdn\n", - "2024-02-20 12:55:06.793 (8970.452s) [ 6E06DF] nodes.h:329 INFO| sort\n", - "2024-02-20 12:55:06.794 (8970.452s) [ 6E06DF] edges.h:375 INFO| sort\n", - "2024-02-20 12:55:06.794 (8970.452s) [ 6E06DF] edges.h:325 INFO| sorting edge [ prev]: 10239\n", - "2024-02-20 12:55:06.796 (8970.454s) [ 6E06DF] edges.h:325 INFO| sorting edge [ next]: 10304\n", - "2024-02-20 12:55:06.798 (8970.456s) [ 6E06DF] edges.h:325 INFO| sorting edge [ tax-dn]: 800\n", - "2024-02-20 12:55:06.798 (8970.456s) [ 6E06DF] edges.h:325 INFO| sorting edge [ tax-up]: 1294\n", - "2024-02-20 12:55:06.798 (8970.456s) [ 6E06DF] edges.h:325 INFO| sorting edge [ to-token]: 362\n", - "2024-02-20 12:55:06.798 (8970.456s) [ 6E06DF] edges.h:325 INFO| sorting edge [ from-token]: 362\n", - "2024-02-20 12:55:06.798 (8970.456s) [ 6E06DF] edges.h:325 INFO| sorting edge [ to-pos]: 1339\n", - "2024-02-20 12:55:06.798 (8970.456s) [ 6E06DF] edges.h:325 INFO| sorting edge [ from-pos]: 1339\n", - "2024-02-20 12:55:06.798 (8970.457s) [ 6E06DF] edges.h:325 INFO| sorting edge [ to-label]: 1232\n", - "2024-02-20 12:55:06.799 (8970.457s) [ 6E06DF] edges.h:325 INFO| sorting edge [ from-label]: 1232\n", - "2024-02-20 12:55:06.799 (8970.457s) [ 6E06DF] edges.h:325 INFO| sorting edge [ to-root]: 832\n", - "2024-02-20 12:55:06.799 (8970.457s) [ 6E06DF] edges.h:325 INFO| sorting edge [ from-root]: 849\n", - "2024-02-20 12:55:06.799 (8970.457s) [ 6E06DF] edges.h:325 INFO| sorting edge [ to-sent]: 1378\n", - "2024-02-20 12:55:06.799 (8970.457s) [ 6E06DF] edges.h:325 INFO| sorting edge [ from-sent]: 1378\n", - "2024-02-20 12:55:06.799 (8970.458s) [ 6E06DF] edges.h:325 INFO| sorting edge [ to-text]: 1283\n", - "2024-02-20 12:55:06.800 (8970.458s) [ 6E06DF] edges.h:325 INFO| sorting edge [ from-text]: 1283\n", - "2024-02-20 12:55:06.800 (8970.458s) [ 6E06DF] edges.h:325 INFO| sorting edge [ to-doc]: 769\n", - "2024-02-20 12:55:06.800 (8970.458s) [ 6E06DF] edges.h:325 INFO| sorting edge [ from-doc]: 769\n", - "2024-02-20 12:55:06.800 (8970.458s) [ 6E06DF] nodes.h:329 INFO| sort\n", - "2024-02-20 12:55:06.800 (8970.458s) [ 6E06DF] edges.h:375 INFO| sort\n", - "2024-02-20 12:55:06.800 (8970.458s) [ 6E06DF] topology.h:482 INFO| computing topology ...\n", - "2024-02-20 12:55:06.800 (8970.459s) [ 6E06DF] topology.h:499 INFO| computing nodes-topology ...\n", - "2024-02-20 12:55:06.801 (8970.459s) [ 6E06DF] topology.h:531 INFO| computing edges-topology ...\n", - "2024-02-20 12:55:06.802 (8970.460s) [ 6E06DF] topology.h:124 INFO| \n", - "2024-02-20 12:55:06.802 (8970.460s) [ 6E06DF] topology.h:195 INFO| node table: \n", - "| ----------- | ---------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | \n", - "| flavor | node-name | count | 0 | 1 | 2 | 4 | 8 | 16 | 32 | 64 | 128 | 256 | 512 | 1024 | 2048 | 4096 | 8192 | 16384 | 32768 | 65536 | \n", - "| ----------- | ---------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | \n", - "| 0 | subw_token | 1353 | 0 | 0 | 6.4e+02 | 3.5e+02 | 1.9e+02 | 9.2e+01 | 5.8e+01 | 2.2e+01 | 4.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 1 | word_token | 1196 | 4.0e+00 | 0 | 6.9e+02 | 2.5e+02 | 1.4e+02 | 6.6e+01 | 2.7e+01 | 1.5e+01 | 4.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 2 | syntax | 35 | 0 | 0 | 3.0e+00 | 1.0e+00 | 1.0e+00 | 6.0e+00 | 4.0e+00 | 1.2e+01 | 8.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 4 | label | 6 | 6.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 5 | sublabel | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 8 | cont | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 9 | conn | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 10 | term | 818 | 4.9e+01 | 0 | 5.9e+02 | 1.2e+02 | 4.3e+01 | 1.7e+01 | 3.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 11 | verb | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 16 | sentence | 238 | 0 | 0 | 2.3e+02 | 3.0e+00 | 3.0e+00 | 2.0e+00 | 1.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 32 | text | 160 | 1.0e+02 | 0 | 6.0e+01 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 48 | table | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 64 | section | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 65 | chapter | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 96 | document | 1 | 1.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 97 | book | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| ----------- | ---------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | \n", - "| total (cnt) | | 3.8e+03 | 1.6e+02 | 0 | 2.2e+03 | 7.2e+02 | 3.7e+02 | 1.8e+02 | 9.3e+01 | 4.9e+01 | 1.6e+01 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| total (%) | | 1.0e+00 | 4.2e-02 | 0.0e+00 | 5.8e-01 | 1.9e-01 | 9.8e-02 | 4.8e-02 | 2.4e-02 | 1.3e-02 | 4.2e-03 | 0.0e+00 | 0.0e+00 | 0.0e+00 | 0.0e+00 | 0.0e+00 | 0.0e+00 | 0.0e+00 | 0.0e+00 | 0.0e+00 | \n", - "| cumul (%) | | 1.0e+00 | 4.2e-02 | 4.2e-02 | 6.2e-01 | 8.1e-01 | 9.1e-01 | 9.6e-01 | 9.8e-01 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | \n", - "| ----------- | ---------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | \n", - "\n", - "2024-02-20 12:55:06.803 (8970.461s) [ 6E06DF] topology.h:268 INFO| edge table: \n", - "| ----------- | ----------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | \n", - "| flavor | edge-name | count | 0 | 1 | 2 | 4 | 8 | 16 | 32 | 64 | 128 | 256 | 512 | 1024 | 2048 | 4096 | 8192 | 16384 | 32768 | 65536 | \n", - "| ----------- | ----------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | \n", - "| -6 | prev-6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| -5 | prev-5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| -4 | prev-4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| -3 | prev-3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| -2 | prev-2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| -1 | prev | 1.0e+04 | 0 | 6.0e+03 | 2.1e+03 | 1.0e+03 | 5.7e+02 | 3.0e+02 | 1.4e+02 | 7.8e+01 | 2.7e+01 | 5.0e+00 | 5.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 1 | next | 1.0e+04 | 0 | 6.0e+03 | 2.1e+03 | 1.0e+03 | 5.7e+02 | 3.1e+02 | 1.4e+02 | 8.1e+01 | 2.7e+01 | 5.0e+00 | 5.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 2 | next-2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 3 | next-3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 4 | next-4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 5 | next-5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 6 | next-6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 32 | tax-dn | 8.0e+02 | 6.7e+01 | 1.8e+02 | 4.1e+02 | 7.4e+01 | 3.7e+01 | 2.2e+01 | 6.0e+00 | 3.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 33 | tax-up | 1.3e+03 | 6.7e+01 | 4.6e+02 | 4.9e+02 | 1.3e+02 | 7.7e+01 | 4.7e+01 | 1.6e+01 | 7.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 64 | to-lower | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 65 | to-upper | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 66 | to-singular | 1.7e+02 | 0 | 1.7e+02 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 67 | to-plural | 1.7e+02 | 0 | 1.7e+02 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 96 | to-token | 3.6e+02 | 0 | 2.2e+02 | 5.7e+01 | 4.0e+01 | 2.0e+01 | 1.4e+01 | 6.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 97 | from-token | 3.6e+02 | 0 | 2.2e+02 | 5.7e+01 | 4.0e+01 | 2.0e+01 | 1.4e+01 | 6.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 98 | to-pos | 1.3e+03 | 0 | 7.2e+02 | 2.0e+02 | 1.7e+02 | 1.1e+02 | 8.0e+01 | 3.3e+01 | 1.7e+01 | 7.0e+00 | 4.0e+00 | 3.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 99 | from-pos | 1.3e+03 | 0 | 7.2e+02 | 2.0e+02 | 1.7e+02 | 1.1e+02 | 8.0e+01 | 3.3e+01 | 1.7e+01 | 7.0e+00 | 4.0e+00 | 3.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 100 | to-label | 1.2e+03 | 0 | 7.3e+02 | 2.0e+02 | 1.4e+02 | 8.2e+01 | 5.2e+01 | 2.1e+01 | 1.0e+01 | 0 | 1.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 101 | from-label | 1.2e+03 | 0 | 7.3e+02 | 2.0e+02 | 1.4e+02 | 8.2e+01 | 5.2e+01 | 2.1e+01 | 1.0e+01 | 0 | 1.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 102 | to-root | 8.3e+02 | 7.0e+01 | 5.9e+02 | 9.0e+01 | 4.4e+01 | 2.8e+01 | 1.2e+01 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 103 | from-root | 8.5e+02 | 5.3e+01 | 4.1e+02 | 2.5e+02 | 7.2e+01 | 3.5e+01 | 2.1e+01 | 8.0e+00 | 1.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 128 | to-sent | 1.4e+03 | 0 | 1.3e+03 | 6.7e+01 | 2.2e+01 | 3.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 129 | from-sent | 1.4e+03 | 0 | 1.3e+03 | 6.7e+01 | 2.2e+01 | 3.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 130 | to-text | 1.3e+03 | 0 | 1.1e+03 | 1.1e+02 | 2.9e+01 | 9.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 131 | from-text | 1.3e+03 | 0 | 1.1e+03 | 1.1e+02 | 2.9e+01 | 9.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 132 | to-table | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 133 | from-table | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 134 | to-doc | 7.7e+02 | 0 | 5.5e+02 | 1.0e+02 | 6.0e+01 | 3.0e+01 | 2.0e+01 | 6.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| 135 | from-doc | 7.7e+02 | 0 | 5.5e+02 | 1.0e+02 | 6.0e+01 | 3.0e+01 | 2.0e+01 | 6.0e+00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| ----------- | ----------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | \n", - "| total (cnt) | | 3.7e+04 | 2.6e+02 | 2.3e+04 | 6.9e+03 | 3.3e+03 | 1.8e+03 | 1.0e+03 | 4.3e+02 | 2.2e+02 | 6.8e+01 | 2.0e+01 | 1.6e+01 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n", - "| total (%) | | 1.0e+00 | 6.9e-03 | 6.2e-01 | 1.8e-01 | 8.9e-02 | 4.9e-02 | 2.8e-02 | 1.2e-02 | 6.0e-03 | 1.8e-03 | 5.3e-04 | 4.3e-04 | 0.0e+00 | 0.0e+00 | 0.0e+00 | 0.0e+00 | 0.0e+00 | 0.0e+00 | 0.0e+00 | \n", - "| cumul (%) | | 1.0e+00 | 6.9e-03 | 6.3e-01 | 8.1e-01 | 9.0e-01 | 9.5e-01 | 9.8e-01 | 9.9e-01 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | 1.0e+00 | \n", - "| ----------- | ----------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | \n", - "\n", - "2024-02-20 12:55:06.803 (8970.461s) [ 6E06DF] create.h:144 INFO| done creating glm!\n", - "2024-02-20 12:55:06.803 (8970.461s) [ 6E06DF] save.h:145 INFO| writing started ...\n", - "2024-02-20 12:55:06.803 (8970.461s) [ 6E06DF] save.h:155 INFO| writing ./glm/parameters.json\n", - "2024-02-20 12:55:06.803 (8970.461s) [ 6E06DF] save.h:165 INFO| writing ./glm/topology.json\n", - "2024-02-20 12:55:06.806 (8970.464s) [ 6E06DF] save.h:171 INFO| writing ./glm/topology.txt\n", - "2024-02-20 12:55:06.807 (8970.465s) [ 6E06DF] nodes.h:329 INFO| sort\n", - "2024-02-20 12:55:06.807 (8970.466s) [ 6E06DF] save.h:182 INFO| writing ./glm/nodes.bin\n", - "2024-02-20 12:55:06.810 (8970.468s) [ 6E06DF] save.h:208 INFO| writing ./glm/edges.bin\n", - "2024-02-20 12:55:06.817 (8970.475s) [ 6E06DF] save.h:283 INFO| writing CSV started ...\n", - "2024-02-20 12:55:06.817 (8970.475s) [ 6E06DF] save.h:292 INFO| writing ./glm/nodes.csv\n", - "2024-02-20 12:55:06.846 (8970.504s) [ 6E06DF] save.h:324 INFO| writing ./glm/edges.csv\n", - "2024-02-20 12:55:06.900 (8970.558s) [ 6E06DF] save.h:365 INFO| writing done!\n" - ] - } - ], + "outputs": [], "source": [ - "ifile = \"./converted_docs/20140197356.json\"\n", - "\n", "odir = \"./glm\"\n", "os.makedirs(odir, exist_ok=True)\n", "\n", "model_names = \"spm;term\"\n", - "json_files = [ifile]\n", + "json_files = [str(file) for file in json_files]\n", "\n", "odir, glm = create_glm_from_docs(odir, json_files, model_names)" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "175550c9-8b1e-4b02-8069-6c36aceafd99", "metadata": {}, "outputs": [ @@ -603,24 +384,11 @@ "name": "stdout", "output_type": "stream", "text": [ - " total-count nodes-text\n", - "2590 30 composition\n", - "2591 29 invention\n", - "2592 27 substrate\n", - "2593 24 ppm\n", - "2594 19 compositions\n", - "... ... ...\n", - "3403 0 Suitable polymer\n", - "3404 0 film\n", - "3405 0 › tion\n", - "3406 0 insulator material\n", - "3407 0 A\n", - "\n", - "[818 rows x 2 columns]\n", + "None\n", " weight prob cumul text count\n", - "0 0.5625 0.5625 0.5625 CMP composition 9\n", - "1 0.3750 0.3750 0.9375 polishing composition 6\n", - "2 0.0625 0.0625 1.0000 abrasive composition 1\n" + "0 0.5625 0.5625 0.5625 CMP composition 27\n", + "1 0.3750 0.3750 0.9375 polishing composition 18\n", + "2 0.0625 0.0625 1.0000 abrasive composition 3\n" ] } ], @@ -629,8 +397,9 @@ "# print(nodes)\n", "\n", "# Get all terms of the document\n", - "terms = nodes[nodes[\"name\"] == \"term\"][[\"total-count\", \"nodes-text\"]]\n", - "print(terms)\n", + "if nodes:\n", + " terms = nodes[nodes[\"name\"] == \"term\"][[\"total-count\", \"nodes-text\"]]\n", + " print(terms)\n", "\n", "# Get all terms of the document with `composition`\n", "res = expand_terms(glm, \"composition\")\n", @@ -654,7 +423,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -668,7 +437,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.10.4" }, "toc": { "base_numbering": 1, @@ -682,11 +451,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": false - }, - "vscode": { - "interpreter": { - "hash": "23663f76e1e243f0a6319b8ef58f504b6b45c83666dfefd3138ba8cf69ab01fa" - } } }, "nbformat": 4, diff --git a/examples/qa/qa_quick_start.ipynb b/examples/qa/qa_quick_start.ipynb index 0fdc3c3..5c8b9a2 100644 --- a/examples/qa/qa_quick_start.ipynb +++ b/examples/qa/qa_quick_start.ipynb @@ -153,8 +153,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/pva/work/github.com/DS4SD/deepsearch-examples/.venv/lib/python3.10/site-packages/pydantic/main.py:347: UserWarning: Pydantic serializer warnings:\n", - " Expected `list[str]` but got `_LiteralGenericAlias` - serialized value may not be as expected\n", + "/home/santana/Documents/dev/deepsearch-examples/venv/lib/python3.10/site-packages/pydantic/main.py:426: UserWarning: Pydantic serializer warnings:\n", + " Expected `list[str]` but got `_LiteralGenericAlias` with value `typing.Literal['SemanticI...emanticIngestSourceUrl']` - serialized value may not be as expected\n", " return self.__pydantic_serializer__.to_python(\n" ] }, @@ -201,11 +201,11 @@ { "data": { "text/html": [ - "
Where was the first European IBM lab located?\n",
+       "
sentiment analysis\n",
        "
\n"
       ],
       "text/plain": [
-       "Where was the first European IBM lab located?\n"
+       "sentiment analysis\n"
       ]
      },
      "metadata": {},
@@ -217,23 +217,30 @@
        "
RAGResult(\n",
        "    answers=[\n",
        "        RAGAnswerItem(\n",
-       "            answer='The first European IBM lab was located in Adliswil, Switzerland, near Zurich.',\n",
+       "            answer=\"The context information provided does not contain any information related to sentiment \n",
+       "analysis. Sentiment analysis is a field of study that analyzes people's sentiments, attitudes, or emotions towards \n",
+       "certain entities, such as products, services, individuals, issues, events, topics, and their attributes. It \n",
+       "involves the use of natural language processing, text analysis, computational linguistics, and biometrics to \n",
+       "systematically identify, extract, quantify, and study affective states and subjective information.\\nTherefore, it \n",
+       "is not possible to perform sentiment analysis based on the context information provided.\",\n",
        "            grounding=RAGGroundingInfo(\n",
        "                retr_items=[\n",
        "                    SearchResultItem(\n",
-       "                        doc_hash='b30bc667a324ae111d025526563b674a8d3fd869bc07c8fd204aa95b05d41f0c',\n",
-       "                        chunk='Switzerland\\nIn 1956, IBM opened their first European research laboratory in \n",
-       "Adliswil, Switzerland, near Zurich. The lab moved to its own campus in neighboring Rüschlikon in 1962. The Zurich \n",
-       "lab is staffed by a multicultural and interdisciplinary team of a few hundred permanent research staff members, \n",
-       "graduate students and post-doctoral fellows, representing about 45 nationalities. Collocated with the lab is a \n",
-       "Client Center (formerly the Industry Solutions Lab), an executive briefing facility demonstrating technology \n",
-       "prototypes and solutions.',\n",
-       "                        main_path='main-text.71',\n",
-       "                        path_group=['main-text.69', 'main-text.71'],\n",
+       "                        doc_hash='029210df929c78e70d74e6f141a46d8326905ce58562f2081819c80c3921d5a3',\n",
+       "                        main_path='main-text.65',\n",
+       "                        path_group=['main-text.47', 'main-text.65'],\n",
+       "                        chunk='References\\n- 18. \n",
+       "https://www.licensing.org/inside-licensing/at-ces-licensing-stretches-brands-in-new-directions/ At CES, Licensing \n",
+       "Stretches Brands in New Directions',\n",
        "                        source_is_text=True\n",
        "                    )\n",
        "                ],\n",
        "                gen_ctx_paths=[\n",
+       "                    'main-text.55',\n",
+       "                    'main-text.56',\n",
+       "                    'main-text.57',\n",
+       "                    'main-text.58',\n",
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        "                    'main-text.60',\n",
        "                    'main-text.61',\n",
        "                    'main-text.62',\n",
@@ -243,16 +250,7 @@
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        "                    'main-text.67',\n",
        "                    'main-text.68',\n",
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-       "                    'main-text.74',\n",
-       "                    'main-text.75',\n",
-       "                    'main-text.76',\n",
-       "                    'main-text.77',\n",
-       "                    'main-text.78'\n",
+       "                    'main-text.69'\n",
        "                ]\n",
        "            ),\n",
        "            prompt=None\n",
@@ -260,33 +258,30 @@
        "    ],\n",
        "    search_result_items=[\n",
        "        SearchResultItem(\n",
-       "            doc_hash='b30bc667a324ae111d025526563b674a8d3fd869bc07c8fd204aa95b05d41f0c',\n",
-       "            chunk='Switzerland\\nIn 1956, IBM opened their first European research laboratory in Adliswil, \n",
-       "Switzerland, near Zurich. The lab moved to its own campus in neighboring Rüschlikon in 1962. The Zurich lab is \n",
-       "staffed by a multicultural and interdisciplinary team of a few hundred permanent research staff members, graduate \n",
-       "students and post-doctoral fellows, representing about 45 nationalities. Collocated with the lab is a Client Center\n",
-       "(formerly the Industry Solutions Lab), an executive briefing facility demonstrating technology prototypes and \n",
-       "solutions.',\n",
-       "            main_path='main-text.71',\n",
-       "            path_group=['main-text.69', 'main-text.71'],\n",
+       "            doc_hash='029210df929c78e70d74e6f141a46d8326905ce58562f2081819c80c3921d5a3',\n",
+       "            main_path='main-text.65',\n",
+       "            path_group=['main-text.47', 'main-text.65'],\n",
+       "            chunk='References\\n- 18. \n",
+       "https://www.licensing.org/inside-licensing/at-ces-licensing-stretches-brands-in-new-directions/ At CES, Licensing \n",
+       "Stretches Brands in New Directions',\n",
        "            source_is_text=True\n",
        "        ),\n",
        "        SearchResultItem(\n",
-       "            doc_hash='b30bc667a324ae111d025526563b674a8d3fd869bc07c8fd204aa95b05d41f0c',\n",
-       "            chunk='Switzerland\\nIBM Research-Zurich (previously called IBM Zurich Research Laboratory, ZRL) is the \n",
-       "European branch of IBM Research. It was opened in 1956 and is located in Rüschlikon, near Zurich, Switzerland.',\n",
-       "            main_path='main-text.70',\n",
-       "            path_group=['main-text.69', 'main-text.70'],\n",
+       "            doc_hash='029210df929c78e70d74e6f141a46d8326905ce58562f2081819c80c3921d5a3',\n",
+       "            main_path='main-text.61',\n",
+       "            path_group=['main-text.47', 'main-text.61'],\n",
+       "            chunk='References\\n- 14. \"17\" \n",
+       "(https://web.archive.org/web/20050226123115/http://www.washingtontechnology.com/news/1 1_1/news/10284-1.html). \n",
+       "www.washingtontechnology.com . Archived from the original (http://www. \n",
+       "washingtontechnology.com/news/11_1/news/10284-1.html) on 26 February 2005. Retrieved 15 January 2022.',\n",
        "            source_is_text=True\n",
        "        ),\n",
        "        SearchResultItem(\n",
-       "            doc_hash='b30bc667a324ae111d025526563b674a8d3fd869bc07c8fd204aa95b05d41f0c',\n",
-       "            chunk=\"History\\nThe roots of today's IBM Research began with the 1945 opening of the Watson Scientific \n",
-       "Computing Laboratory at Columbia University. $^{[4]}$ This was the first IBM laboratory devoted to pure science and\n",
-       "later expanded into additional IBM Research locations in Westchester County, New York, starting in the 1950s, \n",
-       "$^{[5][6]}$ including the Thomas J. Watson Research Center in 1961. [5][6]\",\n",
-       "            main_path='main-text.8',\n",
-       "            path_group=['main-text.7', 'main-text.8'],\n",
+       "            doc_hash='029210df929c78e70d74e6f141a46d8326905ce58562f2081819c80c3921d5a3',\n",
+       "            main_path='main-text.63',\n",
+       "            path_group=['main-text.47', 'main-text.63'],\n",
+       "            chunk='References\\n- 16. Nathan Schueth. \"Ultra Electronics - USSI, Innovation Through Experience\" \n",
+       "(http://www.ultra-uss i.com/). Ultra-ussi.com . Retrieved 25 October 2014.',\n",
        "            source_is_text=True\n",
        "        )\n",
        "    ]\n",
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-       "            \u001b[33manswer\u001b[0m=\u001b[32m'The first European IBM lab was located in Adliswil, Switzerland, near Zurich.'\u001b[0m,\n",
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+       "\u001b[32msystematically identify, extract, quantify, and study affective states and subjective information.\\nTherefore, it \u001b[0m\n",
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-       "                        \u001b[33mchunk\u001b[0m=\u001b[32m'Switzerland\\nIn 1956, IBM opened their first European research laboratory in \u001b[0m\n",
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+       "                        \u001b[33mchunk\u001b[0m=\u001b[32m'References\\n- 18. \u001b[0m\n",
+       "\u001b[32mhttps://www.licensing.org/inside-licensing/at-ces-licensing-stretches-brands-in-new-directions/ At CES, Licensing \u001b[0m\n",
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        "                \u001b[33mgen_ctx_paths\u001b[0m=\u001b[1m[\u001b[0m\n",
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        "                    \u001b[32m'main-text.60'\u001b[0m,\n",
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        "                \u001b[1m]\u001b[0m\n",
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+       "\u001b[32mhttps://www.licensing.org/inside-licensing/at-ces-licensing-stretches-brands-in-new-directions/ At CES, Licensing \u001b[0m\n",
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-       "\u001b[32mEuropean branch of IBM Research. It was opened in 1956 and is located in Rüschlikon, near Zurich, Switzerland.'\u001b[0m,\n",
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+       "            \u001b[33mchunk\u001b[0m=\u001b[32m'References\\n- 16. Nathan Schueth. \"Ultra Electronics - USSI, Innovation Through Experience\" \u001b[0m\n",
+       "\u001b[32m(\u001b[0m\u001b[32mhttp://www.ultra-uss i.com/\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. Ultra-ussi.com . Retrieved 25 October 2014.'\u001b[0m,\n",
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-       "solutions.',\n",
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+       "https://www.licensing.org/inside-licensing/at-ces-licensing-stretches-brands-in-new-directions/ At CES, Licensing \n",
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-       "            doc_hash='b30bc667a324ae111d025526563b674a8d3fd869bc07c8fd204aa95b05d41f0c',\n",
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-       "European branch of IBM Research. It was opened in 1956 and is located in Rüschlikon, near Zurich, Switzerland.',\n",
-       "            main_path='main-text.70',\n",
-       "            path_group=['main-text.69', 'main-text.70'],\n",
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+       "            path_group=['main-text.47', 'main-text.61'],\n",
+       "            chunk='References\\n- 14. \"17\" \n",
+       "(https://web.archive.org/web/20050226123115/http://www.washingtontechnology.com/news/1 1_1/news/10284-1.html). \n",
+       "www.washingtontechnology.com . Archived from the original (http://www. \n",
+       "washingtontechnology.com/news/11_1/news/10284-1.html) on 26 February 2005. Retrieved 15 January 2022.',\n",
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-       "Computing Laboratory at Columbia University. $^{[4]}$ This was the first IBM laboratory devoted to pure science and\n",
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-       "            main_path='main-text.8',\n",
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+       "            chunk='References\\n- 16. Nathan Schueth. \"Ultra Electronics - USSI, Innovation Through Experience\" \n",
+       "(http://www.ultra-uss i.com/). Ultra-ussi.com . Retrieved 25 October 2014.',\n",
        "            source_is_text=True\n",
        "        )\n",
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-       "            \u001b[33mchunk\u001b[0m=\u001b[32m'Switzerland\\nIn 1956, IBM opened their first European research laboratory in Adliswil, \u001b[0m\n",
-       "\u001b[32mSwitzerland, near Zurich. The lab moved to its own campus in neighboring Rüschlikon in 1962. The Zurich lab is \u001b[0m\n",
-       "\u001b[32mstaffed by a multicultural and interdisciplinary team of a few hundred permanent research staff members, graduate \u001b[0m\n",
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-       "\u001b[32m(\u001b[0m\u001b[32mformerly the Industry Solutions Lab\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, an executive briefing facility demonstrating technology prototypes and \u001b[0m\n",
-       "\u001b[32msolutions.'\u001b[0m,\n",
-       "            \u001b[33mmain_path\u001b[0m=\u001b[32m'main-text.71'\u001b[0m,\n",
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+       "            \u001b[33mpath_group\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m'main-text.47'\u001b[0m, \u001b[32m'main-text.65'\u001b[0m\u001b[1m]\u001b[0m,\n",
+       "            \u001b[33mchunk\u001b[0m=\u001b[32m'References\\n- 18. \u001b[0m\n",
+       "\u001b[32mhttps://www.licensing.org/inside-licensing/at-ces-licensing-stretches-brands-in-new-directions/ At CES, Licensing \u001b[0m\n",
+       "\u001b[32mStretches Brands in New Directions'\u001b[0m,\n",
        "            \u001b[33msource_is_text\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
        "        \u001b[1m)\u001b[0m,\n",
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-       "            \u001b[33mchunk\u001b[0m=\u001b[32m'Switzerland\\nIBM Research-Zurich \u001b[0m\u001b[32m(\u001b[0m\u001b[32mpreviously called IBM Zurich Research Laboratory, ZRL\u001b[0m\u001b[32m)\u001b[0m\u001b[32m is the \u001b[0m\n",
-       "\u001b[32mEuropean branch of IBM Research. It was opened in 1956 and is located in Rüschlikon, near Zurich, Switzerland.'\u001b[0m,\n",
-       "            \u001b[33mmain_path\u001b[0m=\u001b[32m'main-text.70'\u001b[0m,\n",
-       "            \u001b[33mpath_group\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m'main-text.69'\u001b[0m, \u001b[32m'main-text.70'\u001b[0m\u001b[1m]\u001b[0m,\n",
+       "            \u001b[33mdoc_hash\u001b[0m=\u001b[32m'029210df929c78e70d74e6f141a46d8326905ce58562f2081819c80c3921d5a3'\u001b[0m,\n",
+       "            \u001b[33mmain_path\u001b[0m=\u001b[32m'main-text.61'\u001b[0m,\n",
+       "            \u001b[33mpath_group\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m'main-text.47'\u001b[0m, \u001b[32m'main-text.61'\u001b[0m\u001b[1m]\u001b[0m,\n",
+       "            \u001b[33mchunk\u001b[0m=\u001b[32m'References\\n- 14. \"17\" \u001b[0m\n",
+       "\u001b[32m(\u001b[0m\u001b[32mhttps://web.archive.org/web/20050226123115/http://www.washingtontechnology.com/news/1 1_1/news/10284-1.html\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. \u001b[0m\n",
+       "\u001b[32mwww.washingtontechnology.com . Archived from the original \u001b[0m\u001b[32m(\u001b[0m\u001b[32mhttp://www. \u001b[0m\n",
+       "\u001b[32mwashingtontechnology.com/news/11_1/news/10284-1.html\u001b[0m\u001b[32m)\u001b[0m\u001b[32m on 26 February 2005. Retrieved 15 January 2022.'\u001b[0m,\n",
        "            \u001b[33msource_is_text\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
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-       "            \u001b[33mdoc_hash\u001b[0m=\u001b[32m'b30bc667a324ae111d025526563b674a8d3fd869bc07c8fd204aa95b05d41f0c'\u001b[0m,\n",
-       "            \u001b[33mchunk\u001b[0m=\u001b[32m\"History\u001b[0m\u001b[32m\\nThe roots of today's IBM Research began with the 1945 opening of the Watson Scientific \u001b[0m\n",
-       "\u001b[32mComputing Laboratory at Columbia University. $^\u001b[0m\u001b[32m{\u001b[0m\u001b[32m[\u001b[0m\u001b[32m4\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m$ This was the first IBM laboratory devoted to pure science and\u001b[0m\n",
-       "\u001b[32mlater expanded into additional IBM Research locations in Westchester County, New York, starting in the 1950s, \u001b[0m\n",
-       "\u001b[32m$^\u001b[0m\u001b[32m{\u001b[0m\u001b[32m[\u001b[0m\u001b[32m5\u001b[0m\u001b[32m]\u001b[0m\u001b[32m[\u001b[0m\u001b[32m6\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m$ including the Thomas J. Watson Research Center in 1961. \u001b[0m\u001b[32m[\u001b[0m\u001b[32m5\u001b[0m\u001b[32m]\u001b[0m\u001b[32m[\u001b[0m\u001b[32m6\u001b[0m\u001b[32m]\u001b[0m\u001b[32m\"\u001b[0m,\n",
-       "            \u001b[33mmain_path\u001b[0m=\u001b[32m'main-text.8'\u001b[0m,\n",
-       "            \u001b[33mpath_group\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m'main-text.7'\u001b[0m, \u001b[32m'main-text.8'\u001b[0m\u001b[1m]\u001b[0m,\n",
+       "            \u001b[33mdoc_hash\u001b[0m=\u001b[32m'029210df929c78e70d74e6f141a46d8326905ce58562f2081819c80c3921d5a3'\u001b[0m,\n",
+       "            \u001b[33mmain_path\u001b[0m=\u001b[32m'main-text.63'\u001b[0m,\n",
+       "            \u001b[33mpath_group\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m'main-text.47'\u001b[0m, \u001b[32m'main-text.63'\u001b[0m\u001b[1m]\u001b[0m,\n",
+       "            \u001b[33mchunk\u001b[0m=\u001b[32m'References\\n- 16. Nathan Schueth. \"Ultra Electronics - USSI, Innovation Through Experience\" \u001b[0m\n",
+       "\u001b[32m(\u001b[0m\u001b[32mhttp://www.ultra-uss i.com/\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. Ultra-ussi.com . Retrieved 25 October 2014.'\u001b[0m,\n",
        "            \u001b[33msource_is_text\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
        "        \u001b[1m)\u001b[0m\n",
        "    \u001b[1m]\u001b[0m\n",
@@ -521,7 +505,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
diff --git a/poetry.lock b/poetry.lock
index 889e18e..b335e2c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,110 +1,123 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
-name = "aiohttp"
-version = "3.9.5"
-description = "Async http client/server framework (asyncio)"
+name = "aiohappyeyeballs"
+version = "2.4.4"
+description = "Happy Eyeballs for asyncio"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
+aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
-async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
+async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
-yarl = ">=1.0,<2.0"
+propcache = ">=0.2.0"
+yarl = ">=1.17.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "brotlicffi"]
+speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
-version = "1.3.1"
+version = "1.3.2"
 description = "aiosignal: a list of registered asynchronous callbacks"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 files = [
-    {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"},
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 ]
 
 [package.dependencies]
@@ -121,30 +134,27 @@ files = [
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
 ]
 
-[package.dependencies]
-typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
-
 [[package]]
 name = "anyio"
-version = "4.4.0"
+version = "4.8.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
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+    {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
+    {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
 ]
 
 [package.dependencies]
 exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (>=0.23)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+trio = ["trio (>=0.26.1)"]
 
 [[package]]
 name = "appnope"
@@ -159,35 +169,24 @@ files = [
 
 [[package]]
 name = "argilla"
-version = "1.28.0"
-description = "Open-source tool for exploring, labeling, and monitoring data for NLP projects."
+version = "2.6.0"
+description = "The Argilla python server SDK"
 optional = false
-python-versions = "<3.12,>=3.8"
+python-versions = ">=3.9"
 files = [
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+    {file = "argilla-2.6.0-py3-none-any.whl", hash = "sha256:b7335c8db9705cb3ab1d9b16fd5c6dc9c1542e536fe110d6ab3d0323ca404a53"},
+    {file = "argilla-2.6.0.tar.gz", hash = "sha256:c0dde9873c4ede3a8d2a966bf9f4ae99ac8310c8b818dd4aeb91ad4822faa068"},
 ]
 
 [package.dependencies]
-backoff = "*"
-deprecated = ">=1.2.0,<1.3.0"
-httpx = ">=0.15,<=0.26"
-monotonic = "*"
-numpy = "<1.24.0"
-packaging = ">=20.0"
-pandas = ">=1.0.0"
-pydantic = ">=1.10.7"
-rich = "!=13.1.0"
-tqdm = ">=4.27.0"
-typer = ">=0.6.0,<0.10.0"
-wrapt = ">=1.13,<1.15"
-
-[package.extras]
-integrations = ["PyYAML (>=5.4.1,<6.1.0)", "datasets (>1.17.0,!=2.3.2)", "evaluate", "faiss-cpu", "flair (>=0.12.2)", "flyingsquid", "huggingface-hub (>=0.5.0)", "ipynbname", "openai (>=0.27.10,<1.0.0)", "peft", "pgmpy", "plotly (>=4.1.0)", "sentence-transformers", "sentence-transformers (>=2.0.0,<3.0.0)", "seqeval", "setfit (>=1.0.0)", "snorkel (>=0.9.7)", "spacy (>=3.5.0,<3.7.0)", "spacy-huggingface-hub (>=0.0.10)", "spacy-transformers (>=1.2.5)", "span-marker", "textdescriptives (>=2.7.0,<3.0.0)", "transformers[torch] (>=4.30.0)", "trl (>=0.5.0)"]
-listeners = ["schedule (>=1.1.0,<1.2.0)"]
-server = ["argilla-server (>=1.28.0,<1.29.0)"]
-server-postgresql = ["argilla-server[postgresql] (>=1.28.0,<1.29.0)"]
-tests = ["factory-boy (>=3.2.1,<3.3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock"]
+datasets = ">=2.0.0"
+httpx = ">=0.26.0"
+huggingface_hub = ">=0.22.0"
+pillow = ">=9.5.0"
+pydantic = ">=2.6.0,<3.0.0"
+rich = ">=10.0.0"
+standardwebhooks = ">=1.0.0"
+tqdm = ">=4.60.0"
 
 [[package]]
 name = "argon2-cffi"
@@ -267,21 +266,18 @@ test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock
 
 [[package]]
 name = "asttokens"
-version = "2.4.1"
+version = "3.0.0"
 description = "Annotate AST trees with source code positions"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
-    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
+    {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"},
+    {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"},
 ]
 
-[package.dependencies]
-six = ">=1.12.0"
-
 [package.extras]
-astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
-test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+astroid = ["astroid (>=2,<4)"]
+test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "async-lru"
@@ -299,73 +295,48 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "async-timeout"
-version = "4.0.3"
+version = "5.0.1"
 description = "Timeout context manager for asyncio programs"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
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 name = "attrs"
-version = "23.2.0"
+version = "24.3.0"
 description = "Classes Without Boilerplate"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
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-dev = ["attrs[tests]", "pre-commit"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
-tests = ["attrs[tests-no-zope]", "zope-interface"]
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-tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
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+cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
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+docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
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 name = "babel"
-version = "2.15.0"
+version = "2.16.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.8"
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-[package.dependencies]
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-
 [package.extras]
 dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
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-version = "0.2.0"
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-
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 version = "0.18"
@@ -437,80 +408,72 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "bleach"
-version = "6.1.0"
+version = "6.2.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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 [package.dependencies]
-six = ">=1.9.0"
+tinycss2 = {version = ">=1.1.0,<1.5", optional = true, markers = "extra == \"css\""}
 webencodings = "*"
 
 [package.extras]
-css = ["tinycss2 (>=1.1.0,<1.3)"]
+css = ["tinycss2 (>=1.1.0,<1.5)"]
 
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-version = "0.7.11"
+version = "1.2.0"
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 name = "bracex"
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 description = "Bash style brace expander."
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 [[package]]
@@ -526,74 +489,89 @@ files = [
 
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+version = "2024.12.14"
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 [package.dependencies]
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 ]
 
 [[package]]
 name = "cibuildwheel"
-version = "2.18.1"
+version = "2.22.0"
 description = "Build Python wheels on CI with minimal configuration."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 bashlex = "!=0.13"
 bracex = "*"
 certifi = "*"
+dependency-groups = ">=1.2"
 filelock = "*"
 packaging = ">=20.9"
 platformdirs = "*"
@@ -731,20 +712,17 @@ tomli = {version = "*", markers = "python_version < \"3.11\""}
 typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
-bin = ["click", "packaging (>=21.0)", "pip-tools", "pygithub", "pyyaml", "requests", "rich (>=9.6)"]
-dev = ["build", "click", "jinja2", "packaging (>=21.0)", "pip-tools", "pygithub", "pytest (>=6)", "pytest-timeout", "pytest-xdist", "pyyaml", "requests", "rich (>=9.6)", "tomli-w", "validate-pyproject"]
-docs = ["jinja2 (>=3.1.2)", "mkdocs (==1.3.1)", "mkdocs-include-markdown-plugin (==2.8.0)", "mkdocs-macros-plugin", "pymdown-extensions"]
-test = ["build", "jinja2", "pytest (>=6)", "pytest-timeout", "pytest-xdist", "tomli-w", "validate-pyproject"]
+uv = ["uv"]
 
 [[package]]
 name = "click"
-version = "8.1.7"
+version = "8.1.8"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -752,13 +730,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cloudpathlib"
-version = "0.16.0"
+version = "0.20.0"
 description = "pathlib-style classes for cloud storage services."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -766,9 +744,9 @@ typing_extensions = {version = ">4", markers = "python_version < \"3.11\""}
 
 [package.extras]
 all = ["cloudpathlib[azure]", "cloudpathlib[gs]", "cloudpathlib[s3]"]
-azure = ["azure-storage-blob (>=12)"]
+azure = ["azure-storage-blob (>=12)", "azure-storage-file-datalake (>=12)"]
 gs = ["google-cloud-storage"]
-s3 = ["boto3"]
+s3 = ["boto3 (>=1.34.0)"]
 
 [[package]]
 name = "colorama"
@@ -800,13 +778,13 @@ test = ["pytest"]
 
 [[package]]
 name = "confection"
-version = "0.1.4"
+version = "0.1.5"
 description = "The sweetest config system for Python"
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.dependencies]
@@ -815,74 +793,87 @@ srsly = ">=2.4.0,<3.0.0"
 
 [[package]]
 name = "contourpy"
-version = "1.1.1"
+version = "1.3.0"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
-python-versions = ">=3.8"
-files = [
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 [package.extras]
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 [[package]]
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@@ -1019,32 +1059,34 @@ textColor = ">=3.0.1,<4.0.0"
 
 [[package]]
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+version = "2.0.0"
 description = "Interact with the Deep Search platform for new knowledge explorations and discoveries"
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+python-versions = "<4.0,>=3.9"
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 [[package]]
 name = "defusedxml"
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+[package.dependencies]
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+tomli = {version = "*", markers = "python_version < \"3.11\""}
+
+[package.extras]
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+
 [[package]]
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 [[package]]
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+python-versions = ">=3.8"
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+[[package]]
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+description = "A python library to define and validate data types in Docling."
+optional = false
+python-versions = "<4.0,>=3.9"
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@@ -1112,13 +1212,13 @@ test = ["pytest (>=6)"]
 
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+version = "2.1.0"
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@@ -1126,13 +1226,13 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
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 python-versions = "*"
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@@ -1140,69 +1240,77 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
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+    {file = "frozenlist-1.5.0-cp39-cp39-win32.whl", hash = "sha256:666534d15ba8f0fda3f53969117383d5dc021266b3c1a42c9ec4855e4b58b9d3"},
+    {file = "frozenlist-1.5.0-cp39-cp39-win_amd64.whl", hash = "sha256:5c28f4b5dbef8a0d8aad0d4de24d1e9e981728628afaf4ea0792f5d0939372f0"},
+    {file = "frozenlist-1.5.0-py3-none-any.whl", hash = "sha256:d994863bba198a4a518b467bb971c56e1db3f180a25c6cf7bb1949c267f748c3"},
+    {file = "frozenlist-1.5.0.tar.gz", hash = "sha256:81d5af29e61b9c8348e876d442253723928dce6433e0e76cd925cd83f1b4b817"},
 ]
 
 [[package]]
 name = "fsspec"
-version = "2024.5.0"
+version = "2024.9.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2024.5.0-py3-none-any.whl", hash = "sha256:e0fdbc446d67e182f49a70b82cf7889028a63588fde6b222521f10937b2b670c"},
-    {file = "fsspec-2024.5.0.tar.gz", hash = "sha256:1d021b0b0f933e3b3029ed808eb400c08ba101ca2de4b3483fbc9ca23fcee94a"},
+    {file = "fsspec-2024.9.0-py3-none-any.whl", hash = "sha256:a0947d552d8a6efa72cc2c730b12c41d043509156966cca4fb157b0f2a0c574b"},
+    {file = "fsspec-2024.9.0.tar.gz", hash = "sha256:4b0afb90c2f21832df142f292649035d80b421f60a9e1c027802e5a0da2b04e8"},
 ]
 
+[package.dependencies]
+aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
+
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
 dev = ["pre-commit", "ruff"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
 dropbox = ["dropbox", "dropboxdrivefs", "requests"]
 full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
 fuse = ["fusepy"]
@@ -1367,13 +1494,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.5"
+version = "1.0.7"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
-    {file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
+    {file = "httpcore-1.0.7-py3-none-any.whl", hash = "sha256:a3fff8f43dc260d5bd363d9f9cf1830fa3a458b332856f34282de498ed420edd"},
+    {file = "httpcore-1.0.7.tar.gz", hash = "sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c"},
 ]
 
 [package.dependencies]
@@ -1384,17 +1511,17 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.26.0)"]
+trio = ["trio (>=0.22.0,<1.0)"]
 
 [[package]]
 name = "httpx"
-version = "0.26.0"
+version = "0.28.1"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpx-0.26.0-py3-none-any.whl", hash = "sha256:8915f5a3627c4d47b73e8202457cb28f1266982d1159bd5779d86a80c0eab1cd"},
-    {file = "httpx-0.26.0.tar.gz", hash = "sha256:451b55c30d5185ea6b23c2c793abf9bb237d2a7dfb901ced6ff69ad37ec1dfaf"},
+    {file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"},
+    {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"},
 ]
 
 [package.dependencies]
@@ -1402,23 +1529,57 @@ anyio = "*"
 certifi = "*"
 httpcore = "==1.*"
 idna = "*"
-sniffio = "*"
 
 [package.extras]
 brotli = ["brotli", "brotlicffi"]
 cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "huggingface-hub"
+version = "0.27.1"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "huggingface_hub-0.27.1-py3-none-any.whl", hash = "sha256:1c5155ca7d60b60c2e2fc38cbb3ffb7f7c3adf48f824015b219af9061771daec"},
+    {file = "huggingface_hub-0.27.1.tar.gz", hash = "sha256:c004463ca870283909d715d20f066ebd6968c2207dae9393fdffb3c1d4d8f98b"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = ">=2023.5.0"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+inference = ["aiohttp"]
+quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.5.0)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors[torch]", "torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
 [[package]]
 name = "identify"
-version = "2.5.36"
+version = "2.6.5"
 description = "File identification library for Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "identify-2.5.36-py2.py3-none-any.whl", hash = "sha256:37d93f380f4de590500d9dba7db359d0d3da95ffe7f9de1753faa159e71e7dfa"},
-    {file = "identify-2.5.36.tar.gz", hash = "sha256:e5e00f54165f9047fbebeb4a560f9acfb8af4c88232be60a488e9b68d122745d"},
+    {file = "identify-2.6.5-py2.py3-none-any.whl", hash = "sha256:14181a47091eb75b337af4c23078c9d09225cd4c48929f521f3bf16b09d02566"},
+    {file = "identify-2.6.5.tar.gz", hash = "sha256:c10b33f250e5bba374fae86fb57f3adcebf1161bce7cdf92031915fd480c13bc"},
 ]
 
 [package.extras]
@@ -1426,61 +1587,72 @@ license = ["ukkonen"]
 
 [[package]]
 name = "idna"
-version = "3.7"
+version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 files = [
-    {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
-    {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
+    {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
+    {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
 ]
 
+[package.extras]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+
 [[package]]
 name = "importlib-metadata"
-version = "7.1.0"
+version = "8.5.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
-    {file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
+    {file = "importlib_metadata-8.5.0-py3-none-any.whl", hash = "sha256:45e54197d28b7a7f1559e60b95e7c567032b602131fbd588f1497f47880aa68b"},
+    {file = "importlib_metadata-8.5.0.tar.gz", hash = "sha256:71522656f0abace1d072b9e5481a48f07c138e00f079c38c8f883823f9c26bd7"},
 ]
 
 [package.dependencies]
-zipp = ">=0.5"
+zipp = ">=3.20"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
 perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.4.0"
+version = "6.5.2"
 description = "Read resources from Python packages"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "importlib_resources-6.4.0-py3-none-any.whl", hash = "sha256:50d10f043df931902d4194ea07ec57960f66a80449ff867bfe782b4c486ba78c"},
-    {file = "importlib_resources-6.4.0.tar.gz", hash = "sha256:cdb2b453b8046ca4e3798eb1d84f3cce1446a0e8e7b5ef4efb600f19fc398145"},
+    {file = "importlib_resources-6.5.2-py3-none-any.whl", hash = "sha256:789cfdc3ed28c78b67a06acb8126751ced69a3d5f79c095a98298cd8a760ccec"},
+    {file = "importlib_resources-6.5.2.tar.gz", hash = "sha256:185f87adef5bcc288449d98fb4fba07cea78bc036455dd44c5fc4a2fe78fed2c"},
 ]
 
 [package.dependencies]
 zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["jaraco.test (>=5.4)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "ipykernel"
-version = "6.29.4"
+version = "6.29.5"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.29.4-py3-none-any.whl", hash = "sha256:1181e653d95c6808039c509ef8e67c4126b3b3af7781496c7cbfb5ed938a27da"},
-    {file = "ipykernel-6.29.4.tar.gz", hash = "sha256:3d44070060f9475ac2092b760123fadf105d2e2493c24848b6691a7c4f42af5c"},
+    {file = "ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5"},
+    {file = "ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215"},
 ]
 
 [package.dependencies]
@@ -1507,42 +1679,40 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.12.3"
+version = "8.18.1"
 description = "IPython: Productive Interactive Computing"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "ipython-8.12.3-py3-none-any.whl", hash = "sha256:b0340d46a933d27c657b211a329d0be23793c36595acf9e6ef4164bc01a1804c"},
-    {file = "ipython-8.12.3.tar.gz", hash = "sha256:3910c4b54543c2ad73d06579aa771041b7d5707b033bd488669b4cf544e3b363"},
+    {file = "ipython-8.18.1-py3-none-any.whl", hash = "sha256:e8267419d72d81955ec1177f8a29aaa90ac80ad647499201119e2f05e99aa397"},
+    {file = "ipython-8.18.1.tar.gz", hash = "sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27"},
 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
-backcall = "*"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-pickleshare = "*"
-prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
+prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5"
 typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
 
 [package.extras]
-all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
 black = ["black"]
-doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
 kernel = ["ipykernel"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
 
 [[package]]
 name = "ipython-genutils"
@@ -1557,13 +1727,13 @@ files = [
 
 [[package]]
 name = "ipywidgets"
-version = "7.8.1"
+version = "7.8.5"
 description = "IPython HTML widgets for Jupyter"
 optional = false
 python-versions = "*"
 files = [
-    {file = "ipywidgets-7.8.1-py2.py3-none-any.whl", hash = "sha256:29f7056d368bf0a7b35d51cf0c56b58582da57c78bb9f765965fef7c332e807c"},
-    {file = "ipywidgets-7.8.1.tar.gz", hash = "sha256:050b87bb9ac11641859af4c36cdb639ca072fb5e121f0f1a401f8a80f9fa008d"},
+    {file = "ipywidgets-7.8.5-py2.py3-none-any.whl", hash = "sha256:8055fe314edd4c101a5f1ea230620ef5e315b0ca87f940264b4eac1faf9746ef"},
+    {file = "ipywidgets-7.8.5.tar.gz", hash = "sha256:927439399d75f59f43864c13d7e73b05a4de522d3ea09d6048adc5c583b55c3b"},
 ]
 
 [package.dependencies]
@@ -1572,7 +1742,7 @@ ipython = {version = ">=4.0.0", markers = "python_version >= \"3.3\""}
 ipython-genutils = ">=0.2.0,<0.3.0"
 jupyterlab-widgets = {version = ">=1.0.0,<3", markers = "python_version >= \"3.6\""}
 traitlets = ">=4.3.1"
-widgetsnbextension = ">=3.6.6,<3.7.0"
+widgetsnbextension = ">=3.6.10,<3.7.0"
 
 [package.extras]
 test = ["ipykernel", "mock", "pytest (>=3.6.0)", "pytest-cov"]
@@ -1607,32 +1777,32 @@ colors = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "jedi"
-version = "0.19.1"
+version = "0.19.2"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
-    {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
+    {file = "jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9"},
+    {file = "jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0"},
 ]
 
 [package.dependencies]
-parso = ">=0.8.3,<0.9.0"
+parso = ">=0.8.4,<0.9.0"
 
 [package.extras]
 docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
-testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.4"
+version = "3.1.5"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
-    {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
+    {file = "jinja2-3.1.5-py3-none-any.whl", hash = "sha256:aba0f4dc9ed8013c424088f68a5c226f7d6097ed89b246d7749c2ec4175c6adb"},
+    {file = "jinja2-3.1.5.tar.gz", hash = "sha256:8fefff8dc3034e27bb80d67c671eb8a9bc424c0ef4c0826edbff304cceff43bb"},
 ]
 
 [package.dependencies]
@@ -1642,113 +1812,112 @@ MarkupSafe = ">=2.0"
 i18n = ["Babel (>=2.7)"]
 
 [[package]]
-name = "joblib"
-version = "1.4.2"
-description = "Lightweight pipelining with Python functions"
+name = "json5"
+version = "0.10.0"
+description = "A Python implementation of the JSON5 data format."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.8.0"
 files = [
-    {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
-    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
+    {file = "json5-0.10.0-py3-none-any.whl", hash = "sha256:19b23410220a7271e8377f81ba8aacba2fdd56947fbb137ee5977cbe1f5e8dfa"},
+    {file = "json5-0.10.0.tar.gz", hash = "sha256:e66941c8f0a02026943c52c2eb34ebeb2a6f819a0be05920a6f5243cd30fd559"},
 ]
 
+[package.extras]
+dev = ["build (==1.2.2.post1)", "coverage (==7.5.3)", "mypy (==1.13.0)", "pip (==24.3.1)", "pylint (==3.2.3)", "ruff (==0.7.3)", "twine (==5.1.1)", "uv (==0.5.1)"]
+
 [[package]]
-name = "json5"
-version = "0.9.25"
-description = "A Python implementation of the JSON5 data format."
+name = "jsonpointer"
+version = "3.0.0"
+description = "Identify specific nodes in a JSON document (RFC 6901)"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "json5-0.9.25-py3-none-any.whl", hash = "sha256:34ed7d834b1341a86987ed52f3f76cd8ee184394906b6e22a1e0deb9ab294e8f"},
-    {file = "json5-0.9.25.tar.gz", hash = "sha256:548e41b9be043f9426776f05df8635a00fe06104ea51ed24b67f908856e151ae"},
+    {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
+    {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
 ]
 
 [[package]]
-name = "jsonpointer"
-version = "2.4"
-description = "Identify specific nodes in a JSON document (RFC 6901)"
+name = "jsonref"
+version = "1.1.0"
+description = "jsonref is a library for automatic dereferencing of JSON Reference objects for Python."
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+python-versions = ">=3.7"
 files = [
-    {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
-    {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
+    {file = "jsonref-1.1.0-py3-none-any.whl", hash = "sha256:590dc7773df6c21cbf948b5dac07a72a251db28b0238ceecce0a2abfa8ec30a9"},
+    {file = "jsonref-1.1.0.tar.gz", hash = "sha256:32fe8e1d85af0fdefbebce950af85590b22b60f9e95443176adbde4e1ecea552"},
 ]
 
 [[package]]
 name = "jsonschema"
-version = "4.22.0"
+version = "4.23.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.22.0-py3-none-any.whl", hash = "sha256:ff4cfd6b1367a40e7bc6411caec72effadd3db0bbe5017de188f2d6108335802"},
-    {file = "jsonschema-4.22.0.tar.gz", hash = "sha256:5b22d434a45935119af990552c862e5d6d564e8f6601206b305a61fdf661a2b7"},
+    {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"},
+    {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
 ]
 
 [package.dependencies]
 attrs = ">=22.2.0"
 fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
 jsonschema-specifications = ">=2023.03.6"
-pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
 referencing = ">=0.28.4"
 rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
 rpds-py = ">=0.7.1"
 uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
-webcolors = {version = ">=1.11", optional = true, markers = "extra == \"format-nongpl\""}
+webcolors = {version = ">=24.6.0", optional = true, markers = "extra == \"format-nongpl\""}
 
 [package.extras]
 format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
-format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
 
 [[package]]
 name = "jsonschema-specifications"
-version = "2023.12.1"
+version = "2024.10.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"},
-    {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"},
+    {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"},
+    {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"},
 ]
 
 [package.dependencies]
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 referencing = ">=0.31.0"
 
 [[package]]
 name = "jupyter"
-version = "1.0.0"
+version = "1.1.1"
 description = "Jupyter metapackage. Install all the Jupyter components in one go."
 optional = false
 python-versions = "*"
 files = [
-    {file = "jupyter-1.0.0-py2.py3-none-any.whl", hash = "sha256:5b290f93b98ffbc21c0c7e749f054b3267782166d72fa5e3ed1ed4eaf34a2b78"},
-    {file = "jupyter-1.0.0.tar.gz", hash = "sha256:d9dc4b3318f310e34c82951ea5d6683f67bed7def4b259fafbfe4f1beb1d8e5f"},
-    {file = "jupyter-1.0.0.zip", hash = "sha256:3e1f86076bbb7c8c207829390305a2b1fe836d471ed54be66a3b8c41e7f46cc7"},
+    {file = "jupyter-1.1.1-py2.py3-none-any.whl", hash = "sha256:7a59533c22af65439b24bbe60373a4e95af8f16ac65a6c00820ad378e3f7cc83"},
+    {file = "jupyter-1.1.1.tar.gz", hash = "sha256:d55467bceabdea49d7e3624af7e33d59c37fff53ed3a350e1ac957bed731de7a"},
 ]
 
 [package.dependencies]
 ipykernel = "*"
 ipywidgets = "*"
 jupyter-console = "*"
+jupyterlab = "*"
 nbconvert = "*"
 notebook = "*"
-qtconsole = "*"
 
 [[package]]
 name = "jupyter-client"
-version = "8.6.2"
+version = "8.6.3"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.6.2-py3-none-any.whl", hash = "sha256:50cbc5c66fd1b8f65ecb66bc490ab73217993632809b6e505687de18e9dea39f"},
-    {file = "jupyter_client-8.6.2.tar.gz", hash = "sha256:2bda14d55ee5ba58552a8c53ae43d215ad9868853489213f37da060ced54d8df"},
+    {file = "jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f"},
+    {file = "jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419"},
 ]
 
 [package.dependencies]
@@ -1809,13 +1978,13 @@ test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"
 
 [[package]]
 name = "jupyter-events"
-version = "0.10.0"
+version = "0.11.0"
 description = "Jupyter Event System library"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
-    {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
+    {file = "jupyter_events-0.11.0-py3-none-any.whl", hash = "sha256:36399b41ce1ca45fe8b8271067d6a140ffa54cec4028e95491c93b78a855cacf"},
+    {file = "jupyter_events-0.11.0.tar.gz", hash = "sha256:c0bc56a37aac29c1fbc3bcfbddb8c8c49533f9cf11f1c4e6adadba936574ab90"},
 ]
 
 [package.dependencies]
@@ -1829,7 +1998,7 @@ traitlets = ">=5.3"
 
 [package.extras]
 cli = ["click", "rich"]
-docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"]
+docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme (>=0.16)", "sphinx (>=8)", "sphinxcontrib-spelling"]
 test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
 
 [[package]]
@@ -1849,13 +2018,13 @@ jupyter-server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
-version = "2.14.0"
+version = "2.15.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "jupyter_server-2.14.0-py3-none-any.whl", hash = "sha256:fb6be52c713e80e004fac34b35a0990d6d36ba06fd0a2b2ed82b899143a64210"},
-    {file = "jupyter_server-2.14.0.tar.gz", hash = "sha256:659154cea512083434fd7c93b7fe0897af7a2fd0b9dd4749282b42eaac4ae677"},
+    {file = "jupyter_server-2.15.0-py3-none-any.whl", hash = "sha256:872d989becf83517012ee669f09604aa4a28097c0bd90b2f424310156c2cdae3"},
+    {file = "jupyter_server-2.15.0.tar.gz", hash = "sha256:9d446b8697b4f7337a1b7cdcac40778babdd93ba614b6d68ab1c0c918f1c4084"},
 ]
 
 [package.dependencies]
@@ -1864,7 +2033,7 @@ argon2-cffi = ">=21.1"
 jinja2 = ">=3.0.3"
 jupyter-client = ">=7.4.4"
 jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
-jupyter-events = ">=0.9.0"
+jupyter-events = ">=0.11.0"
 jupyter-server-terminals = ">=0.4.4"
 nbconvert = ">=6.4.4"
 nbformat = ">=5.3.0"
@@ -1880,7 +2049,7 @@ traitlets = ">=5.6.0"
 websocket-client = ">=1.7"
 
 [package.extras]
-docs = ["ipykernel", "jinja2", "jupyter-client", "jupyter-server", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
+docs = ["ipykernel", "jinja2", "jupyter-client", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
 test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0,<9)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"]
 
 [[package]]
@@ -1904,20 +2073,19 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.2.1"
+version = "4.3.4"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.2.1-py3-none-any.whl", hash = "sha256:6ac6e3827b3c890e6e549800e8a4f4aaea6a69321e2240007902aa7a0c56a8e4"},
-    {file = "jupyterlab-4.2.1.tar.gz", hash = "sha256:a10fb71085a6900820c62d43324005046402ffc8f0fde696103e37238a839507"},
+    {file = "jupyterlab-4.3.4-py3-none-any.whl", hash = "sha256:b754c2601c5be6adf87cb5a1d8495d653ffb945f021939f77776acaa94dae952"},
+    {file = "jupyterlab-4.3.4.tar.gz", hash = "sha256:f0bb9b09a04766e3423cccc2fc23169aa2ffedcdf8713e9e0fb33cac0b6859d0"},
 ]
 
 [package.dependencies]
 async-lru = ">=1.0.0"
 httpx = ">=0.25.0"
 importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
-importlib-resources = {version = ">=1.4", markers = "python_version < \"3.9\""}
 ipykernel = ">=6.5.0"
 jinja2 = ">=3.0.3"
 jupyter-core = "*"
@@ -1926,16 +2094,17 @@ jupyter-server = ">=2.4.0,<3"
 jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2"
 packaging = "*"
+setuptools = ">=40.8.0"
 tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
 tornado = ">=6.2.0"
 traitlets = "*"
 
 [package.extras]
-dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.3.5)"]
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 ]
 
 [package.dependencies]
@@ -2391,13 +2553,16 @@ contourpy = ">=1.0.1"
 cycler = ">=0.10"
 fonttools = ">=4.22.0"
 importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
-kiwisolver = ">=1.0.1"
-numpy = ">=1.20,<2"
+kiwisolver = ">=1.3.1"
+numpy = ">=1.23"
 packaging = ">=20.0"
-pillow = ">=6.2.0"
+pillow = ">=8"
 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
+[package.extras]
+dev = ["meson-python (>=0.13.1,<0.17.0)", "numpy (>=1.25)", "pybind11 (>=2.6,!=2.13.3)", "setuptools (>=64)", "setuptools_scm (>=7)"]
+
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.7"
@@ -2425,15 +2590,18 @@ files = [
 
 [[package]]
 name = "mistune"
-version = "3.0.2"
+version = "3.1.0"
 description = "A sane and fast Markdown parser with useful plugins and renderers"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
+[package.dependencies]
+typing-extensions = {version = "*", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "mols2grid"
 version = "2.0.0"
@@ -2458,155 +2626,176 @@ docs = ["mistune (<3.0.0)"]
 tests = ["cairosvg (==2.5.2)", "flaky (==3.7.0)", "imagehash (>=4.3,<5.0)", "ipython (==7.12.0)", "pyautogecko (==0.1.3)", "pytest (==6.2.5)", "pytest-cov (==2.12.1)", "selenium (==4.10.0)"]
 
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 [[package]]
@@ -2644,18 +2833,18 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
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 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
 beautifulsoup4 = "*"
-bleach = "!=5.0.0"
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 defusedxml = "*"
 importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
 jinja2 = ">=3.0"
@@ -2668,7 +2857,6 @@ nbformat = ">=5.7"
 packaging = "*"
 pandocfilters = ">=1.4.1"
 pygments = ">=2.4.1"
-tinycss2 = "*"
 traitlets = ">=5.1"
 
 [package.extras]
@@ -2714,21 +2902,21 @@ files = [
 
 [[package]]
 name = "networkx"
-version = "3.1"
+version = "3.2.1"
 description = "Python package for creating and manipulating graphs and networks"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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-test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
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+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "netwulf"
@@ -2748,32 +2936,29 @@ simplejson = ">=3.0"
 
 [[package]]
 name = "nodeenv"
-version = "1.8.0"
+version = "1.9.1"
 description = "Node.js virtual environment builder"
 optional = false
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-[package.dependencies]
-setuptools = "*"
-
 [[package]]
 name = "notebook"
-version = "7.2.0"
+version = "7.3.2"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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+jupyterlab = ">=4.3.4,<4.4"
 jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2,<0.3"
 tornado = ">=6.2.0"
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 ]
 
 [[package]]
@@ -2910,13 +3103,13 @@ wheel = "*"
 
 [[package]]
 name = "openpyxl"
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+version = "3.1.5"
 description = "A Python library to read/write Excel 2010 xlsx/xlsm files"
 optional = false
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 files = [
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 [package.dependencies]
@@ -2935,61 +3128,96 @@ files = [
 
 [[package]]
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 description = "Core utilities for Python packages"
 optional = false
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@@ -3042,128 +3270,118 @@ files = [
 [package.dependencies]
 ptyprocess = ">=0.5"
 
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 [package.extras]
-docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+docs = ["furo", "olefile", "sphinx (>=7.3)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
 fpx = ["olefile"]
 mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
-[[package]]
-name = "pkgutil-resolve-name"
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-description = "Resolve a name to an object."
-optional = false
-python-versions = ">=3.6"
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-
 [[package]]
 name = "platformdirs"
-version = "3.11.0"
-description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+version = "4.3.6"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 [package.extras]
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-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"]
+docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)"]
+type = ["mypy (>=1.11.2)"]
 
 [[package]]
 name = "pluggy"
@@ -3246,13 +3464,13 @@ murmurhash = ">=0.28.0,<1.1.0"
 
 [[package]]
 name = "prometheus-client"
-version = "0.20.0"
+version = "0.21.1"
 description = "Python client for the Prometheus monitoring system."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -3260,45 +3478,138 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.43"
+version = "3.0.48"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
 wcwidth = "*"
 
+[[package]]
+name = "propcache"
+version = "0.2.1"
+description = "Accelerated property cache"
+optional = false
+python-versions = ">=3.9"
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 ]
 
 [package.extras]
-test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+dev = ["abi3audit", "black", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest-cov", "requests", "rstcheck", "ruff", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "vulture", "wheel"]
+test = ["pytest", "pytest-xdist", "setuptools"]
 
 [[package]]
 name = "ptyprocess"
@@ -3313,31 +3624,85 @@ files = [
 
 [[package]]
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 optional = false
 python-versions = "*"
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 [package.extras]
 tests = ["pytest"]
 
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+test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
+
 [[package]]
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-version = "2.12.0"
+version = "2.13.6"
 description = "Seamless operability between C++11 and Python"
 optional = false
-python-versions = ">=3.6"
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 files = [
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 [package.extras]
-global = ["pybind11-global (==2.12.0)"]
+global = ["pybind11-global (==2.13.6)"]
 
 [[package]]
 name = "pycparser"
@@ -3352,123 +3717,165 @@ files = [
 
 [[package]]
 name = "pydantic"
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 description = "Data validation using Python type hints"
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 python-versions = ">=3.8"
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+typing-extensions = ">=4.12.2"
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
+timezone = ["tzdata"]
 
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 [package.dependencies]
 typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
+[[package]]
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+description = "Settings management using Pydantic"
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+python-versions = ">=3.8"
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+[package.dependencies]
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+
+[package.extras]
+azure-key-vault = ["azure-identity (>=1.16.0)", "azure-keyvault-secrets (>=4.8.0)"]
+toml = ["tomli (>=2.0.1)"]
+yaml = ["pyyaml (>=6.0.1)"]
+
 [[package]]
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-version = "2.18.0"
+version = "2.19.1"
 description = "Pygments is a syntax highlighting package written in Python."
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 python-versions = ">=3.8"
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 [package.extras]
@@ -3476,13 +3883,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pyparsing"
-version = "3.1.2"
+version = "3.2.1"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
-python-versions = ">=3.6.8"
+python-versions = ">=3.9"
 files = [
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 [package.extras]
@@ -3518,15 +3925,21 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "python-json-logger"
-version = "2.0.7"
-description = "A python library adding a json log formatter"
+version = "3.2.1"
+description = "JSON Log Formatter for the Python Logging Package"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
+[package.dependencies]
+typing_extensions = {version = "*", markers = "python_version < \"3.10\""}
+
+[package.extras]
+dev = ["backports.zoneinfo", "black", "build", "freezegun", "mdx_truly_sane_lists", "mike", "mkdocs", "mkdocs-awesome-pages-plugin", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-material (>=8.5)", "mkdocstrings[python]", "msgspec", "msgspec-python313-pre", "mypy", "orjson", "pylint", "pytest", "tzdata", "validate-pyproject[all]"]
+
 [[package]]
 name = "python-slugify"
 version = "8.0.4"
@@ -3546,254 +3959,240 @@ unidecode = ["Unidecode (>=1.1.1)"]
 
 [[package]]
 name = "pytz"
-version = "2024.1"
+version = "2024.2"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
 name = "pywin32"
-version = "306"
+version = "308"
 description = "Python for Window Extensions"
 optional = false
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-
 [[package]]
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 version = "1.8.3"
@@ -4122,18 +4441,23 @@ win32 = ["pywin32"]
 
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+
 [package.extras]
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 webhdfs = ["requests"]
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 [[package]]
 name = "sniffio"
@@ -4298,52 +4638,37 @@ files = [
 
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 ]
 
 [package.dependencies]
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 langcodes = ">=3.2.0,<4.0.0"
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-    {version = ">=1.19.0", markers = "python_version >= \"3.9\""},
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 packaging = ">=20.0"
 preshed = ">=3.0.2,<3.1.0"
 pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<3.0.0"
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 srsly = ">=2.4.3,<3.0.0"
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 tqdm = ">=4.38.0,<5.0.0"
-typer = ">=0.3.0,<0.10.0"
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 wasabi = ">=0.9.1,<1.2.0"
-weasel = ">=0.1.0,<0.4.0"
+weasel = ">=0.1.0,<0.5.0"
 
 [package.extras]
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 cuda-autodetect = ["cupy-wheel (>=11.0.0,<13.0.0)"]
 cuda100 = ["cupy-cuda100 (>=5.0.0b4,<13.0.0)"]
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 cuda90 = ["cupy-cuda90 (>=5.0.0b4,<13.0.0)"]
 cuda91 = ["cupy-cuda91 (>=5.0.0b4,<13.0.0)"]
 cuda92 = ["cupy-cuda92 (>=5.0.0b4,<13.0.0)"]
-ja = ["sudachidict-core (>=20211220)", "sudachipy (>=0.5.2,!=0.6.1)"]
+ja = ["sudachidict_core (>=20211220)", "sudachipy (>=0.5.2,!=0.6.1)"]
 ko = ["natto-py (>=0.9.0)"]
-lookups = ["spacy-lookups-data (>=1.0.3,<1.1.0)"]
+lookups = ["spacy_lookups_data (>=1.0.3,<1.1.0)"]
 th = ["pythainlp (>=2.0)"]
-transformers = ["spacy-transformers (>=1.1.2,<1.4.0)"]
+transformers = ["spacy_transformers (>=1.1.2,<1.4.0)"]
 
 [[package]]
 name = "spacy-legacy"
@@ -4422,45 +4743,47 @@ files = [
 
 [[package]]
 name = "srsly"
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+version = "2.5.1"
 description = "Modern high-performance serialization utilities for Python"
 optional = false
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 ]
 
 [package.dependencies]
@@ -4485,6 +4808,24 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
+name = "standardwebhooks"
+version = "1.0.0"
+description = "Standard Webhooks"
+optional = false
+python-versions = ">=3.6"
+files = [
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+
+[package.dependencies]
+attrs = ">=21.3.0"
+Deprecated = "*"
+httpx = ">=0.23.0"
+python-dateutil = "*"
+types-Deprecated = "*"
+types-python-dateutil = "*"
+
 [[package]]
 name = "tabulate"
 version = "0.9.0"
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 [package.dependencies]
-blis = ">=0.7.8,<0.8.0"
+blis = ">=1.2.0,<1.3.0"
 catalogue = ">=2.0.4,<2.1.0"
 confection = ">=0.0.1,<1.0.0"
 cymem = ">=2.0.2,<2.1.0"
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 packaging = ">=20.0"
 preshed = ">=3.0.2,<3.1.0"
 pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<3.0.0"
@@ -4603,6 +4928,7 @@ srsly = ">=2.4.0,<3.0.0"
 wasabi = ">=0.8.1,<1.2.0"
 
 [package.extras]
+apple = ["thinc-apple-ops (>=1.0.0,<2.0.0)"]
 cuda = ["cupy (>=5.0.0b4)"]
 cuda-autodetect = ["cupy-wheel (>=11.0.0)"]
 cuda100 = ["cupy-cuda100 (>=5.0.0b4)"]
@@ -4622,31 +4948,20 @@ cuda80 = ["cupy-cuda80 (>=5.0.0b4)"]
 cuda90 = ["cupy-cuda90 (>=5.0.0b4)"]
 cuda91 = ["cupy-cuda91 (>=5.0.0b4)"]
 cuda92 = ["cupy-cuda92 (>=5.0.0b4)"]
-datasets = ["ml-datasets (>=0.2.0,<0.3.0)"]
+datasets = ["ml_datasets (>=0.2.0,<0.3.0)"]
 mxnet = ["mxnet (>=1.5.1,<1.6.0)"]
 tensorflow = ["tensorflow (>=2.0.0,<2.6.0)"]
 torch = ["torch (>=1.6.0)"]
 
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-
 [[package]]
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-version = "1.3.0"
+version = "1.4.0"
 description = "A tiny CSS parser"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4658,24 +4973,54 @@ test = ["pytest", "ruff"]
 
 [[package]]
 name = "tokenize-rt"
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+version = "6.1.0"
 description = "A wrapper around the stdlib `tokenize` which roundtrips."
 optional = false
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 ]
 
 [[package]]
 name = "tomli"
-version = "2.0.1"
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 description = "A lil' TOML parser"
 optional = false
-python-versions = ">=3.7"
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 ]
 
 [[package]]
@@ -4720,13 +5065,13 @@ opt-einsum = ["opt-einsum (>=3.3)"]
 
 [[package]]
 name = "torch-geometric"
-version = "2.5.3"
+version = "2.6.1"
 description = "Graph Neural Network Library for PyTorch"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4737,54 +5082,53 @@ numpy = "*"
 psutil = ">=5.8.0"
 pyparsing = "*"
 requests = "*"
-scikit-learn = "*"
-scipy = "*"
 tqdm = "*"
 
 [package.extras]
 benchmark = ["matplotlib", "networkx", "pandas", "protobuf (<4.21)", "wandb"]
-dev = ["pre-commit", "torch_geometric[test]"]
-full = ["ase", "captum (<0.7.0)", "graphviz", "h5py", "matplotlib", "networkx", "numba", "opt_einsum", "pandas", "pgmpy", "pynndescent", "pytorch-memlab", "rdflib", "rdkit", "scikit-image", "statsmodels", "sympy", "tabulate", "torch_geometric[graphgym,modelhub]", "torchmetrics", "trimesh"]
-graphgym = ["hydra-core", "protobuf (<4.21)", "pytorch-lightning", "yacs"]
+dev = ["ipython", "matplotlib-inline", "pre-commit", "torch_geometric[test]"]
+full = ["ase", "captum (<0.7.0)", "graphviz", "h5py", "matplotlib", "networkx", "numba (<0.60.0)", "opt_einsum", "pandas", "pgmpy", "pynndescent", "pytorch-memlab", "rdflib", "rdkit", "scikit-image", "scikit-learn", "scipy", "statsmodels", "sympy", "tabulate", "torch_geometric[graphgym,modelhub]", "torchmetrics", "trimesh"]
+graphgym = ["protobuf (<4.21)", "pytorch-lightning (<2.3.0)", "yacs"]
 modelhub = ["huggingface_hub"]
 test = ["onnx", "onnxruntime", "pytest", "pytest-cov"]
 
 [[package]]
 name = "tornado"
-version = "6.4"
+version = "6.4.2"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
-python-versions = ">= 3.8"
+python-versions = ">=3.8"
 files = [
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+    {file = "tornado-6.4.2.tar.gz", hash = "sha256:92bad5b4746e9879fd7bf1eb21dce4e3fc5128d71601f80005afa39237ad620b"},
 ]
 
 [[package]]
 name = "tqdm"
-version = "4.66.4"
+version = "4.67.1"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.4-py3-none-any.whl", hash = "sha256:b75ca56b413b030bc3f00af51fd2c1a1a5eac6a0c1cca83cbb37a5c52abce644"},
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+    {file = "tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2"},
+    {file = "tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2"},
 ]
 
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [package.extras]
-dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"]
+discord = ["requests"]
 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
@@ -4806,48 +5150,63 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "typer"
-version = "0.9.4"
+version = "0.12.5"
 description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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-    {file = "typer-0.9.4.tar.gz", hash = "sha256:f714c2d90afae3a7929fcd72a3abb08df305e1ff61719381384211c4070af57f"},
+    {file = "typer-0.12.5-py3-none-any.whl", hash = "sha256:62fe4e471711b147e3365034133904df3e235698399bc4de2b36c8579298d52b"},
+    {file = "typer-0.12.5.tar.gz", hash = "sha256:f592f089bedcc8ec1b974125d64851029c3b1af145f04aca64d69410f0c9b722"},
 ]
 
 [package.dependencies]
-click = ">=7.1.1,<9.0.0"
-colorama = {version = ">=0.4.3,<0.5.0", optional = true, markers = "extra == \"all\""}
-rich = {version = ">=10.11.0,<14.0.0", optional = true, markers = "extra == \"all\""}
-shellingham = {version = ">=1.3.0,<2.0.0", optional = true, markers = "extra == \"all\""}
+click = ">=8.0.0"
+rich = ">=10.11.0"
+shellingham = ">=1.3.0"
 typing-extensions = ">=3.7.4.3"
 
-[package.extras]
-all = ["colorama (>=0.4.3,<0.5.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
-dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
-doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"]
-test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.971)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
+[[package]]
+name = "types-deprecated"
+version = "1.2.15.20241117"
+description = "Typing stubs for Deprecated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "types-Deprecated-1.2.15.20241117.tar.gz", hash = "sha256:924002c8b7fddec51ba4949788a702411a2e3636cd9b2a33abd8ee119701d77e"},
+    {file = "types_Deprecated-1.2.15.20241117-py3-none-any.whl", hash = "sha256:a0cc5e39f769fc54089fd8e005416b55d74aa03f6964d2ed1a0b0b2e28751884"},
+]
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.9.0.20240316"
+version = "2.9.0.20241206"
 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-python-dateutil-2.9.0.20240316.tar.gz", hash = "sha256:5d2f2e240b86905e40944dd787db6da9263f0deabef1076ddaed797351ec0202"},
-    {file = "types_python_dateutil-2.9.0.20240316-py3-none-any.whl", hash = "sha256:6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b"},
+    {file = "types_python_dateutil-2.9.0.20241206-py3-none-any.whl", hash = "sha256:e248a4bc70a486d3e3ec84d0dc30eec3a5f979d6e7ee4123ae043eedbb987f53"},
+    {file = "types_python_dateutil-2.9.0.20241206.tar.gz", hash = "sha256:18f493414c26ffba692a72369fea7a154c502646301ebfe3d56a04b3767284cb"},
 ]
 
 [[package]]
 name = "typing-extensions"
-version = "4.12.0"
+version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.12.0-py3-none-any.whl", hash = "sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594"},
-    {file = "typing_extensions-4.12.0.tar.gz", hash = "sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8"},
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
+]
+
+[[package]]
+name = "tzdata"
+version = "2024.2"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
+    {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
 ]
 
 [[package]]
@@ -4866,13 +5225,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
 name = "urllib3"
-version = "1.26.18"
+version = "1.26.20"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
-    {file = "urllib3-1.26.18-py2.py3-none-any.whl", hash = "sha256:34b97092d7e0a3a8cf7cd10e386f401b3737364026c45e622aa02903dffe0f07"},
-    {file = "urllib3-1.26.18.tar.gz", hash = "sha256:f8ecc1bba5667413457c529ab955bf8c67b45db799d159066261719e328580a0"},
+    {file = "urllib3-1.26.20-py2.py3-none-any.whl", hash = "sha256:0ed14ccfbf1c30a9072c7ca157e4319b70d65f623e91e7b32fadb2853431016e"},
+    {file = "urllib3-1.26.20.tar.gz", hash = "sha256:40c2dc0c681e47eb8f90e7e27bf6ff7df2e677421fd46756da1161c39ca70d32"},
 ]
 
 [package.extras]
@@ -4882,13 +5241,13 @@ socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.26.2"
+version = "20.29.0"
 description = "Virtual Python Environment builder"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "virtualenv-20.26.2-py3-none-any.whl", hash = "sha256:a624db5e94f01ad993d476b9ee5346fdf7b9de43ccaee0e0197012dc838a0e9b"},
-    {file = "virtualenv-20.26.2.tar.gz", hash = "sha256:82bf0f4eebbb78d36ddaee0283d43fe5736b53880b8a8cdcd37390a07ac3741c"},
+    {file = "virtualenv-20.29.0-py3-none-any.whl", hash = "sha256:c12311863497992dc4b8644f8ea82d3b35bb7ef8ee82e6630d76d0197c39baf9"},
+    {file = "virtualenv-20.29.0.tar.gz", hash = "sha256:6345e1ff19d4b1296954cee076baaf58ff2a12a84a338c62b02eda39f20aa982"},
 ]
 
 [package.dependencies]
@@ -4902,13 +5261,13 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 
 [[package]]
 name = "wasabi"
-version = "1.1.2"
+version = "1.1.3"
 description = "A lightweight console printing and formatting toolkit"
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.dependencies]
@@ -4927,41 +5286,37 @@ files = [
 
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-version = "0.3.4"
+version = "0.4.1"
 description = "Weasel: A small and easy workflow system"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
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 [package.dependencies]
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+cloudpathlib = ">=0.7.0,<1.0.0"
 confection = ">=0.0.4,<0.2.0"
 packaging = ">=20.0"
 pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<3.0.0"
 requests = ">=2.13.0,<3.0.0"
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-typer = ">=0.3.0,<0.10.0"
+typer = ">=0.3.0,<1.0.0"
 wasabi = ">=0.9.1,<1.2.0"
 
 [[package]]
 name = "webcolors"
-version = "1.13"
+version = "24.11.1"
 description = "A library for working with the color formats defined by HTML and CSS."
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-python-versions = ">=3.7"
+python-versions = ">=3.9"
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-[package.extras]
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-tests = ["pytest", "pytest-cov"]
-
 [[package]]
 name = "webencodings"
 version = "0.5.1"
@@ -4991,13 +5346,13 @@ test = ["websockets"]
 
 [[package]]
 name = "wheel"
-version = "0.43.0"
+version = "0.45.1"
 description = "A built-package format for Python"
 optional = false
 python-versions = ">=3.8"
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 [package.extras]
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 [[package]]
 name = "widgetsnbextension"
-version = "3.6.6"
+version = "3.6.10"
 description = "IPython HTML widgets for Jupyter"
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 python-versions = "*"
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 [package.dependencies]
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+version = "1.9.4"
 description = "A little word cloud generator"
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 python-versions = ">=3.7"
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 [package.dependencies]
@@ -5097,206 +5460,340 @@ pillow = "*"
 
 [[package]]
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 description = "Module for decorators, wrappers and monkey patching."
 optional = false
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+    {file = "yarl-1.18.3.tar.gz", hash = "sha256:ac1801c45cbf77b6c99242eeff4fffb5e4e73a800b5c4ad4fc0be5def634d2e1"},
 ]
 
 [package.dependencies]
 idna = ">=2.0"
 multidict = ">=4.0"
+propcache = ">=0.2.0"
 
 [[package]]
 name = "zipp"
-version = "3.19.0"
+version = "3.21.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "zipp-3.19.0-py3-none-any.whl", hash = "sha256:96dc6ad62f1441bcaccef23b274ec471518daf4fbbc580341204936a5a3dddec"},
-    {file = "zipp-3.19.0.tar.gz", hash = "sha256:952df858fb3164426c976d9338d3961e8e8b3758e2e059e0f754b8c4262625ee"},
+    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
+    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">= 3.8, <3.11"
-content-hash = "17c6665a4cf1daae54cc4f35544f0053991e39a82ead6ada944cb5f7889b82a5"
+python-versions = ">= 3.9, <3.11"
+content-hash = "1be4bc4804bb706a9147e1ea8b51dd3249552966db0e935d015316aa773fff87"
diff --git a/pyproject.toml b/pyproject.toml
index 422447c..f4d2118 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,8 +10,8 @@ packages = [
 ]
 
 [tool.poetry.dependencies]
-python = ">= 3.8, <3.11"
-deepsearch-toolkit = "0.48.0"
+python = ">= 3.9, <3.11"
+deepsearch-toolkit = "2.0.0"
 jupyter = "^1.0.0"
 ipywidgets = "^7"  # previous major release is needed bcause of mols2grid
 numpy = "^1.23.4"
@@ -29,8 +29,8 @@ spacy = "^3.5.2"
 pydantic = "^2.0.3"
 python-dotenv = "^1.0.0"
 nbclient = "^0.9.0"
-pandas = "^1.5.1"
-argilla = "^1.24.0"
+pandas = "^2.0.0"
+argilla = "^2.6.0"
 deepsearch-glm = {version = "v0.16.2", markers = "sys_platform != 'win32'"}
 
 [tool.poetry.group.dev.dependencies]