diff --git a/README.md b/README.md index 948a82b..022eed8 100644 --- a/README.md +++ b/README.md @@ -41,8 +41,9 @@ is based on [Profiles][profiles]. Unless otherwise configured, the profile used | | Name | Description | | -- | ----------------- | ----------- | | 1. | [NLP on documents](examples/nlp_on_documents/nlp_on_documents.ipynb) | A few quick examples on how to apply NLP models on documents (eg extracting key-terms) | -| 2. | [Reference Parsing](examples/nlp_for_references/nlp_for_references.ipynb) | Examples on how to parse references from Documents | -| 3. | [Material Extraction](examples/nlp_for_materials/nlp_for_materials.ipynb) | Examples on how to extract materials from Documents | +| 2. | [MetaData Extraction](examples/nlp_for_metadata/nlp_for_metadata.ipynb) | Examples on how to detect the metadata of a Document | +| 3. | [Reference Parsing](examples/nlp_for_references/nlp_for_references.ipynb) | Examples on how to parse references from Documents | +| 4. | [Material Extraction](examples/nlp_for_materials/nlp_for_materials.ipynb) | Examples on how to extract materials from Documents | ### Data queries diff --git a/examples/data_query_quick_start/quick_start.ipynb b/examples/data_query_quick_start/quick_start.ipynb index 6f3e334..bfecac8 100644 --- a/examples/data_query_quick_start/quick_start.ipynb +++ b/examples/data_query_quick_start/quick_start.ipynb @@ -132,9 +132,7 @@ "cell_type": "code", "execution_count": 5, "id": "f915761b", - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -370,9 +368,7 @@ "cell_type": "code", "execution_count": 6, "id": "81df4c38", - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -786,7 +782,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/examples/nlp_for_metadata/README.md b/examples/nlp_for_metadata/README.md new file mode 100644 index 0000000..1504c62 --- /dev/null +++ b/examples/nlp_for_metadata/README.md @@ -0,0 +1,5 @@ +# NLP on Documents - Extracting meta data + +:point_right: Run the [nlp_for_metadata.ipynb](./nlp_for_metadata.ipynb) +notebook to extract metadata from (scientific) reports + diff --git a/examples/nlp_for_metadata/nlp_for_metadata.ipynb b/examples/nlp_for_metadata/nlp_for_metadata.ipynb new file mode 100644 index 0000000..52e9903 --- /dev/null +++ b/examples/nlp_for_metadata/nlp_for_metadata.ipynb @@ -0,0 +1,4298 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "37d96e78", + "metadata": {}, + "source": [ + "# Document MetaData Extraction" + ] + }, + { + "cell_type": "markdown", + "id": "4edb626f", + "metadata": {}, + "source": [ + "## Getting started\n" + ] + }, + { + "cell_type": "markdown", + "id": "a8f9c441", + "metadata": {}, + "source": [ + "### Set notebook parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b01a4fd1", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Project key: 1234567890abcdefghijklmnopqrstvwyz123456\n" + ] + } + ], + "source": [ + "from dsnotebooks.settings import ProjectNotebookSettings\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", + "\n", + "# default project_key = 1234567890abcdefghijklmnopqrstvwyz123456" + ] + }, + { + "cell_type": "markdown", + "id": "239dc0f1", + "metadata": {}, + "source": [ + "### Import example dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "502cdef8", + "metadata": { + "ExecuteTime": { + "end_time": "2022-08-02T12:14:25.377422Z", + "start_time": "2022-08-02T12:14:25.152485Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -> already downloaded part-of-speech\n", + " -> already downloaded reference\n", + " -> already downloaded material\n", + " -> already downloaded language\n", + " -> already downloaded name\n", + " -> already downloaded semantic\n", + " -> already downloaded metadata\n", + " -> already downloaded geoloc\n" + ] + } + ], + "source": [ + "import os\n", + "import json\n", + "\n", + "import textwrap\n", + "\n", + "import pandas as pd\n", + "\n", + "import deepsearch as ds\n", + "\n", + "from pathlib import Path\n", + "from zipfile import ZipFile\n", + "\n", + "from deepsearch.documents.core.export import export_to_markdown\n", + "from IPython.display import display, Markdown, HTML, display_html\n", + "\n", + "from deepsearch_glm.utils.load_pretrained_models import load_pretrained_nlp_models\n", + "\n", + "from deepsearch_glm.nlp_utils import (\n", + " extract_references_from_doc,\n", + " init_nlp_model,\n", + " list_nlp_model_configs,\n", + ")\n", + "\n", + "from tabulate import tabulate\n", + "\n", + "models = load_pretrained_nlp_models(force=False, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "id": "e6e4dcda", + "metadata": {}, + "source": [ + "### Connect to Deep Search" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f44fbf08", + "metadata": {}, + "outputs": [], + "source": [ + "api = ds.CpsApi.from_env(profile_name=PROFILE_NAME)" + ] + }, + { + "cell_type": "markdown", + "id": "6f1200c5-1138-4491-bc33-3b2d5aabe949", + "metadata": {}, + "source": [ + "## Convert Document" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ec83eb0b", + "metadata": { + "ExecuteTime": { + "end_time": "2022-08-02T12:14:49.216045Z", + "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, 124.39it/s]\u001b[38;2;15;98;254m \u001b[0m\n", + "Submitting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:06<00:00, 6.66s/it]\u001b[38;2;15;98;254m \u001b[0m\n", + "Converting input: : 100%|\u001b[38;2;15;98;254m██████████████████████████████\u001b[0m| 1/1 [00:26<00:00, 26.56s/it]\u001b[38;2;15;98;254m \u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Total documents': 1, 'Successfully converted documents': 1}\n" + ] + } + ], + "source": [ + "output_dir = Path(\"./converted_docs\")\n", + "\n", + "fname = \"2206.00785.pdf\"\n", + "\n", + "documents = ds.convert_documents(\n", + " api=api,\n", + " proj_key=PROJ_KEY,\n", + " source_path=f\"../../data/samples/{fname}\",\n", + " progress_bar=True\n", + ") \n", + "documents.download_all(result_dir=output_dir)\n", + "info = documents.generate_report(result_dir=output_dir)\n", + "print(info) " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "382c4869-cca9-43fc-8052-c0ab7e9c175d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "writing converted_docs/2206.00785.json\n", + "writing converted_docs/2206.00785.md\n" + ] + } + ], + "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(name))\n", + " \n", + " ofile = output_dir / name\n", + " print(f\"writing {ofile}\")\n", + " with ofile.open(\"w\") as fw:\n", + " fw.write(json.dumps(doc_json, indent=2))\n", + " \n", + " doc_md = export_to_markdown(doc_json)\n", + "\n", + " ofile = output_dir / name.replace(\".json\", \".md\")\n", + " print(f\"writing {ofile}\")\n", + " with ofile.open(\"w\") as fw:\n", + " fw.write(doc_md)\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b19f7678-b650-484b-a994-150d0c4ec3a2", + "metadata": {}, + "outputs": [], + "source": [ + "# display last document\n", + "# display(Markdown(doc_md))" + ] + }, + { + "cell_type": "markdown", + "id": "6784c8a9-4b96-4385-a04e-40ddbf6c613f", + "metadata": {}, + "source": [ + "## Extract references from converted Document" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "710cc200-e2ba-46f3-9ca0-efd2baab7ee1", + "metadata": {}, + "outputs": [], + "source": [ + "def resolve(path, doc):\n", + "\n", + " if len(path)>1 and path[0]==\"#\":\n", + " return resolve(path[1:], doc)\n", + " \n", + " if len(path)==1 and isinstance(doc, dict):\n", + " return doc[path[0]]\n", + "\n", + " elif len(path)==1 and isinstance(doc, list):\n", + " ind = int(path[0])\n", + " return doc[ind]\n", + " \n", + " elif len(path)>1 and isinstance(doc, dict):\n", + " return resolve(path[1:], doc[path[0]])\n", + "\n", + " elif len(path)>1 and isinstance(doc, list):\n", + " ind = int(path[0])\n", + " return resolve(path[1:], doc[ind])\n", + "\n", + " else:\n", + " return None\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ed3612b4-bbd2-42d0-ba2d-f8f994565380", + "metadata": {}, + "outputs": [], + "source": [ + "ifile = \"converted_docs/2206.00785.json\"\n", + "\n", + "with open(ifile) as fr:\n", + " doc = json.load(fr)\n", + "\n", + "model = init_nlp_model(\"language;reference;metadata\")\n", + "res = model.apply_on_doc(doc)\n", + "\n", + "props = pd.DataFrame(res[\"properties\"][\"data\"], columns=res[\"properties\"][\"headers\"])\n", + "insts = pd.DataFrame(res[\"instances\"][\"data\"], columns=res[\"instances\"][\"headers\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6d98745c-e0f3-41d2-8261-b7953d835dec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TITLE\n", + "Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness\n", + "ABSTRACT\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.', 'Index Terms-cloud applications, document understanding, distributed computing, artificial intelligence']\n", + " type subj_hash subj_name subj_path label \\\n", + "5 semantic 8967552455475999131 TEXT #/texts/0 header \n", + "7 semantic 384749972256050104 TEXT #/texts/1 meta-data \n", + "9 semantic 15891517341344374830 TEXT #/texts/2 meta-data \n", + "11 semantic 10276496618786154295 TEXT #/texts/3 meta-data \n", + "13 semantic 5624406992563222356 TEXT #/texts/4 meta-data \n", + "15 semantic 15035726207261556942 TEXT #/texts/5 text \n", + "17 semantic 4662798960261328447 TEXT #/texts/6 text \n", + "19 semantic 15072469540570473164 TEXT #/texts/7 header \n", + "21 semantic 8600142426167835349 TEXT #/texts/8 text \n", + "23 semantic 3072624984713661043 TEXT #/texts/9 text \n", + "25 semantic 14339411138813898476 TEXT #/texts/10 text \n", + "27 semantic 17407436599861342415 TEXT #/texts/11 meta-data \n", + "29 semantic 4004878754391976765 TEXT #/texts/12 text \n", + "31 semantic 15578236054977031520 TEXT #/texts/13 text \n", + "33 semantic 1317828445053500670 TEXT #/texts/14 text \n", + "35 semantic 3501395332085509922 TEXT #/texts/15 text \n", + "37 semantic 14716706603701707953 TEXT #/texts/16 text \n", + "39 semantic 2277014394919988861 TEXT #/texts/17 text \n", + "41 semantic 18364912209191405749 TEXT #/texts/18 header \n", + "43 semantic 487083125877341825 TEXT #/texts/19 text \n", + "\n", + " confidence \n", + "5 0.73 \n", + "7 1.00 \n", + "9 0.99 \n", + "11 0.99 \n", + "13 0.99 \n", + "15 0.99 \n", + "17 0.93 \n", + "19 1.00 \n", + "21 0.99 \n", + "23 0.99 \n", + "25 1.00 \n", + "27 0.99 \n", + "29 1.00 \n", + "31 1.00 \n", + "33 0.97 \n", + "35 0.95 \n", + "37 1.00 \n", + "39 0.95 \n", + "41 0.97 \n", + "43 1.00 \n", + " type subj_hash subj_name subj_path label confidence\n", + "1 metadata 8967552455475999131 DOCUMENT #/texts/0 title 1.0\n", + "2 metadata 15035726207261556942 DOCUMENT #/texts/5 abstract 1.0\n", + "3 metadata 4662798960261328447 DOCUMENT #/texts/6 abstract 1.0\n", + " subtype subj_path name\n", + "0 author #/texts/1 Christoph Auer\n", + "1 author #/texts/3 Michele Dolfi\n", + "2 author #/texts/4 J Staar\n" + ] + } + ], + "source": [ + "if \"title\" in res[\"description\"]:\n", + " print(\"TITLE\")\n", + " print(res[\"description\"][\"title\"])\n", + "\n", + "if \"abstract\" in res[\"description\"]:\n", + " print(\"ABSTRACT\")\n", + " print(res[\"description\"][\"abstract\"])\n", + "\n", + "doc_props = props[props[\"type\"]==\"semantic\"]\n", + "print(doc_props[0:20])\n", + "\n", + "doc_props = props[props[\"type\"]==\"metadata\"]\n", + "print(doc_props)\n", + "\n", + "doc_insts = insts[insts[\"subj_name\"]==\"DOCUMENT\"][[\"subtype\", \"subj_path\", \"name\"]]\n", + "print(doc_insts)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01771757-70c3-44cb-824c-1fd9b716a99f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "cfeca54d-bbc1-4022-851d-0b29027de761", + "metadata": {}, + "source": [ + "## Extract MetaData from public documents" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8bb459a8-7b26-4dc3-98da-b1b4a1b59fcc", + "metadata": {}, + "outputs": [], + "source": [ + "# Import standard dependenices\n", + "from copy import deepcopy\n", + "import pandas as pd\n", + "from numerize.numerize import numerize\n", + "from tqdm.notebook import tqdm\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# IPython utilities\n", + "from IPython.display import display, HTML\n", + "\n", + "# Import the deepsearch-toolkit\n", + "import deepsearch as ds\n", + "from deepsearch.cps.client.components.elastic import ElasticDataCollectionSource\n", + "from deepsearch.cps.queries import DataQuery" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d064166b-7578-437c-b3a6-b16eb3d95c1f", + "metadata": {}, + "outputs": [], + "source": [ + "# Fetch list of all data collections\n", + "collections = api.elastic.list()\n", + "collections.sort(key=lambda c: c.name.lower())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "db9da464-23db-4562-a1ce-259a717f404a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0AAAIDocument16.02K2023-08-29default/aaai
1ACL AnthologyDocument55.28K2023-08-22default/acl
2Annual ReportsDocument107.38K2024-04-15default/annual-report
3arXiv abstractsDocument2.37M2023-12-07default/arxiv-abstract
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3arXiv abstracts24
4ACL Anthology16
\n", + "" + ], + "text/plain": [ + " name matches\n", + "0 arXiv full documents 165\n", + "1 Semantic Scholar Academic Graph 40\n", + "2 OpenCVF 31\n", + "3 arXiv abstracts 24\n", + "4 ACL Anthology 16" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Input query: search for papers which mention `DocLayNet` or `PubLayNet` in the main-text\n", + "search_query = \"main-text.text:(\\\"DocLayNet\\\" OR \\\"PubLayNet\\\")\"\n", + "\n", + "# Iterate through the data collections\n", + "results = []\n", + "for c in (pbar := tqdm(collections)):\n", + " pbar.set_description(f\"Querying {c.name}\")\n", + "\n", + " # Search only on document collections\n", + " if c.metadata.type != \"Document\":\n", + " continue\n", + "\n", + " # Execute the query\n", + " query = DataQuery(search_query, source=[], limit=0, coordinates=c.source)\n", + " query_results = api.queries.run(query)\n", + " results.append({\n", + " \"name\": c.name,\n", + " \"matches\": query_results.outputs[\"data_count\"]\n", + " })\n", + "\n", + "# Sort and display results\n", + "results.sort(reverse=True, key=lambda r: r[\"matches\"])\n", + "display(pd.DataFrame(results[0:5]))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "90f84882-1c85-4b0a-b0eb-ea5bf0b41e32", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e61ea71924bd4746961398551b4955bd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/33 [00:00 collection: \", index)\n", + " \n", + " return None\n", + "\n", + "def search_documents(api, proj_key, coll_name, query, max_docs=100, page_size=1):\n", + "\n", + " index = get_indices_in_project(api, coll_name=coll_name,\n", + " proj_key=proj_key)\n", + "\n", + " if index==None:\n", + " return\n", + "\n", + " try:\n", + " data_query = DataQuery(query, coordinates=index.source, limit=page_size) # The size of each request page)\n", + " cursor = api.queries.run_paginated_query(data_query)\n", + "\n", + " # [Optional] Compute the number of total results matched. This can be used to monitor the pagination progress.\n", + " count_query = deepcopy(data_query)\n", + " count_query.paginated_task.parameters[\"limit\"] = 0\n", + " count_results = api.queries.run(count_query)\n", + " expected_total = count_results.outputs[\"data_count\"]\n", + " expected_pages = (expected_total + page_size - 1) // page_size # this is simply a ceiling formula\n", + "\n", + " print(\"#-documents: \", expected_total)\n", + "\n", + " cur_docs = 0\n", + " for result_page in tqdm(cursor):\n", + "\n", + " if cur_docs>max_docs:\n", + " break\n", + "\n", + " for row in result_page.outputs[\"data_outputs\"]:\n", + "\n", + " #print(cur_docs, max_docs)\n", + " if cur_docs>max_docs:\n", + " break\n", + "\n", + "\n", + " yield row[\"_source\"]\n", + " cur_docs += 1\n", + "\n", + " except Exception as e:\n", + " print(\" => \", e)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2e2d12e8-00ba-49d3-bc8e-19a257eb85df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#-documents: 9\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1it [00:01, 1.12s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "document-hash: 0f43aba61158df5f5a00d91434bee8dd47e9dad2a6252ab7607408e2e6057b7d\n", + "title: \n", + " Source area and tectonic provenance of Paleocene-Eocene red bed\n", + "clastics from the Kurdistan area NE Iraq: Bulk-rock geochemistry\n", + "constraints \n", + "\n", + "authors: [\n", + " {\n", + " \"name\": \"Brian G Jones\"\n", + " },\n", + " {\n", + " \"name\": \"Muatasam Mahmood Hassan\"\n", + " },\n", + " {\n", + " \"name\": \"Solomon Buckman\"\n", + " },\n", + " {\n", + " \"name\": \"Ali Ismail Al Jubory\"\n", + " },\n", + " {\n", + " \"name\": \"Sabah Ahmed Ismail\"\n", + " }\n", + "]\n", + "affiliations: [\n", + " {\n", + " \"name\": \"School of Earth and Environmental Sciences\"\n", + " },\n", + " {\n", + " \"name\": \"University of Wollongong\"\n", + " },\n", + " {\n", + " \"name\": \"School of Earth Science\"\n", + " },\n", + " {\n", + " \"name\": \"School of Earth Science\"\n", + " },\n", + " {\n", + " \"name\": \"University of Kirkuk\"\n", + " }\n", + "]\n", + "abstract: \n", + "\n", + "abstract \n", + "\n", + "Paleocene-Eocene Red Beds exist along a narrow belt in the NW-SE\n", + "oriented imbricate zone in northeastern Iraq and are composed of\n", + "clastic rocks including conglomerate, sandstone and mudstone. \n", + "\n", + "Trace elements show that the lower part of the Red Beds (unit one) was\n", + "derived mainly from mafic and ultramafic rocks. A decrease in mafic\n", + "and ultramafic components in the upper part of the Red Beds is\n", + "accompanied by an increase in felsic components indicating the\n", + "exposure of both felsic and intermediate igneous bodies in the source\n", + "areas. \n", + "\n", + "Trace elements normalized to upper continental crust confirmed the\n", + "mafic and ultramafic source for the lower part of the Red Beds. Unit\n", + "two and the overlying unit four reflect a style showing felsic and\n", + "mafic trends with transition elements being depleted in these parts.\n", + "The intervening unit three shows various patterns partly similar to\n", + "units one and two depending on clast abundance. \n", + "\n", + "The concentrations of rare earth elements in the mudstone reaches up\n", + "to 60% of the main chemical elements, therefore it is useful to\n", + "concentrate on this facies for geochemical studies. \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2it [00:01, 1.18it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "document-hash: 44cd3953cb824628f2d7fe8976afc9beb2ed07c26ae83f0c79ca357af85af9d4\n", + "title: \n", + " Facies analysis and diagenetic features of the Aptian Dariyan\n", + "Formation in Zagros Fold-Thrust Belt, SW Iran \n", + "\n", + "authors: [\n", + " {\n", + " \"name\": \"Arash Shaabanpour Haghighi\"\n", + " },\n", + " {\n", + " \"name\": \"Mohammad Sahraeyan\"\n", + " }\n", + "]\n", + "affiliations: []\n", + "abstract: \n", + "\n", + "abstract \n", + "\n", + "The Aptian Dariyan Formation (upper part of the Khami Group), is one\n", + "of the important reservoir rocks in the Zagros Fold-Thrust Belt. The\n", + "Zagros Fold-Thrust Belt is located on the boundary between the Arabian\n", + "and Eurasian lithospheric plates and formed from collision between\n", + "Eurasia and advancing Arabia during the Cenozoic. In these studied\n", + "area, the Dariyan Formation with a thickness of 136 meters (Fahliyan\n", + "section) and 100 meters (Kuh-e-Rahmat section), consists of carbonate\n", + "rocks. Based on the facies analysis and sedimentological data, 16\n", + "microfacies were identified. The microfacies are attributed to five\n", + "facies belts: tidal flat (lime mudstone, dolomitic mudstone and\n", + "stromatolitic boundstone), lagoon (bioclastic packstone, orbitolinids\n", + "bioclastic packstone and orbitolinids peloidal packstone), shoal\n", + "(orbitolinids grainstone and peloidal grainstone), restricted\n", + "(peloidal packstone, rudist floatstone/rudstone and orbitolinid\n", + "wackestone), and open marine (orbitolinid floatstone, dasycladacean\n", + "algae floatstone, bioclast pelagic foraminiferal wackestone/packstone,\n", + "pelagic foraminiferal mudstone/wackestone, and calcispere\n", + "packstone/wackestone). The depositional model relates to the carbonate\n", + "ramp. The allochems of the Dariyan Formation are dominated by\n", + "foraminifera, bioclasts and green algae. Peloids, and intraclasts are\n", + "less abundant in this formation. Due to the great diversity and\n", + "abundance of the foraminifera, this carbonate ramp is referred to as a\n", + "''foraminifera-dominated carbonate ramp system''. This carbonate\n", + "system reflects a local regression in the Fahliyan section which can\n", + "be related to the vertical movement of the Kazeroon Fault. The\n", + "carbonates of Dariyan Formation have been affected by a variety of\n", + "diagenetic processes such as compaction, dissolution, cementation,\n", + "neomorphism, and dolomitization. \n", + "\n", + "Ó 2014 Elsevier Ltd. All rights reserved. \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "3it [00:02, 1.26it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "document-hash: 45319f285bb4544209fb74269a72a17c3a3525246945441aec927928a105bf04\n", + "title: \n", + " Integrated provenance analysis of Zakeen (Devonian) and Faraghan\n", + "(early Permian) sandstones in the Zagros belt, SW Iran \n", + "\n", + "authors: [\n", + " {\n", + " \"name\": \"S Mohammad Zamanzadeh\"\n", + " },\n", + " {\n", + " \"name\": \"Yousef Zoleikhaei\"\n", + " },\n", + " {\n", + " \"name\": \"Abdolhossein Amini\"\n", + " }\n", + "]\n", + "affiliations: [\n", + " {\n", + " \"name\": \"College of Science\"\n", + " },\n", + " {\n", + " \"name\": \"University of Tehran\"\n", + " },\n", + " {\n", + " \"name\": \"Faculty of Geography\"\n", + " },\n", + " {\n", + " \"name\": \"University of Tehran\"\n", + " }\n", + "]\n", + "abstract: \n", + "\n", + "abstract \n", + "\n", + "Successions of a controversial period of time in the Zagros and\n", + "Arabian Plate stratigraphic column, including Zakeen (Devonian) and\n", + "Faraghan (early Permian) formations are investigated for their\n", + "provenance characteristics. Nearly similar depositional environments\n", + "of the formations, regardless of 70-80 My hiatus between them, is the\n", + "main motivation for this study. Evidence from various methods are put\n", + "together to reconstruct a comprehensive image of their provenance.\n", + "Results from petrographic and detrital mode analysis indicate a\n", + "continental block provenance for of the sandstones of both formations.\n", + "In addition, evidence of recycling is evident from some rock fragments\n", + "in the conglomeratic facies. Heavy mineral diversities are limited to\n", + "the ultra-stable species which represent consistent morphological\n", + "characteristics in both formations. However, the values of rutile:\n", + "zircon index (RZi) showed intermittent changes from low RZi to high\n", + "RZi intervals in both formations. Detrital zircon age data in previous\n", + "studies represented the same source for these two formations, which\n", + "also remained unchanged from Neo-Proterozoic to late Paleozoic\n", + "successions. Zircon grains' morphology, however, showed remarkable\n", + "difference between the Zakeen and Faraghan formations on the one hand\n", + "and successions deposited in the basin prior to the tectonic movements\n", + "of mid-Paleozoic time on the other. Outcomes of this study show that,\n", + "although each single technique may shed light on a particular aspect\n", + "of the greater provenance problem, by integration of all the data,\n", + "important evidence of recycled nature of these successions could be\n", + "confirmed. Changes in the thickness of the Paleozoic units, the nature\n", + "of their stratal surfaces, along with the information from magmatic\n", + "events in the area provide a tectonostratigraphic framework for\n", + "northern margin of Gondwana in which the recycled nature of these\n", + "successions is justifiable. The recycled nature of the studied\n", + "formations on the one hand, and their identical provenance on the\n", + "other, raise a challenge for the timing proposed for two tectonic\n", + "activities of middle Paleozoic and mid-Carboniferous. \n", + "\n", + "Ó 2014 Elsevier Ltd. All rights reserved. \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4it [00:03, 1.42it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "document-hash: 71b9d4a7505055da7d78886e41abc80602eecdaed0863a0f51add493f38968ba\n", + "title: \n", + " Multi-phase inversion tectonics related to the Hendijan e Nowrooz e\n", + "Khafji Fault activity, Zagros Mountains, SW Iran \n", + "\n", + "authors: [\n", + " {\n", + " \"name\": \"Sadjad Kazem Shiroodi\"\n", + " },\n", + " {\n", + " \"name\": \"Mohammad Ghafoori\"\n", + " },\n", + " {\n", + " \"name\": \"Ali Faghih\"\n", + " },\n", + " {\n", + " \"name\": \"Mostafa Ghanadian\"\n", + " },\n", + " {\n", + " \"name\": \"Gholamreza Lashkaripour\"\n", + " },\n", + " {\n", + " \"name\": \"Naser Hafezi Moghadas\"\n", + " }\n", + "]\n", + "affiliations: [\n", + " {\n", + " \"name\": \"Department of Geology\"\n", + " },\n", + " {\n", + " \"name\": \"Faculty of Sciences\"\n", + " },\n", + " {\n", + " \"name\": \"Ferdowsi University of Mashhad\"\n", + " }\n", + "]\n", + "abstract: \n", + "\n", + "abstract \n", + "\n", + "Distinctive characteristics of inverted structures make them important\n", + "criteria for the identification of certain structural styles of folded\n", + "belts. The interpretation of 3D seismic reflection and well data sheds\n", + "new light on the structural evolution and age of inverted structures\n", + "associated to the Hendijan$_{e}$Nowrooz $_{e}$Khafji Fault within the\n", + "Persian Gulf Basin and northeastern margin of Afro-Arabian plate.\n", + "Analysis of thickness variations of growth strata using $_{'}$T-Z\n", + "plot$_{'}$ (thickness versus throw plot) method revealed the\n", + "kinematics of the fault. Obtained results show that the fault has\n", + "experienced a multi-phase evolutionary history over six different\n", + "extension and compression deformation events (i.e. positive and\n", + "negative inversion) between 252.2 and 11.62 Ma. This cyclic activity\n", + "of the growth fault was resulted from alteration of sedimentary\n", + "processes during continuous fault slip. The structural development of\n", + "the study area both during positive and negative inversion geometry\n", + "styles was ultimately controlled by the relative motion between the\n", + "Afro-Arabian and Central-Iranian plates. \n", + "\n", + "© 2015 Elsevier Ltd. All rights reserved. \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5it [00:03, 1.57it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "document-hash: 7594495bb7872d4aa3bfa7bacbc4f598fa8c84fddc6c553effaf4f1b101935c0\n", + "title: \n", + " Lithofacies, architectural elements and tectonic provenance of the\n", + "siliciclastic rocks of the Lower Permian Dorud Formation in the Alborz\n", + "Mountain Range, Northern Iran \n", + "\n", + "authors: [\n", + " {\n", + " \"name\": \"Mojtaba Javidan\"\n", + " },\n", + " {\n", + " \"name\": \"Hosseinali Mokhtarpour\"\n", + " },\n", + " {\n", + " \"name\": \"Mohammad Sahraeyan\"\n", + " },\n", + " {\n", + " \"name\": \"Hojatollah Kheyrandish\"\n", + " }\n", + "]\n", + "affiliations: [\n", + " {\n", + " \"name\": \"Department of Geology\"\n", + " },\n", + " {\n", + " \"name\": \"College of Basic Sciences\"\n", + " },\n", + " {\n", + " \"name\": \"Department of Geology\"\n", + " },\n", + " {\n", + " \"name\": \"Department of Geology\"\n", + " }\n", + "]\n", + "abstract: \n", + "\n", + "abstract \n", + "\n", + "The siliciclastic deposits of the Lower Permian Dorud Formation widely\n", + "crop out in the eastern part of the Alborz Mountain Range (northern\n", + "Iran). In order to interpret the sedimentary environments and tectonic\n", + "provenance of these deposits, two sections in the Kiyasar and\n", + "Talmadareh with 112 and 122 m thickness, respectively; have been\n", + "studied. The analysis of lithofacies and architectural elements, leads\n", + "to recognition of seven lithofacies (Gmm, Sr, Sl, Sh, Sp, Fl, and Fm),\n", + "and four architectural elements (FF, LA, CH, and CR). Based on these\n", + "results, the sedimentary environment of these deposits has been\n", + "identified as a sandy meandering river. The petrographical analysis\n", + "indicates that these sediments were deposited under humid weather in\n", + "the craton interior and recycled orogeny tectonic provenance. \n", + "\n", + "Ó 2015 Elsevier Ltd. All rights reserved. \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "6it [00:04, 1.64it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "document-hash: 7c5d4947280cec27fbb01892eea145933df0813be615ccd5fb5bb5503254d0f1\n", + "title: \n", + " Stratigraphy, mineralogy and depositional environment of the evaporite\n", + "unit in the As ¸ kale (Erzurum) sub-basin, Eastern Anatolia (Turkey) \n", + "\n", + "authors: [\n", + " {\n", + " \"name\": \"Emel Abdio\"\n", + " },\n", + " {\n", + " \"name\": \"Mehmet Arslan\"\n", + " },\n", + " {\n", + " \"name\": \"Cahit Helvac\"\n", + " }\n", + "]\n", + "affiliations: []\n", + "abstract: \n", + "\n", + "abstract \n", + "\n", + "The study area is situated in the As¸ kale sub-basin where the Early-\n", + "Middle Miocene aged As¸ kale Formation was deposited in a shallow\n", + "marine to lagoonal environment, and consists of interstratifications\n", + "of clastic sediments, carbonates and evaporites. The successions of\n", + "the As¸ kale Formation can be divided into four main members\n", + "interfingering with one another both vertically and laterally, and\n", + "composed of the sandstone-mudstone-limestone member, the evaporite\n", + "member, the gravelstone-sandstone-mudstone intercalations and the\n", + "limestone member. The evaporite unit comprises of secondary gypsum\n", + "lithofacies formed by hydration of precursor anhydrite, anhydrite,\n", + "gypsum-bearing limestone and claystone in the form of wedges and\n", + "lenses. Massive, nodular, nodular-banded, laminated and laminated-\n", + "banded gpysum lithofacieses in addition to chicken-wire and rare\n", + "entrolithic structures were described, indicating a sabhka or a\n", + "shallow water depositional environment. Alabastrine and porphyblastic\n", + "textures of gypsum were identified within the all lithofacieses with\n", + "abundant amount of anhydrite relics. Additionally, saponite and\n", + "illite/smectite, calcite and dolomite, celestite, epsomite were also\n", + "observed. Successions of the As¸ kale Formation were deposited in\n", + "stable subtropical climatic conditions within rapidly subsiding sub-\n", + "basin resulted in conversion of sub-basin to shallow platform and even\n", + "in lagoon environment. \n", + "\n", + "© 2015 Elsevier Ltd. All rights reserved. \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "6it [00:04, 1.32it/s]\n" + ] + } + ], + "source": [ + "api = ds.CpsApi.from_env(profile_name=PROFILE_NAME)\n", + "\n", + "model = init_nlp_model(\"language;reference;metadata\")\n", + "\n", + "proj_key = \"c4ae6545156c5f99770fdfd161102a01567d8ecd\"\n", + "#coll_name = \"GeoArabia\"\n", + "#coll_name = \"BasinResearch1\"\n", + "coll_name = \"African_ES\"\n", + "\n", + "query = \"*\"\n", + "\n", + "for doc in search_documents(api, proj_key, coll_name, query, max_docs=5, page_size=1):\n", + " \n", + " print(\"document-hash: \", doc[\"file-info\"][\"document-hash\"])\n", + " \n", + " res = model.apply_on_doc(doc)\n", + " #print(res[\"description\"].keys())\n", + " \n", + " if \"title\" in res[\"description\"]:\n", + " text = res[\"description\"][\"title\"]\n", + " text = \"\\n\".join(textwrap.wrap(text, width=70))\n", + "\n", + " print(\"title: \\n\", text, \"\\n\")\n", + "\n", + " if \"authors\" in res[\"description\"]:\n", + " print(\"authors: \", json.dumps(res[\"description\"][\"authors\"], indent=2))\n", + "\n", + " if \"affiliations\" in res[\"description\"]:\n", + " print(\"affiliations: \", json.dumps(res[\"description\"][\"affiliations\"], indent=2))\n", + " \n", + " if \"abstract\" in res[\"description\"]:\n", + "\n", + " print(\"abstract: \\n\")\n", + " for _ in res[\"description\"][\"abstract\"]:\n", + " text = \"\\n\".join(textwrap.wrap(_, width=70))\n", + " print(text, \"\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7207ed71-a8e3-4e4f-88c1-5fbbb4f82ac1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b8d13fa-d23d-46c6-9b05-a07b27e4d6c7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + 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{file = "wheel-0.42.0.tar.gz", hash = "sha256:c45be39f7882c9d34243236f2d63cbd58039e360f85d0913425fbd7ceea617a8"}, + {file = "wheel-0.43.0-py3-none-any.whl", hash = "sha256:55c570405f142630c6b9f72fe09d9b67cf1477fcf543ae5b8dcb1f5b7377da81"}, + {file = "wheel-0.43.0.tar.gz", hash = "sha256:465ef92c69fa5c5da2d1cf8ac40559a8c940886afcef87dcf14b9470862f1d85"}, ] [package.extras] @@ -5163,20 +5262,21 @@ multidict = ">=4.0" [[package]] name = "zipp" -version = "3.17.0" +version = "3.18.1" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.8" files = [ - {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"}, - {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"}, + {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"}, + {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"}, ] [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 = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] +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", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" python-versions = ">= 3.8, <3.11" -content-hash = "fd5f2d6a718f50247e5ae782dba09a51968ef8424b1e88ce67fe54b90bad6afb" +content-hash = "73f1b0b84cdeb292efcf668a0a3deeb2ac3f76ebcc3ced6791f681792f17d3b8" + diff --git a/pyproject.toml b/pyproject.toml index e9681a6..9459b1d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,7 +31,7 @@ python-dotenv = "^1.0.0" nbclient = "^0.9.0" pandas = "^1.5.1" argilla = "^1.24.0" -deepsearch-glm = "v0.16.2" +deepsearch-glm = "v0.18.4" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "^22.1.0"}