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adding docling pointers (#45)
Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>
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src/_data/past_members.json

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[
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{
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"firstName": "Yusik",
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"lastName": "Kim"
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},
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{
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"firstName": "Rafael",
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"lastName": "Teixeira de Lima"
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},
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{
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"firstName": "Shubham",
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"lastName": "Gupta"
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},
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{
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"firstName": "Lokesh",
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"lastName": "Mishra"

src/_data/team.json

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"code": "kasper-dinkla",
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"imageCode": "4530"
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},
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{
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"firstName": "Shubham",
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"lastName": "Gupta",
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"code": "shubham-gupta",
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"imageCode": "2661"
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},
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{
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"firstName": "Yusik",
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"lastName": "Kim",
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"code": "yusik-kim",
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"imageCode": "1009"
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},
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{
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"firstName": "Viktor",
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"lastName": "Kuropiatnyk",
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"code": "ahmed-nassar",
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"imageCode": "2439"
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},
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{
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"firstName": "Rafael",
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"lastName": "Teixeira de Lima",
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"code": "rafael-teixeira-de-lima",
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"imageCode": "16434"
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},
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{
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"firstName": "Panos",
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"lastName": "Vagenas",

src/_includes/layout.njk

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</div>
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<div class="actions">
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<!--
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<a href="mailto:deepsearch-core@zurich.ibm.com">
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<img src="/icons/email.svg"/>
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<span>Contact</span>
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</a>
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-->
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<a href="https://github.com/DS4SD">
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<img src="/icons/github.svg"/>
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<span>GitHub</span>

src/index.njk

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<a href="#enrich">enrich</a>, and
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<a href="#link">link</a>
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large document collections. You can use it for both
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<b>public and proprietary PDF documents</b>. Deep Search is offered commercially
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as-a-service.
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</p>
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<p class="counter">
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<span id="conversion-count"></span>
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documents have been converted by our open access system. Try it yourself for free or request access to an enterprise system.
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<div class="button-group">
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<a class="button" href="https://deepsearch-experience.res.ibm.com">
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Free access <img src="/icons/arrow-right.svg"/>
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</a>
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<a class="button inverse-ghost" href="https://airtable.com/appFvft8QiHQbGa9d/shr3aE6NX74XTWnaG">
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Enterprise access
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<img src="/icons/arrow-right.svg"/>
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</a>
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</div>
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</p>
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<p>
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<table>
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<thead>
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<tr>
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<th>
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Feature
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</th>
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<th>
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Free
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</th>
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<th>
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Enterprise
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</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>
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<a href="#convert">Convert</a>
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documents</td>
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<td><img src="/icons/checkmark.svg"/></td>
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<td><img src="/icons/checkmark.svg"/></td>
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</tr>
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<tr>
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<td>Extract components such as tables and figures</td>
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<td><img src="/icons/checkmark.svg"/></td>
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<td><img src="/icons/checkmark.svg"/></td>
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</tr>
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<tr>
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<td>Automate your process via the
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<a href="#toolkit">Toolkit</a>
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</td>
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<td><img src="/icons/checkmark.svg"/></td>
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<td><img src="/icons/checkmark.svg"/></td>
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</tr>
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<tr>
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<td>Upload and search your own documents</td>
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<td></td>
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<td><img src="/icons/checkmark.svg"/></td>
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</tr>
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<tr>
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<td>Configure the document conversion process</td>
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<td></td>
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<td><img src="/icons/checkmark.svg"/></td>
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</tr>
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<tr>
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<td>Search millions of
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<a href="#collect">pre-loaded documents</a>
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</td>
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<td></td>
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<td><img src="/icons/checkmark.svg"/></td>
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</tr>
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<tr>
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<td>Use <a href="#doc-qa">question-answering</a> on your own documents</td>
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<td></td>
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<td><img src="/icons/checkmark.svg"/></td>
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</tr>
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<td>Integrate with gen AI systems like <a href="https://www.ibm.com/products/watsonx-ai" target="_blank">IBM watsonx.ai</a></td>
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<td></td>
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<td><img src="/icons/checkmark.svg"/></td>
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</tr>
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<tr>
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<td>Search and extract
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<a href="#chemistry">chemical (sub-)structures</a>
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</td>
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<td></td>
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<td>on request</td>
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</tr>
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</tbody>
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</table>
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<b>public and proprietary PDF documents</b>.
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</p>
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</header>
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<section>
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<h2>Docling</h2>
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<p>
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Work on Deep Search has evolved into <a href="https://docling.ai"><strong>Docling</strong></a>, an open-source package that has quickly become a leader in the document processing community. The project has been donated to LF AI & Data—learn more at <a href="https://docling.ai">docling.ai</a>.
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</p>
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<hr />
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<h2>Overview</h2>
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<p>
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Deep Search

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