---
title: "Slides: Structuring the Unstructured - Cedric Clyburn, Red Hat"
category: "slides"
video_id: "-x5GEVnkuRw"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: Structuring the Unstructured - Cedric Clyburn, Red Hat

## Source Video
[Structuring the Unstructured - Cedric Clyburn, Red Hat](https://www.youtube.com/watch?v=-x5GEVnkuRw)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/-x5GEVnkuRw/slide-001.jpg]]

OCR text:

> ~~ a Al engincer
> 
> icone | World's Fair |
> 
> Ais} [o) of =) g
> Structuring the Unstructured: Advanced
> Document Parsing for Al Workflows
> AT Engineer: 2026
> (OX-to fool an mee
> Sen:or Developer Advocate ;
> Bacenae sesame sink ©| _ rs

![[assets/slides/-x5GEVnkuRw/slide-002.jpg]]

OCR text:

> We've got a lot to cover today!
> pe ©
> PDF
> a
> Wait, so 85% of the
> world’s data is...
> unstructured?!

![[assets/slides/-x5GEVnkuRw/slide-003.jpg]]

OCR text:

> We've got a lot to cover today!
> , Azure Al Document Intelligence
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> a sending your private data!
> Wait, so 85% of the a oS ee se a
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> unstructured?! let's learn about l : nit Nowe
> extraction, parsing, ek or » er psis
> chunking, and much more! ) ae “gD TE, fa
> ( tee Ee
> Se a
> So, how can we easily parse
> bes graphs, tables, etc to formats

![[assets/slides/-x5GEVnkuRw/slide-004.jpg]]

OCR text:

> Data is the key ingredient behind Al applications!
> Chaptar 2 Creating an Amazon $3 cheat ind [=r eeeesee
> steno S Shae
> — vem ane Financial
> “Tote — =. _ Documents
> — Technical Meeting Minutes
> Documentation e

![[assets/slides/-x5GEVnkuRw/slide-005.jpg]]

OCR text:

> Data is the key ingredient behind Al applications!
> a
> ea — iEeses-+ Powering:
> a - ceang anamaens3eerie9 : Sse RAG (Document Q&A)
> notebook coms tae
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> cecpnenuene tenes Hoegmte ge nah tener eT OO aT Financial
> ee ae Documents 7 ete.
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> _ cee Meeting Minutes 15s Wcenbee pitin ibe
> nore wmen we eeeteneinns ~ amie ey J. Hugging Face
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> ake geen . &3 databricks
> are MMeta -s .
> Technical ere nn .
> Documentation / H Mi
> + much more! es Go gle
> Knowledge Base oe
> nice his

![[assets/slides/-x5GEVnkuRw/slide-006.jpg]]

OCR text:

> Data processing & prep is quite important!
> & gqurovduptal ©
> © lol over 20 screnbtic papers now feature the
> nontensical term ‘vegelatire electron
> frcroscopy”
> all because an Al mebnierpreted a 1959 article,
> Merging ‘vegetalne and electron mcroscopy’
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![[assets/slides/-x5GEVnkuRw/slide-007.jpg]]

OCR text:

> Data processing & prep is quite important!
> WETU UOTUUMARD IU) BL CALIECS FD BUTE UN RCSCE UT LY pe. 16 Bas CUUCIUUOU ORS BL EOE
> integrated at pH 7.0. Peptide was released part of the sporangia) wall was dissolved away
> which eetabliahed that the coats contained sub- to allow release of the spore. It appears likely
> strate for the lytic enzyme present in spores. that the exosporium of B. cerews does not hare
> Peptide was also released [rom spore costs of B. a composition samilar to that of the vegetative
> megatervum by the action of the enzyme from B. cell wall, from the results obtained by Dr. J. R.
> ‘The lytio entyme did not how fe tee . .
> chanel bog The spore develops in the vegetative ced, whach thus becomes a sporangum It a by ho mans certain what happens to the
> The spore develops in the vegetative cell, snaetatne cell wall when the spore is released In Clostncum species tf appears that at least part of thes structure «6 retained as an
> which thus becomes a sporangium. It is by no Gutter Memb ane ound the epore It the opine of some workers that the wal of the sporuaating ced forms the expeporium wher
> a ae Pai . G0SIS a5 an Outer Coat around spores of several Bac dus species Spores of several vaneves of B cereus had exosporia whereas these
> cel) wall when the spore is released. In Clee. 9 S7UCturet appeared to be advent from spores of B megotenum and B suptin It seers, however, that in Bacwus speces at least
> fridium species it appears that at least pert of (it seater pert of the vegetative cet watts dssolved away before the developed spore 6 released If thes 6 true, then soluble
> this struct is retained as an outer membrane Components contamng the characterst< cometuents shoukd appear in the mechan during wore release Cutture filtrates from &
> 4 the It is the . of some Cereus organs at various stages of growth and sporulabon were hydrolyzed and the hycrolyzates anatyred for amino sugars and
> » % chamingpuneld acid (28) Results showed that a large increase n the concentration of these substances in the cutture filtrate
> workers that the wall of the sporulating call ood dung ipore release (lable 2), they were found to be present in a nonckatyzable peptide of the charactecnc type It was
> forms the exosporium which existe a0 an outer conciaded that at least part of the sporangaal wal was dasolved away fo ahow release of the spore It appea’s baely that the
> examporum of B cereus Goes not have a compoution smiw to that of the vegetative cel wast, from the cesults oblaned by Dr JR
> Norns of Leeds Uneverstty (personal communcation) He treated spores with a mgity ace preparation of lyt enzyme fr
> Cereus Spores and examined the effect by means of glection mcroscopy ~~ @

![[assets/slides/-x5GEVnkuRw/slide-008.jpg]]

OCR text:

> So, let's try a simple PDF parser...
> S J : : 5 LE 2 : 8 : S ra Y Very fast and cheap
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> | L alco re ~ Unfit for most use
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![[assets/slides/-x5GEVnkuRw/slide-009.jpg]]

OCR text:

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![[assets/slides/-x5GEVnkuRw/slide-010.jpg]]

OCR text:

> Maybe there's a middle ground... Welcome to Docling!
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![[assets/slides/-x5GEVnkuRw/slide-011.jpg]]

OCR text:

> Docling: Get your documents ready for gen Al
> An open source processor using advanced vision models * OCR
> > Parsing of multipie document formats incl. PDF, iC ™
> DOCX, XLSX, HTML, images, and more Oi ced thd
> mee Gen
> » Advanced PDF understanding with page layout, j= a - | | r
> | '
> reading order, table structure, code, formulas, - ast
> image classification, etc
> » Plug-and-play ecosystem integrations
> » Local execution for sensitive data and
> air-gapped environments

![[assets/slides/-x5GEVnkuRw/slide-012.jpg]]

OCR text:

> Docling: Scale, cost, and performance
> 5
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> ie iiss ecg en Amram Mee & 475,019,140 PDFs parsed end-to-end
> — 2 ree (Ss Aas CRRA OR PTERRS, Q 1,733 languages represented
> [O mee tee weet Beene enews ror nes iJ ~3 trillion tokens (~2,918B) extracted
> enclmis =—— SE esac M1 3.65 T8 of high-quatity, deduplicated text
> . ~ ~ : ie DR TE Ve “* Data spanning 2013-2025 across 105
> Aesdl fens Beansece wiles CommonCrawl snapshots
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![[assets/slides/-x5GEVnkuRw/slide-013.jpg]]

OCR text:

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> i Fe gene ce pp emmamium manner seman eitoaw ee FOMES Bart Bie) MSA (yetoe) Mangage Leen Condut ined: and Comers OrEnge: ING? 1S the Agency RARBS canegory
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> seAEA ENRON ARON 0315 Re NomAgency RMBS category Later necresees 10 0% ofl Agency RUBS decreases 19 415 USK reas 10 14
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> was Wem 271 AIS MEA her TON 2 TEN Mongage Lean Candua form 119. 1 “@1 and Commenced hee TN me The cnet
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![[assets/slides/-x5GEVnkuRw/slide-014.jpg]]

OCR text:

> Docling:MorethansimpleDocumentConversion
> hello
> 1010A**01+
> 'billnumber':'01234',
> 'total invoice price':550,
> 'currency of total invoice price':'usD',
> 'name ofinvoice addressee':'Jonathan Patterson'
> 'name of invoice sender':'Eventure Event Planner
> invoice_dict=
> :Jaqunu13q
> "string"
> "totalinvoice price":"float",
> "currency of total tnvoice price":“string”
> "name of invoice addressee':"string”,

![[assets/slides/-x5GEVnkuRw/slide-015.jpg]]

OCR text:

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> <> Code ©) ttsues SL Pultrequests Agents ©) Acbom = #3 Projects «Security and quality 9 oY Insights
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> @] dthorgrave Mere cu segues! HID Hoe -tre-grante Comers ty Ueberdabet MB te 2 tres, D106 Comments
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![[assets/slides/-x5GEVnkuRw/slide-016.jpg]]

OCR text:

> Q
> B
> Conversion.ipynbMX
> Token_Cost_Comparison.ipynb M
> Chunkless_RAG.ipynb M
> Chunking.ipynb
> DOCLING-WORKSHOP
> notebooks
> >fixtures
> Add Code Add Markdown|Run All Restart Clear All Outputs|Jupyter Variables Outline
> venv (3.13.7) (Python 3.13.7)
> ndno<
> ImportEssentialComponents
> gitignore
> Chunking.ipynb
> Chunkless_RAG.ipynb
> from pathlib import Path
> Conversion_Colab.ipynb
> Conversion.ipynb
> M
> Core Docling imports
> MCP_Agents.ipynb
> M
> from docling.document_converter import DocumentConverter
> Ollama RAG.ipynb
> from docling.datamodel.base_models import InputFormat
> from docling.datamodel.pipeline_options import PdfPipelineOptions
> RAGipynb
> from docling.document_converterimportPdfFormatoption
> Serving.ipynb
> Token_Cost_Compari.M
> #For advanced features
> scripts
> >src
> #For data processing and visualization
> gitignore
> import matplotlib.pyplot as plt
> !markdownlint-cli2.yaml
> !.poutine.yml
> Create output directory
> !pre-commit-config.yaml
> output_dir=Path("output")
> output_dir.mkdir(exist_ok=True)
> F.spellcheck-en-custom.txt
> √0.0s
> Python
> !.spellcheck.yaml
> Fconstraints.txt
> LICENSE
> BasicDocumentConversion
> !mkdocs.yml
> pyproject.toml
> MinimalExample
> ①README.md
> The simplestway toconvertadocument:
> OUTLINE

![[assets/slides/-x5GEVnkuRw/slide-017.jpg]]

OCR text:

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![[assets/slides/-x5GEVnkuRw/slide-018.jpg]]

OCR text:

> Q。
> Conversion.ipynbMX
> Token_Cost_Comparison.ipynb M
> Chunkless_RAG.ipynb M
> Chunking.ipynb
> DOCLING-WORKSHOP
> Al-Ready Data with DoclingM Basic Document Conversion>M Document Structure Exploration>#Document metadata-important for tracking
> notebooks
> >fixtures
> AddCodeAddMarkdownRunAll Restart ClearAllOutputs|JupyterVariablesOutline
> venv (3.13.7) (Python 3.13.7)
> ndno<
> SectionHeaderItem:Abstract
> gitignore
> TextItem:We introduce Docling,an easy-to-use,self-contained,MITlicensed,open-source toolkit for document conversion,tr
> Chunking.ipynb
> TextItem:Repository-https://github.com/DS4SD/docling
> Chunkless_RAG.ipynbM
> SectionHeaderItem:1Introduction
> TextItem:Converting documentsback into a unified machineprocessable format has been a major challenge for decades due to th
> Conversion_Colab.ipynb
> TextItem:*These authors contributed equally.
> Converson.pynb
> M
> MCP_Agents.ipynb
> M
> Ollama RAG.ipynb
> ExportFormatsandOptions
> RAG.ipynb
> Serving.ipynb
> Doclingsupportsmultipleexportformatswithvariousoptions:
> Token_Cost_Compar.M
> Add Code
> Add Markdown
> >scripts
> >src
> #Export to different formats (various options available,but called with default ones)
> gitignore
> markdown_text=doc.export_to_markdown()
> !.markdownlint-cli2.yaml
> html_text=doc.export_to_html()
> !.poutine.yml
> json_dict=doc.export_to_dict（)
> doc_tags=doc.export_to_doctags()
> !.pre-commit-config.yaml
> F.spellcheck-en-custom.txt
> #Save different formats(various options available,some showm)
> !.spellcheck.yaml
> doc.save_as_markdown(
> constraints.txt
> output_dir/"document.nd",
> LICENSE
> image_mode=ImageRefMode.PLACEHOLDER,
> image_placeholder="<!--my image placeholder
> !mkdocs.yml
> pyproject.toml
> ①README.md
> OUTLINE
> JSON

![[assets/slides/-x5GEVnkuRw/slide-019.jpg]]

OCR text:

> Q
> Conversion.ipynbMx
> Token_Cost_Comparison.ipynbM
> Chunkless_RAG.ipynb M
> Chunking.ipynb
> DOCLING-WORKSHOP
> ntsinto Al-Ready Data with Docling>M Working with Tables>M Basic Table Export >print(f\nDocument contains{len(table_doc.tables)} tables)
> notebooks
> >fixtures
> Add CodeAdd MarkdownRun All Restart Clear All Outputs|Jupyter Variables Outline
> .venv (3.13.7)(Python 3.13.7)
> >output
> #Save as HTHL
> gitignore
> withopen(output_dir/f"table_(table_idx).html",
> "）asfp:
> Chunking.ipynb
> fp.write(table.export_to_html(doc=table_doc))
> BChunkless_RAG.ipynb
> [es]
> √0.0s
> Python
> Conversion_Colab.ipynb
> Usage ofTableItem.export_to_dataframe()withoutdoc'argument isdeprecated.
> Conversion.ipynb
> M
> MCP_Agents.ipynb
> M
> Document contains 8 tables
> Ollama RAG.ipynb
> RAG.ipynb
> ##Table
> Shape:(4,4)
> Serving.ipynb
> Token_Cost_Compari..M
> Cauldron
> LLaVa-OneVision
> Cambrian-7m
> >scripts
> General
> 276.5K
> 881.3K
> 1.8M
> >src
> Language/Captioning
> 202.1K
> N/A
> NA
> gitignore
> Math/Science/Reasoning
> 178.4K
> 318.0K
> 354.5K
> !.markdownlint-cli2.yaml
> Image Comparison
> 188.9K
> N/A
> NA
> !poutine.yml
> !.pre-commit-config-yaml
> F.spellcheck-en-custom.txt
> ##Table1
> !.spellcheck.yaml
> Shape:(4,4)
> Fconstraints.txt
> LICENSE
> Cauldron
> LLaVa-OneVision
> Cambrian-7m
> !mkdocs.yml
> General
> 812.7K
> 2.0M
> 7.9M
> pyproject.toml
> Language/Captioning
> 203.3K
> 1.2M
> 1.8M
> ①README.md
> Math/Science/Reasoning
> 765.1K
> 464.8K
> 802.0K
> Image Comparison
> 237.9K
> N/A
> N/A
> OUTLINE

![[assets/slides/-x5GEVnkuRw/slide-020.jpg]]

OCR text:

> Q
> B
> Conversion.ipynbMx
> Token_Cost_Comparison.ipynb M
> Chunkless_RAG.ipynb M
> Chunking.ipynb
> DOCLING-WORKSHOP
> notebooks
> >fixtures
> AddCodeAdd MarkdownRun All Restart ClearAll Outputs|JupyterVariablesOutline
> venv (3.13.7) (Python 3.13.7)
> >output
> gitignore
> Chunking.ipynb
> InspectingPicture Content
> Chunkless_RAG.ipynbM
> Conversion_Colab.ipynb
> Conversion.ipynb
> M
> D日
> MCP_Agents.ipynb
> M
> definspect_pictures_with_inages（doc:DoclingDocument,image_size=(6,4)):
> Ollama RAG.ipynb
> pisplay pictures inline with their text content.
> RAG.ipynb
> foridx,pictureinenumerateiPictureItem](doc.pictures):
> Serving.ipynb
> print(f"\n('=′60)")
> Token_Cost_Compari..M
> print（f"Picture(idx)")
> print（f"('='*60）")
> scripts
> ）src
> #Display the image
> gitignore
> try:
> !.markdownlint-cli2.yaml
> img=picture.get_image(doc)
> ifimg:
> !poutine.yml
> plt.figure(figsize=image_size)
> !.pre-commit-config.yaml
> plt.inshow（img)
> F.spellcheck-en-custom.txt
> plt.axis('off')
> !.spellcheck.yaml
> plt.title(f"Picture(idx}")
> Fconstraints.txt
> plt.show()
> LICENSE
> print（f"Could not display image:(e)")
> !mkdocs.yml
> pyproject.toml
> #Display metadata
> README.md
> caption=picture.caption_text(doc)
> ifcaption:
> print（f"\nCaption:(caption}")
> OUTLINE

![[assets/slides/-x5GEVnkuRw/slide-021.jpg]]

OCR text:

> Visualizing Document Layout with Bounding Boxes
> eee eee Sa ae ra re eNO et TED bet ee ee ee ea ae
> (howe oh fe Mog Bog gee bY oe ue 4° cc ee eee
> DwaiHpy An Efficient Open-Source Toolkit for Al-driven Document Conversion
> Middiaos Livathinos ©, Christoph Auer ©, Mauksym Lysak, Ahmed Nassar, Michele Dalfi,
> Panagiotis Vagenas, Cesar Berrospi, Matteo Omenetti, Kasper Dinkla, Yusik Kim,

![[assets/slides/-x5GEVnkuRw/slide-022.jpg]]

OCR text:

> Q。
> B
> Conversion.ipynbMx
> Token_Cost_Comparison.ipynbM
> Chunkless_RAG.ipynbM
> Chunking.ipynb
> DOCLING-WORKSHOP
> 3nced Features: Enrichment>MYour turn:Prompt the vision model >from docling.datamodel.pipeline_options import PictureDescriptionApiOptions
> notebooks
> fixtures
> AddCodeAdd MarkdownRun All RestartClearAll Outputs|JupyterVariablesOutline
> venv (3.13.7) (Python 3.13.7)
> >output
> WecanalsorunitusinganOpenAl-compatibleAPi likeOllama.
> gitignore
> Chunking.ipynb
> D
> Chunkless_RAG.ipynb
> from docling.datamodel.pipeline_optionsimportPictureDescriptionApioptions
> Conversion_Colab.ipynb
> Conversion.ipynb
> ifRUN_LOCAL_OLLAMA:
> M
> MCP_Agents.ipynb
> #Configure enrichment pipeline
> M
> enrichment_options=PdfPipelineOptions(
> OllamaRAG.ipynb
> do_picture_description=True,
> RAG.ipynb
> enable_remote_services=True,
> Serving.ipynb
> picture_description_options=PictureDescriptionApioptions(
> Token_Cost_Compari..M
> url="http://localhost:11434/v1/chatcompletions",
> params=(
> >scripts
> "model":"granite3.2-vision:2b",
> >src
> "max_completion_tokens":200,
> gitignore
> !.markdownlint-cli2.yaml
> prompt="Give a detailed description of what is depicted in the image"
> timeout=60,
> !.poutine.yml
> !.pre-commit-config.yaml
> generate_picture_images=True,
> F.spellcheck-en-custom.txt
> images_scale=1.0,
> !.spellcheck.yaml
> Fconstraints.txt
> converter_enriched =DocumentConverter(
> LICENSE
> format_options={
> !mkdocs.yml
> InputFornat.PDF:PdfFormatoption(pipeline_options=enrichment_options)
> pyproject.toml
> ①README.md
> enr_result=converter_enriched.convert(docling_paper)
> OUTLINE
> enr_doc=enr_result.document

![[assets/slides/-x5GEVnkuRw/slide-023.jpg]]

OCR text:

> BConversion.ipynb M
> Token_Cost_Comparison.ipynbM
> Chunkless_RAG.ipynb M
> Chunking.ipynb
> DOCLING-WO.
> kless RAG actually earns its keep> query_2 = What was Red Hat's revenue growth in 2025 and how did it contribute to IBM's overall software segment?
> notebooks
> >fixtures
> Add CodeAddMarkdown|Run AllRestart ClearAll Outputs GoToJupyterVariablesOutline
> .venv (3.13.7)(Python 3.13.7)
> >output
> HowchunklessRAGworks
> gitignore
> Chunking.ipynb
> Chunklessretrieval isa four-step loop:
> Chunkless_RAG.ipynbM
> Conversion_Colab.ipynb
> Conversion.ipynb
> M
> Document outline
> MCP_Agents.ipynb
> (per-section summaries)
> M
> Ollama RAG.ipynb
> RAG.ipynb
> Serving.ipynb
> Token_Cost_Compari.M
> 1.SELECT
> -LLM picks the most relevant unvisited
> >scripts
> section by reading the outline + query.
> >src
> markdownlint-cli2.yaml
> !.poutine.yml
> 2.FETCH
> -Pullthe *full text*of thatsection's
> !.pre-commit-config.yaml
> subtree from the DoclingDocument.
> F.spellcheck-en-custom.txt
> !.spellcheck.yaml
> Fconstraints.txt
> LICENSE
> !mkdocs.yml
> 3.ATTEMPT-LLM triesto answer from the section text.
> pyproject.toml
> Returns (can_answer: bool, response: str}.
> ①README.md
> OUTLINE

![[assets/slides/-x5GEVnkuRw/slide-024.jpg]]

OCR text:

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> a ’
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## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.