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Databricks Unstructured Pdf Generation

Organization
databricks
databricks-unstructured-pdf-generation

Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.

Overview

Publisherdatabricks
Repositorydatabricks-agent-skills
Skill namedatabricks-unstructured-pdf-generation
Stars
366
Forks
93
Bundled files
4
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by databricks on GitHub. Read the source before you install it.

Installation

Install the Databricks Unstructured Pdf Generation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/databricks/databricks-agent-skills/tree/main/plugins/databricks/claude/skills/databricks-unstructured-pdf-generation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git /tmp/databricks-agent-skills
mkdir -p .claude/skills
cp -r /tmp/databricks-agent-skills/plugins/databricks/claude/skills/databricks-unstructured-pdf-generation .claude/skills/databricks-unstructured-pdf-generation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Databricks Unstructured Pdf Generation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Databricks Unstructured Pdf Generation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Databricks Unstructured Pdf Generation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Unstructured-Document for Demos and Eval Datasets on Databricks

Workflow for producing synthetic PDF documents + paired test questions as a Unity Catalog-resident dataset for Demos and RAG / unstructured-document retrieval evaluation on Databricks. The PDF-generation step uses standard local HTML → PDF tooling; the Databricks-specific value is the workflow shape — UC volume layout, paired question files, and integration with downstream Databricks retrieval / ai_extract / ai_parse_document evaluation.

Workflow

  1. Write HTML files to ./raw_data/html/ (write multiple files in parallel for speed) — domain-shaped to match the documents your retrieval pipeline will see in production.
  2. Convert HTML → PDF using <SKILL_ROOT>/scripts/pdf_generator.py (parallel conversion, wraps plutoprint).
  3. Upload PDFs to a Unity Catalog volume via databricks fs cp — same volume shape your production pipeline will read from.
  4. Generate ./raw_data/pdf/pdf_eval_questions.json pairing each document with retrieval-eval questions; this becomes the gold dataset for mlflow.genai.evaluate() or comparable retrieval-quality scorers.

If you only need ad-hoc PDFs (no Databricks workflow), any HTML → PDF tool (weasyprint, wkhtmltopdf, playwright pdf, plutoprint) works directly — this skill exists for the synthetic-dataset-on-UC end-to-end shape, not as a general PDF generator.

Path convention: <SKILL_ROOT> below = the directory containing this SKILL.md. Resolve to the absolute install path (e.g. ~/.claude/skills/databricks-unstructured-pdf-generation). ./raw_data/... paths are relative to your own project cwd.

Dependencies

bash
uv pip install plutoprint

Step 1: Write HTML Files

bash
mkdir -p ./raw_data/html

Write HTML documents to ./raw_data/html/filename.html. Use subdirectories to organize (structure is preserved).

Step 2: Convert to PDF

bash
# Convert entire folder (parallel, 4 workers)
python <SKILL_ROOT>/scripts/pdf_generator.py convert --input ./raw_data/html --output ./raw_data/pdf

Skips files where PDF exists and is newer than HTML. Use --force to reconvert all.

Step 3: Upload to Volume

databricks fs requires the dbfs: scheme prefix even for UC Volume paths. -r copies the contents of the source directory into the target (the source directory name is not preserved), so name the target raw_data/pdf explicitly to keep the PDFs in their own folder on the volume. They land under raw_data/pdf/ — i.e. dbfs:/Volumes/my_catalog/my_schema/raw_data/pdf/report.pdf — so a Knowledge Assistant or ingest pipeline can point at that single folder.

bash
databricks fs cp -r --overwrite ./raw_data/pdf dbfs:/Volumes/my_catalog/my_schema/raw_data/pdf

Step 4: Generate Test Questions

Create ./raw_data/pdf/pdf_eval_questions.json with questions for Knowledge Assistant (KA) or Multi-Agent Supervisor (MAS) evaluation. It's fine for this file to be uploaded to the volume alongside the PDFs — downstream agents can use it:

json
{
  "api_errors_guide.pdf": {
    "question": "What is the solution for error ERR-4521?",
    "expected_fact": "Call /api/v2/auth/refresh with refresh_token before the 3600s TTL expires"
  },
  "installation_manual.pdf": {
    "question": "What port does the service use by default?",
    "expected_fact": "Port 8443 for HTTPS, configurable via CONFIG_PORT environment variable"
  }
}

This JSON can be used to build KA test cases and validate retrieval accuracy.

Document Content Guidelines

When generating documents for Knowledge Assistant testing or demos:

  • Multi-page documents: Each PDF should be several pages with substantial content
  • Specific error codes and solutions: Include product-specific error codes, causes, and resolution steps
  • Technical details: API endpoints, configuration parameters, version numbers, specific commands
  • Simple CSS: Keep styling minimal for fast HTML creation and reliable PDF conversion
  • Queryable facts: Include details a KA must read the document to answer (not general knowledge)

Good document types:

  • Product user manuals with troubleshooting sections
  • API error reference guides (error codes, causes, solutions)
  • Installation/configuration guides with specific steps
  • Technical specifications with version-specific details

Example content: Instead of generic "Connection failed" errors, write:

  • "Error ERR-4521: OAuth token expired. Cause: Token TTL exceeded 3600s default. Solution: Call /api/v2/auth/refresh with your refresh_token before expiration. See Section 4.2 for token lifecycle management."

CLI Reference

python <SKILL_ROOT>/scripts/pdf_generator.py convert [OPTIONS]

  --input, -i     Input HTML file or folder (required)
  --output, -o    Output folder for PDFs (required)
  --force, -f     Force reconvert (ignore timestamps)
  --workers, -w   Parallel workers (default: 4)

Folder Structure

Subfolder structure is preserved:

./raw_data/html/                    ./raw_data/pdf/
├── report.html             →       ├── report.pdf
├── quarterly/                      ├── quarterly/
│   └── q1.html             →       │   └── q1.pdf
└── legal/                          └── legal/
    └── terms.html          →           └── terms.pdf

Bundled Script

This skill ships one helper script:

FileDescription
scripts/pdf_generator.pyHTML → PDF converter (wraps plutoprint); parallel folder conversion with timestamp-skip. Referenced by Step 2 and the CLI Reference.

The script ships at <SKILL_ROOT>/scripts/pdf_generator.py. If it is absent, recreate it from the CLI Reference above (a convert subcommand taking --input/--output/--force/--workers, wrapping plutoprint for HTML → PDF).

Troubleshooting

IssueSolution
"plutoprint not installed"uv pip install plutoprint
PDF looks wrongCheck HTML/CSS syntax
"Volume does not exist"databricks volumes create CATALOG SCHEMA VOLUME_NAME MANAGED (four separate positional args, not catalog.schema.volume)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Databricks Unstructured Pdf Generation AI skill do?

Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.

Why use Databricks Unstructured Pdf Generation on TypingMind?

Because you install it once and use it with any model. Databricks Unstructured Pdf Generation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Databricks Unstructured Pdf Generation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/databricks/databricks-agent-skills/tree/main/plugins/databricks/claude/skills/databricks-unstructured-pdf-generation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Databricks Unstructured Pdf Generation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Databricks Unstructured Pdf Generation?

As many as you like. As long as a model supports skills, you can use Databricks Unstructured Pdf Generation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Databricks Unstructured Pdf Generation AI skill free?

It is published on GitHub by databricks. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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