databricks-unstructured-pdf-generation

name	databricks-unstructured-pdf-generation
description	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.
compatibility	Requires databricks CLI (>= v1.0.0)
metadata	{"version":"0.1.0"}
parent	databricks-core

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

uv pip install plutoprint

Step 1: Write HTML Files

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

# 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.

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:

{
  "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:

File	Description
scripts/pdf_generator.py	HTML → 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

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