Databricks Unstructured Pdf Generation

by databrickse77e37e8a4daNo license345 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

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.

AI-generated overview

Builds synthetic PDF documents plus paired test questions on Databricks for RAG and Knowledge Assistant retrieval evaluation.

What it does
Generates synthetic HTML documents, converts them to PDF with a bundled parallel converter, and uploads the PDFs to a Unity Catalog volume. It then produces a JSON file pairing each document with a retrieval-evaluation question and expected fact, forming a gold dataset for retrieval-quality scorers. The result is a Unity Catalog-resident dataset shaped like production document folders.
When to use it
Use when you need demo or evaluation documents for a Databricks retrieval pipeline, Knowledge Assistant, or Multi-Agent Supervisor. It fits workflows that require synthetic multi-page PDFs with queryable facts and matching test questions stored in a Unity Catalog volume. For ad-hoc PDF conversion without the Databricks dataset workflow, any HTML-to-PDF tool suffices.
Requirements
Requires the databricks CLI (>= v1.0.0) with access to a Unity Catalog volume, Python with the plutoprint package installed (uv pip install plutoprint), and network access for the upload step. Ships one executable script, scripts/pdf_generator.py, which wraps plutoprint for parallel HTML-to-PDF conversion.

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.py [blocked]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

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)

Source and attribution

Source:databricks/databricks-agent-skillsinplugins/databricks/claude/skills/databricks-unstructured-pdf-generationat commite77e37e

License: No license

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