Create Agent Bricks: Knowledge Assistants (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS).
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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.
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See how to installReview the instructions the agent will receive before you install.
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.
./raw_data/html/ (write multiple files in parallel for speed) — domain-shaped to match the documents your retrieval pipeline will see in production.<SKILL_ROOT>/scripts/pdf_generator.py (parallel conversion, wraps plutoprint).databricks fs cp — same volume shape your production pipeline will read from../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.
uv pip install plutoprint
mkdir -p ./raw_data/html
Write HTML documents to ./raw_data/html/filename.html. Use subdirectories to organize (structure is preserved).
# 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.
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
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.
When generating documents for Knowledge Assistant testing or demos:
Good document types:
Example content: Instead of generic "Connection failed" errors, write:
/api/v2/auth/refresh with your refresh_token before expiration. See Section 4.2 for token lifecycle management."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)
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
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).
| 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) |
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Create Agent Bricks: Knowledge Assistants (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS).
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Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).
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Databricks AI Runtime, the `databricks air` CLI commands for submitting and managing GPU training workloads on Databricks serverless compute. Use for: writing and submitting `databricks air` workload YAML, passing hyperparameters and secrets, checking run status, listing/cancelling runs, streaming a run's logs and watching its progress, custom Docker image setup, and environment configuration.
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Create Databricks AI/BI dashboards. Must use when creating, updating, or deploying Lakeview dashboards as Databricks Dashboard have a unique json structure. CRITICAL: You MUST test ALL SQL queries via CLI BEFORE deploying. Follow guidelines strictly.
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Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.
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Build apps on Databricks Apps platform. Use when asked to create data apps, analytics tools, or custom interactive visualizations. A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.
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