Lakehouse AI commands

These examples assume an authenticated active saved profile with the intended organization and project. Confirm it with tp context show before running commands. Add root --json before the command when machine parsing needs JSON output; use root --profile <name> only to override the active profile.

Discover functions

tp lakehouse ai-functions list --surface python_notebook
tp lakehouse ai-functions describe ai_summarize
tp lakehouse ai-functions describe ai_query --no-probe-workspace

The list is the managed Registry. describe normally overlays target-workspace availability from DESCRIBE FUNCTION EXTENDED without running inference.

Inspect source data

tp lakehouse catalog show
tp lakehouse tables get silver.reviews
tp lakehouse tables preview silver.reviews --limit 10

tp datalake list --path project/raw/reviews
tp datalake preview --path project/raw/reviews/reviews.jsonl
tp datalake resolve-uri --path project/raw/reviews

project/raw/... is a ThinkPlatform logical path, not a Unity Catalog Volume path. Use the URI returned by datalake resolve-uri as the notebook or Job input_path; do not construct /Volumes/<catalog>/project/... or an ABFSS URI yourself.

Run and monitor a SQL statement

tp lakehouse statements execute \
  --statement "SELECT ai_summarize(:text) AS summary" \
  --parameter "text=A short representative review" \
  --parameter-type "text=STRING" \
  --wait-timeout 0s \
  --on-wait-timeout CONTINUE

tp lakehouse statements get <statement-id>
tp lakehouse statements result-chunk <statement-id> 1
tp lakehouse statements cancel <statement-id>

For advanced options, provide a JSON request in the format accepted by Databricks:

tp lakehouse statements execute --body @request.json --dry-run

Import and validate notebooks

tp lakehouse notebooks upsert notebooks/ai/review_enrichment \
  --file review_enrichment.sql \
  --language SQL

tp lakehouse notebooks run notebooks/ai/review_enrichment \
  --compute serverless \
  --environment-version <supported-version> \
  --dependency 'pydantic==2.12.5' \
  --param sample_limit=10 \
  --wait

Omit environment options to inherit the notebook/workspace default. Never combine --serverless and --cluster-id.

Save production work as a job

tp lakehouse jobs upsert-notebook \
  --name review-ai-enrichment \
  --notebook-path notebooks/ai/review_enrichment \
  --compute serverless \
  --environment-version <supported-version> \
  --param sample_limit=0 \
  --dry-run

tp lakehouse jobs run <job-id> --wait
tp lakehouse runs output <run-id> --mode compact

One-time Serverless runs are for validation. Use a saved Job for repeatable or production processing. --wait resolves the latest retry attempt automatically when a run has repeated entries for one task key. For a genuine multi-task Job, inspect the parent with tp lakehouse runs get <run-id>, then request each task run’s output explicitly. SQL notebook compact output may contain only run metadata. Use an independent tp lakehouse statements execute query against the materialized project-catalog table for row-level acceptance checks. A raw-only read_files Statement can be rejected by the project-catalog guard; perform raw inspection in the bounded notebook and keep the independent Statement focused on the resulting project table. Use no more than 10 representative rows for the default validation. Materialize the AI expression once, then perform all acceptance queries against that output. Keep preview caching enabled; add --fresh only for the first read after the materialization commit.

CLI and MCP execution routes

Intent	Local Agent / Codex	ThinkPlatform Agent
Discover function	`tp lakehouse ai-functions list/describe`	AI Function registry MCP system tools
Preview	`tp lakehouse sql preview`	SQL preview system tool, `fresh=false`
Validation run	`tp lakehouse notebooks run --compute serverless`	Notebook run system tool, `compute_mode=serverless`
Saved run	`tp lakehouse jobs upsert-* --compute serverless`	Job upsert system tool, `compute_mode=serverless`
Cost evidence	`tp lakehouse costs overview`	`databricks_get_cost_overview`

If Serverless is unsupported, use job-cluster/job_cluster with the organization policy. Use existing compute only with explicit approval and an explicit cluster ID.

Get started

CLI

Python SDK

Agent skills

Discover functions

Inspect source data

Run and monitor a SQL statement

Import and validate notebooks

Save production work as a job

CLI and MCP execution routes

​Discover functions

​Inspect source data

​Run and monitor a SQL statement

​Import and validate notebooks

​Save production work as a job

​CLI and MCP execution routes

Discover functions

Inspect source data

Run and monitor a SQL statement

Import and validate notebooks

Save production work as a job

CLI and MCP execution routes