lhc analytics

Commands for chart generation, analytics pipeline, SQL validation, and context inspection. These commands talk to the Analytics service (LHC_ANALYTICS_BASE_URL).

Chart generation

analytics generate <query>

Trigger chart generation from a natural-language query:

lhc analytics generate "<query>" [--datasources <ids>] [--model <slug>] [--wait] [--timeout <s>] [--poll-interval <s>]

Flag	Default	Description
--datasources	—	Comma-separated datasource IDs (e.g. 10,1,7)
--model	—	Custom model slug
--wait	false	Poll until the job completes, fails, or times out
--timeout	440	Max polling duration in seconds (only with --wait)
--poll-interval	8	Seconds between poll iterations (only with --wait)

Without --wait the CLI prints the trigger response (including job_id) and exits immediately — the caller is responsible for polling.

With --wait the CLI blocks until the job reaches completed or failed state and prints the final job record.

Example:

lhc analytics generate "Show sales by region" \
  --datasources 10,1 \
  --model awdw \
  --wait

---

analytics job <job-id>

Get the current status of a chart-generation job:

lhc analytics job <job-id>

Example:

lhc analytics job 4f8c3d4e-0000-0000-0000-000000000001

Job status values: pending, running, completed, failed.

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Context and SQL

analytics context-describe

Describe the analytics context for a model — available tables, hierarchies, and chart types:

lhc analytics context-describe --model <slug>

Flag	Required	Description
--model	yes	Custom model slug

---

analytics sql-validate <sql>

Validate a SQL query against the analytics service before running it:

lhc analytics sql-validate "<sql>" [--model <slug>]

Example:

lhc analytics sql-validate "SELECT count(*) FROM orders" --model awdw

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Pipeline (step-by-step)

The pipeline commands expose each step of chart generation individually. Use these when you need full control over the pipeline state or want to debug a specific step. For most use cases, analytics generate --wait is simpler.

The pipeline follows four steps: sql → dataset → chart → finalize. Each step returns a pipeline_state_token that must be passed to the next step.

analytics pipeline sql

Step 1 — Generate and validate the SQL query:

lhc analytics pipeline sql --message "<query>" [--datasources <ids>] [--model <slug>]

Flag	Required	Description
--message	yes	Natural-language user query
--datasources	no	Comma-separated datasource IDs
--model	no	Custom model slug

Returns a pipeline_state_token for step 2.

---

analytics pipeline dataset

Step 2 — Deploy the Superset view and register the dataset:

lhc analytics pipeline dataset --token <pipeline-state-token>

Returns an updated pipeline_state_token for step 3.

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analytics pipeline chart

Step 3 — Create the chart in Superset:

lhc analytics pipeline chart --token <pipeline-state-token> [--chart-type <type>]

Flag	Required	Description
--token	yes	Pipeline state token from step 2
--chart-type	no	Override chart type (bar, line, pie, area, scatter, table, big_number, …)

Returns an updated pipeline_state_token for step 4.

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analytics pipeline finalize

Step 4 — Build the dashboard and embedding configuration:

lhc analytics pipeline finalize --token <pipeline-state-token>

Returns the final chart and dashboard metadata including the chart_id and dashboard_id.

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Full pipeline example

# Step 1 — SQL
TOKEN=$(lhc analytics pipeline sql \
  --message "Revenue by month" \
  --datasources 10 \
  --model awdw \
  --output json | python3 -c "import json,sys; print(json.load(sys.stdin)['pipeline_state_token'])")

# Step 2 — Dataset
TOKEN=$(lhc analytics pipeline dataset --token "$TOKEN" \
  --output json | python3 -c "import json,sys; print(json.load(sys.stdin)['pipeline_state_token'])")

# Step 3 — Chart
TOKEN=$(lhc analytics pipeline chart --token "$TOKEN" \
  --output json | python3 -c "import json,sys; print(json.load(sys.stdin)['pipeline_state_token'])")

# Step 4 — Finalize
lhc analytics pipeline finalize --token "$TOKEN"