omni-query

Omni Query

Run queries against Omni's semantic layer via the Omni CLI. Omni translates field selections into optimized SQL — you specify what you want (dimensions, measures, filters), not how to get it.

Tip: Use omni-model-explorer first if you don't know the available topics and fields.

Prerequisites

# Verify the Omni CLI is installed — if not, ask the user to install it
# See: https://github.com/exploreomni/cli#readme
command -v omni >/dev/null || echo "ERROR: Omni CLI is not installed."

# Show available profiles and select the appropriate one
omni config show
# If multiple profiles exist, ask the user which to use, then switch:
omni config use <profile-name>

You also need a model ID and knowledge of available topics and fields.

Discovering Commands

omni query --help              # List query operations
omni query run --help          # Show flags for running a query
omni ai --help                 # AI-powered query generation

Tip: Use -o json to force structured output for programmatic parsing, or -o human for readable tables. The default is auto (human in a TTY, JSON when piped).

Known Issues & Safe Defaults

• When handed SQL, default to reproducing its intent on a topic — read what the SQL does (grain, measures, filters, joins), then check whether a topic can express it (omni-model-explorer; omni ai pick-topic / omni ai generate-query --run-query=false). Reach for raw userEditedSQL only when no topic fits, or when the user explicitly asks to run their SQL as-is. See Running Raw SQL. Don't passthrough SQL by reflex (you're not a text-to-SQL generator), and don't force-fit a topic that doesn't match.
• When the user asks for a calculated column, table calculation, running total, moving average, percent change, row total, tier label, VLOOKUP, SUMIF, or date difference, satisfy it with a calculations[] table calc selected in query.fields. Do not substitute an existing model field, userEditedSQL, client-side math, or a narrative-only explanation unless the user explicitly asks for that alternative.
• Keep table-calc answers auditable. Don't call the task complete until the final answer names the calc column and shows that same calc_name in both query.fields and calculations[] (true even for simple operators like OMNI_RUNNING_TOTAL). Include a compact query-JSON excerpt — query.fields, the real calculations[] object with operators/operands intact, relevant pivots[]/limit, and the validation result — not a paraphrase like { "OMNI_OFFSET_MULTI over": "field" } or "computed via OMNI_RUNNING_TOTAL." Long CSV can bury the query shape; show a few rows plus the reusable shape, or say you're omitting the full JSON for brevity.
• If a calc query succeeds but the calc column is blank, treat it as a failed calc until proven otherwise. Re-check operand order, for_calc, date truncation, outside_pivot, and whether the calc_name appears in query.fields.
• Don't swallow calc errors while authoring or validating. Keep swallow_errors: false (the default) so a bad calc fails loudly with the real message. With swallow_errors: true, the column silently shows #ERROR! and the query still returns COMPLETE — easy to misread as data, a blank calc, or an engine bug. If you see #ERROR!, re-run with swallow_errors: false to surface the cause (often a referenced field missing from query.fields). When re-running a calc query you pulled from a document, dashboard tile, or omni ai job, run it verbatim — dropping a field the calc references manufactures an error that isn't the calc's fault. Reserve swallow_errors: true for a finalized tile that needs per-cell resilience, and validate it with false first. See references/table-calculations.md §5.11 & §6.5.
• Prefer the documented Omni calc operators over lower-level raw SQL/window ASTs when a template exists. For example, use Omni.OMNI_RUNNING_TOTAL, Omni.OMNI_PERCENT_CHANGE_FROM_PREVIOUS, and Omni.OMNI_FX_AVERAGE(Omni.OMNI_OFFSET_MULTI(...)) for moving averages instead of hand-authored window_call/LAG when the prompt asks for a table calculation.

Build queries on a topic

Prefer building every query on a topic, not a bare base view. Topics carry the governed joins, labels, and access — and a query not built on a topic is not accessible to restricted queriers/viewers (it works for you as a modeler/admin but silently fails for restricted roles). Set the query table to the topic's base view and pass join_paths_from_topic_name: <topic>.

How the join map resolves joined-view fields. table stays the topic's base view; join_paths_from_topic_name lets the topic's join map reach joined-view fields from it — e.g. to select users.state on an order_items-based topic, table stays order_items and the join comes from the topic; you do not set table: users. Omit join_paths_from_topic_name (or point table at a non-base view) and joined-view fields may fail to resolve or join wrong. Confirm the base view and every reachable join with omni models get-topic <modelId> <topic> — its base_view_name and join_via_map show the base view and the join path to each reachable view. (This is the canonical topic-query shape; omni-content-builder tiles and omni-model-builder validation queries use it too.)

Decide where the query should come from:

1. An existing topic answers it (its base view + a join-reachable view) → query that topic.
2. The field is on a join-reachable view but the topic doesn't expose it / lacks the join → propose extending the topic (add the relationship/join), then build it via omni-model-builder.
3. Fundamentally different subject, constraints, or audience → propose a new topic (see the "new topic vs extend" criteria in omni-model-builder). Prompt the requestor first, and build it on a branch.

Fallback — non-topic query pathways. Two pathways run outside any topic: a bare base view (table: + the global relationships file for joins) and raw SQL (userEditedSQL, see "Running Raw SQL"). Both share the same caveat: topic-scoped controls — access filters (row-level) and always_where — are not applied, and in a dashboard the tile is invisible to Viewer / Restricted Querier roles by default (handling a restricted audience is a content-permission concern — see omni-content-builder). The two pathways differ on object-level access grants: a bare-view query still enforces them, but raw SQL bypasses them too (it's the most permissive pathway). Prefer a topic when one fits; reach for a non-topic pathway only when nothing else expresses the query.

When the conclusion is "build or modify a topic," hand off to omni-model-builder to do it right.

Running a Query

Basic Query

omni query run --body '{
  "query": {
    "modelId": "your-model-id",
    "table": "order_items",
    "fields": [
      "order_items.created_at[month]",
      "order_items.total_revenue"
    ],
    "limit": 100,
    "join_paths_from_topic_name": "order_items"
  }
}'

Query Parameters

Parameter	Required	Description
modelId	Yes	UUID of the Omni model
table	Conditional	Base view (the FROM). Required for a semantic query unless join_paths_from_topic_name is set (base view comes from the topic) or userEditedSQL is used (table ignored).
fields	Yes	Array of view.field_name references
join_paths_from_topic_name	Recommended	Topic for join resolution
limit	No	Row limit (default 1000, max 50000, null for unlimited)
sorts	No	Array of sort objects
filters	No	Filter object
pivots	No	Array of field names to pivot on

Field Naming

Fields use view_name.field_name. Date fields support timeframe brackets:

users.created_at[date]      — Daily
users.created_at[week]      — Weekly
users.created_at[month]     — Monthly
users.created_at[quarter]   — Quarterly
users.created_at[year]      — Yearly

Sorts

"sorts": [
  { "column_name": "order_items.total_revenue", "sort_descending": true }
]

Filters

"filters": {
  "order_items.created_at": "last 90 days",
  "order_items.status": "complete",
  "users.state": "California,New York"
}

Expressions: "last 90 days", "this quarter", "2024-01-01 to 2024-12-31", "not California", "null", "not null", ">100", "between 10 and 100", "contains sales", "starts with A". See references/filter-expressions.md for the complete expression syntax reference.

If a date filter string fails with an API error like Cannot use 'in' operator to search for 'query_id' in last 12 months, keep the query semantic and retry with the typed date-filter object shape instead of dropping the filter:

"filters": {
  "order_items.created_at": {
    "type": "date",
    "kind": "TIME_FOR_INTERVAL_DURATION",
    "left_side": "12 months ago",
    "right_side": "12 months",
    "ui_type": "PAST"
  }
}

Pivots

{
  "query": {
    "fields": ["order_items.created_at[month]", "order_items.status", "order_items.count"],
    "pivots": ["order_items.status"],
    "join_paths_from_topic_name": "order_items"
  }
}

Pivoted queries reject limit: null — pass an explicit numeric limit (e.g., 5000). Unlimited is only allowed when pivots[] is empty.

Transpose measures into rows (transposed_measures)

Folds several measures from one wide row into long form — one row per measure — so the measures become a category you can chart against (e.g. a funnel, or a "measures on the axis" bar). transposed_measures is an array of the measure field names to fold (the same names you put in fields), not a boolean — passing true is silently rejected (z.array(z.string())), yielding an empty result.

{
  "query": {
    "table": "order_items",
    "fields": ["order_items.units_sold", "order_items.shipped_items", "order_items.delivered_items"],
    "transposed_measures": ["order_items.units_sold", "order_items.shipped_items", "order_items.delivered_items"],
    "join_paths_from_topic_name": "order_items"
  }
}

The result gains three synthetic columns at the first transposed measure's position:

• measure_name — the measure's field name; renders as the measure's friendly label in a viz (e.g. "Units Sold").
• measure_order — 0, 1, 2, … in the order listed (the stage order).
• measure_value — that measure's value for the row.

This is the supported way to build a funnel from multiple measures (Omni's funnel needs a stage dimension + one measure, not N measures): chart measure_name as the stage and measure_value as the value. See omni-content-builder → Config Object: Funnel.

Table Calculations

Post-query computed columns (running totals, % of total, ratios, conditionals). Authored as AST objects in calculations[]. The query API requires the parsed AST — it does not accept the workbook-frontend {name, formula} shape.

Minimum-viable calc:

{
  "query": {
    "fields": ["orders.month", "orders.total_revenue", "calc_pct"],
    "calculations": [{
      "calc_name": "calc_pct",
      "label": "% of Total",
      "format": "0.0%",
      "sql_expression": {
        "type": "call",
        "operator": "Omni.OMNI_PERCENT_OF_TOTAL",
        "operands": [
          { "type": "field", "field_name": "orders.total_revenue", "for_calc": true }
        ]
      }
    }]
  }
}

The #1 gotcha: calc_name must also appear in query.fields (and the outer queryPresentation.fields for dashboard tiles). A calc defined in calculations[] but absent from fields is computed but never rendered.

The five quick-template operators (each takes one field operand with for_calc: true): Omni.OMNI_PERCENT_OF_TOTAL, Omni.OMNI_PERCENT_OF_PREVIOUS, Omni.OMNI_PERCENT_CHANGE_FROM_PREVIOUS, Omni.OMNI_RUNNING_TOTAL, Omni.OMNI_RANK.

Use omni query run with a hand-authored or copied AST when you already know the calc shape. To generate anything non-trivial — table calculations, period-over-period, multi-step analysis — prefer the agentic path (omni ai job-submit): it authors calcs that generate-query silently drops (e.g. month-over-month % change). To get a reusable AST out of an agentic job, lift the structured query / calculations from the job's actions[].generate_query result (not the resultSummary, and not a userEditedSQL/SQL fallback), then re-run your assembled query with swallow_errors:false and diff the values against the job's csvResult. The job already executed the calc, so the re-run isn't re-proving the math — it's checking your reshape (a dropped/renamed field is the kind of translation failure that "it ran and returned rows" would miss; see table-calculations.md §6). Reserve generate-query for simple deterministic single queries, and for shape-only drafting where query execution isn't permitted.

Query tasks are read-only unless the user explicitly asks to change the model. If a field appears missing, inspect topics/dashboard queries and use the right model/topic/branch or report the missing-field blocker — don't create branches, add measures, or edit YAML just to make a query work. (And don't satisfy a calc request with client-side math or an existing model field like users.tier_label — build and validate the table calc; see Known Issues.)

Quick recipes for common calc requests:

• Percent of total: add a calc using Omni.OMNI_PERCENT_OF_TOTAL with one for_calc: true operand pointing at the selected measure field; set format: "0.0%"; include the calc name in query.fields.
• Running total: add a calc using Omni.OMNI_RUNNING_TOTAL with one for_calc: true field operand. Sort the time dimension ascending before presenting values; do not sort descending and then reverse/recompute the running total outside Omni.
• Trailing 3-period moving average: if you are unsure of the exact AST, harvest it from an agentic job — omni ai job-submit <modelId> "monthly revenue with a trailing 3-month moving average as a table calculation", lift the calculations from actions[].generate_query, then validate with query run (generate-query --run-query=false is the simple/shape-only fallback). The expected shape is Omni.OMNI_FX_AVERAGE over Omni.OMNI_OFFSET_MULTI(field, -2, 0, 3, 1). If the output is a raw window_call, rewrite it to this canonical Omni calc shape unless the user specifically asked for a custom SQL window not expressible with Omni calc operators.
• Month-over-month % change: add a calc using Omni.OMNI_PERCENT_CHANGE_FROM_PREVIOUS with the same single for_calc: true revenue operand; sort the date field ascending; set format: "0.0%"; do not use omni_period_pivot, raw SQL, or a hand-authored LAG window when the template operator fits.
• Row total across pivot columns: for a pivoted query, set a numeric limit and add a calc with outside_pivot: true, Omni.OMNI_FX_SUM, and Omni.OMNI_PIVOT_OFFSET(field, 0, 0, 1, 50) to sweep across pivot columns. Include the row-total calc_name in query.fields; a pivoted query with limit: null is invalid.
• Multi-branch tier labels: use Omni.OMNI_FX_IFS, not an existing model field or SqlStdOperatorTable.CASE, for prompts like High if revenue > 10000, Mid if > 1000, else Low. OMNI_FX_IFS operands alternate (condition, value). Represent the default branch as a final tautology such as SqlStdOperatorTable.EQUALS(1, 1) followed by "Low". Build labels like "High - Acme Corp" with nested binary Omni.OMNI_FX_AMPERSAND calls: (tier & " - ") & <name field>. If the tier depends on a grouped measure, create two calcs: one for the tier and one for the concatenated label.
• SUMIF-style filtered total broadcast on every row: use Omni.OMNI_FX_SUM_IF (underscore between SUM and IF). Both the criteria range and sum range must be full-column Omni.OMNI_OFFSET_MULTI calls with (field, -536870911, 0, 1073741823, 1). The criterion is a string literal like "Complete", not a SQL predicate.
• VLOOKUP-style in-result lookup: first attempt Omni.OMNI_FX_VLOOKUP with four operands: lookup value, key field, full-column OMNI_OFFSET_MULTI over the key field, and a 1-based column number into query.fields starting at the key column. Use literal nodes for static lookup values and column numbers. Validate the query. If a static string lookup like "Complete" fails with No referenced query with id Complete found in query, report that this Omni deployment is treating the string as a query reference, stop retrying VLOOKUP variants, and use the OMNI_FX_SUM_IF broadcast pattern when the user needs a single status revenue repeated on every row. Do not replace this with userEditedSQL.
• Date difference: use Omni.OMNI_FX_DATEDIF in AST order (unit_literal, start_date, end_date), with the unit literal "DAY" and date-truncated operands such as created_at[date] and shipped_at[date]. Do not substitute a native model field unless the user asked for that existing field rather than a calculated column. A bare timestamp operand can produce blank values under swallow_errors; select [date] timeframes or cast to DATE. Filter out or separately explain null shipped dates so the validated diff column contains populated integer values.

For exact JSON AST examples — running totals, moving averages, conditional labels, pivot row totals, DATEDIF, SUM_IF, VLOOKUP, plus the full operator catalog (Omni.* and SqlStdOperatorTable.*), AST node types, validation rules, and the round-trip strategy for unfamiliar calcs — see references/table-calculations.md. Keep SKILL.md as the workflow guardrail and the reference file as the source of detailed shapes.

At execution, calcs compile into an outer SELECT wrapping the base aggregation; window-style operators emit ... OVER (...) there, so the shared data model never needs window functions to support them. In pivoted queries, template operators auto-partition by the pivot column for per-segment series; set outside_pivot: true and wrap an aggregator around OMNI_PIVOT_OFFSET for a row-summary that sweeps across pivot columns.

Running Raw SQL (userEditedSQL)

Given SQL? Reproduce it through a topic first (see Known Issues). Express the SQL's intent through the semantic layer; reach for raw userEditedSQL only when no topic can express it (SQL-first migration, warehouse-specific SQL, one-off ad-hoc read) or the user asks to run it as-is. If a faithful reproduction would need a field/topic that doesn't exist but should, propose modeling it (omni-model-builder) rather than defaulting to raw SQL.

Reading generate-query output: when the topic lacks a measure the metric needs, generate-query returns a ${}-templated userEditedSQL (e.g. SUM(${view.sale_price}) AS sale_price_sum FROM ${Topic}) and lists its SQL-output aliases (like sale_price_sum) in fields. Those aliases are not model fields — don't strip the SQL and try to run them semantically (they won't resolve). And the ${Topic} token resolves only inside generate-query's own execution — the templated query is not directly runnable via query run or persistable as a dashboard tile (it errors with No such view "Order Items"). So don't reuse it as-is; treat it as a signal that the topic is missing a measure and add that measure (omni-model-builder) so the metric becomes a clean semantic field.

userEditedSQL is a non-topic query pathway — the same family as a bare-view query (see "Fallback — non-topic query pathways"). It's an escape hatch for SQL the semantic layer can't express; prefer a topic or semantic fields when they fit.

omni query run --body '{
  "query": {
    "modelId": "<model-id>",
    "fields": [],
    "userEditedSQL": "select count(*) as cnt from ECOMM.ORDER_ITEMS"
  }
}'

• fields must be present (an array; may be empty []); table is not needed. The SQL is authoritative — populated fields/table are ignored when userEditedSQL is set.
• Runs against the model's connection — write warehouse-dialect SQL with fully-qualified names, not field references.
• "rewriteSql": false runs your SQL verbatim (the default parses and re-emits it — re-quoting identifiers, aliasing projections into the view.field namespace). "dbtMode": true allows Jinja/dbt templating. (Both are camelCase; the query object is permissive, so a misspelled/snake_case key is silently dropped.)
• Permission-gated: the querier's role must permit manually-written SQL. Without it the job fails — error_type: "FORBIDDEN", "queries based on manually written SQL are restricted" — returned as HTTP 200 with the error in the job body, not a 4xx.
• Access behavior: raw SQL bypasses all model controls — object-level access grants, row-level access filters, and always_where — and is invisible to Viewer/Restricted Querier roles in a dashboard. It's the most permissive pathway; use only for ad-hoc reads by privileged users, and strip it from any reused/dashboard query (see "Using Job Results in a Dashboard").
• Row cap: an unbounded raw query is capped at 50,000 rows (the response returns 50,001 — the cap plus one truncation sentinel). The envelope limit is not applied to raw SQL; put LIMIT in the SQL itself to bound results.

Request-level options (outside query)

These keys sit at the top level of the body, beside query, not inside it:

Option	Description
resultType	Output format: csv, xlsx, or json. Omit for the default base64 Arrow response.
cache	Cache policy: Standard, SkipRequery, SkipCache.
userId	Run as another user (org-scoped API keys); also the --userid flag.
branchId	Run against a model branch (validate draft model changes on live data). Must be a branch of the same shared model.
planOnly	Return the execution plan without running the query (validate/debug at no warehouse cost). Cannot combine with resultType.
formatResults	On exports, emit formatted values (e.g. $1,234.56) vs. raw. Requires resultType; ignored for Arrow.
timezone	Per-request timezone override (IANA id). Requires the connection setting allowsUserSpecificTimezones and the org setting allowsDocumentCanUseTimezoneOverride; silently no-ops if either is off.

Handling and Validating Results

Default response: base64-encoded Apache Arrow table. Arrow results are binary — you cannot parse individual row data from the raw response. To verify a query returned data, check summary.row_count in the response.

To read the results yourself (to validate or spot-check), request resultType: "csv" or "json" — both come back as text you can parse directly:

{ "query": { ... }, "resultType": "csv" }

resultType: "xlsx" is also valid, but it returns a binary .xlsx file (zip-based) — like the default Arrow blob, you can't read it inline without a spreadsheet app or a library. Use it only to deliver a file to a person, not to inspect results. For agent-side reading, stick to csv/json.

Result Validation

Every query response should be checked before trusting the results or presenting them to the user.

Check for errors:

• If the response contains an error key, the query failed. Common causes: bad field name, missing join path, malformed filter expression, permission error.
• If the response contains remaining_job_ids, the query is still running — poll with omni query wait before checking results.

Check row count:

• summary.row_count == 0 — the query returned no data. This may be valid (e.g., no data in the filter range) but is worth flagging to the user. Common causes: overly restrictive filters, wrong date range, field that doesn't match any rows.
• summary.row_count equals the limit you set — results may be truncated. If the user needs complete data, re-run with a higher limit or null for unlimited.

Spot-check data with CSV:

When accuracy matters, request CSV and scan the output:

omni query run --body '{
  "query": { ... },
  "resultType": "csv"
}'

Check that:

• Column headers match the fields you requested
• Values are in expected ranges (e.g., revenue isn't negative, dates aren't in the future)
• Aggregations make sense (e.g., a count isn't returning a sum)

Validate filter behavior:

If your query includes filters, verify they're being applied:

# Run the same query without filters
omni query run --body '{ "query": { ... (no filters) ... }, "resultType": "csv" }'

# Compare row counts — filtered should be <= unfiltered

If both queries return the same row count, the filter may not be binding (wrong field name, unsupported expression, or the known bug where boolean filters are dropped with pivots).

Validation Checklist

Check	How	When
No error in response	Check for error key	Every query
Data was returned	summary.row_count > 0	Every query
Results not truncated	row_count < limit	When completeness matters
Columns are correct	CSV column headers match requested fields	When building dashboards or reports
Values are reasonable	Spot-check CSV output	When presenting to users
Filters are applied	Compare filtered vs unfiltered row counts	When using filters
Long-running query completed	No remaining_job_ids in final response	Queries on large tables

Decoding Arrow Results

import base64, pyarrow as pa
arrow_bytes = base64.b64decode(response["data"])
reader = pa.ipc.open_stream(arrow_bytes)
df = reader.read_all().to_pandas()

Long-Running Queries

If the response includes remaining_job_ids, poll until complete:

omni query wait --jobids job-id-1,job-id-2

Running Queries from Dashboards

Extract and re-run queries powering existing dashboards:

# Get all queries from a dashboard
omni documents get-queries <dashboardId>

# Run as a specific user
omni query run --body '{ "query": { ... }, "userId": "user-uuid-here" }'

# Cache policy (valid values: Standard, SkipRequery, SkipCache)
omni query run --body '{ "query": { ... }, "cache": "SkipCache" }'

AI-Powered Query Generation

Instead of constructing query JSON manually, you can describe what you want in natural language and let Omni's AI generate the query.

Generate Query (synchronous)

The fastest path — returns a generated query JSON synchronously. Pass --run-query false to get only the query structure without executing it (default runs the query).

# Just generate the query JSON (no execution)
omni ai generate-query your-model-id "Show me revenue by month" --run-query false

Response:

{
  "query": {
    "fields": ["order_items.created_at[month]", "order_items.total_revenue"],
    "table": "order_items",
    "filters": {},
    "sorts": [{"column_name": "order_items.created_at[month]", "sort_descending": false}],
    "limit": 500
  },
  "topic": "order_items",
  "error": null
}

# Generate and execute in one call
omni ai generate-query your-model-id "Top 10 customers by lifetime spend"

Optional flags:

• --branch-id — test against a specific model branch
• --current-topic-name — constrain topic selection to a specific topic

Pick Topic

Check which topic the AI would select for a question, without generating a full query:

omni ai pick-topic your-model-id "How many users signed up last month?"

Agentic Queries (async)

For the full Blobby experience — multi-step analysis, tool use, and topic selection as the AI would actually behave in production. This is async: submit a job, poll for status, then retrieve the result.

# 1. Submit a job
omni ai job-submit your-model-id "Analyze revenue trends and identify our fastest growing product category"
# → returns { "jobId": "job-uuid", "conversationId": "conv-uuid" }

# 2. Poll the `state` field (NOT `status` — reading `.status` is empty every time)
omni ai job-status <jobId>

# 3. Get the result
omni ai job-result <jobId>

Job-status shape. Poll state — omni ai job-status has no status field. States: QUEUED → EXECUTING → DELIVERING → terminal COMPLETE / FAILED / CANCELLED. Note it's COMPLETE, not COMPLETED — the model-refresh (completed) and models jobs-get-status (COMPLETED) flows spell it differently, so a poll loop reused across job types needs a tolerant terminal check: read the field as state ?? status, lowercase it, treat startswith("complete") as done and {failed, cancelled, error} as failed. The answer text is resultSummary; structured output is under actions[] (type: "generate_query" → result.query).

The result contains an actions array with each step the AI took — look for actions with type: "generate_query" to extract the generated queries. The response also includes resultSummary with the AI's narrative interpretation.

Before presenting an async job answer, inspect the actions[] entries. A job can reach COMPLETE while an individual generate_query action has status: "pending" or no csvResult; the narrative may then describe a query that was generated but not executed. If a required action is pending, do not treat the job summary as final. Run or regenerate that specific query, or continue the same analysis with another async job, then present only validated results.

Additional job commands:

• omni ai job-cancel <jobId> — cancel a running job
• omni ai job-visualization <jobId> — get the visualization output

Using Job Results in a Dashboard

The query object inside a job result is not directly usable as a dashboard queryPresentation — it requires a transformation. Key rules:

• Always strip userEditedSQL — it makes the tile a non-topic query, so it bypasses all model controls (object-level access grants, row-level access filters, and always_where) and is invisible to restricted roles in a dashboard. The ${Order Items} topic-name token it contains also fails outside the job execution context.
• When calculations[] is non-empty, stripping userEditedSQL is sufficient — the structured calc renders correctly.
• When calculations[] is empty, Blobby authored the calc as inline SQL. The parsed AST is available in csvResultFields (at result level, not inside result["query"]) and can be reconstructed as a proper calculations[] entry. Fields whose top-level expr operator is an aggregate (SUM, COUNT, etc.) cannot be reconstructed as table calcs — add them to the model as filtered measures instead.

For the complete transformation algorithm, discriminator logic, field-ref injection, aggregate-skip handling, and sanity-check approach, see references/job-result-to-presentation.md.

When to Use Which Approach

Approach	Best For
omni query run	You know exactly which fields, filters, and sorts you need
omni query run with calculations[]	Explicit table-calculation requests where you know or can copy the AST shape
omni ai generate-query --run-query=false	Drafting a simple query AST to inspect/hand-edit; or shape-only when query execution isn't permitted (the fallback when you can't run an agentic job)
omni ai generate-query --run-query=true	Simple dimension/measure queries where you want a synchronous response
omni ai job-submit	Anything non-trivial — multi-step analysis, or generating a table calc / reusable query AST. Lift the structured query/calculations from actions[].generate_query, then validate with query run

Steer the prompt when you know the shape: when a table calculation is the desired or known-correct output, say so in the prompt — append "… as a table calculation", or name the semantics ("running total" / "% of total" / "moving average") — so the agentic job emits a real calc in actions[].generate_query rather than a userEditedSQL/SQL fallback. (The agentic-vs-generate-query split and the "lift the AST from actions[].generate_query, then validate with query run" rule are in the table above and under Table Calculations.)

Multi-Step Analysis Pattern

For complex analysis, chain queries:

1. Broad query — understand the shape of the data
2. Inspect results — identify interesting segments or patterns
3. Focused follow-ups — filter based on findings
4. Synthesize — combine results into a narrative

Common Query Patterns

Time Series: fields + date dimension + ascending sort + date filter

Top N: fields + metric + descending sort + limit

Aggregation with Breakdown: multiple dimensions + multiple measures + descending sort by key metric

Known Bugs

• IS_NOT_NULL filter generates IS NULL (reported Omni bug) — workaround: invert the filter logic or use the base view to apply the filter differently.
• Boolean filters may be silently dropped when a pivots array is present — if boolean filters aren't applying, remove the pivot and test again.
• Some natural-language date filter strings can hit query_id parser errors — retry with the typed date filter object shape shown above before abandoning the filter.

Linking to Results

Queries are ephemeral — there is no persistent URL for a query result. To give the user a shareable link:

• For existing dashboards: {OMNI_BASE_URL}/dashboards/{identifier} (the identifier comes from the document API response)
• For new analysis: Create a document via omni-content-builder with the query as a queryPresentation, then share {OMNI_BASE_URL}/dashboards/{identifier}

Docs Reference

• Query API · Running Document Queries · Querying Documentation · Filter Syntax

Related Skills

• omni-model-explorer — discover fields and topics before querying
• omni-content-explorer — find dashboards whose queries you can extract
• omni-content-builder — turn query results into dashboards
• omni-ai-eval — benchmark and test AI query generation accuracy