ktx/packages/cli/test/context/sl/local-query.test.ts

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import { mkdtemp, rm } from 'node:fs/promises';
import { tmpdir } from 'node:os';
import { join } from 'node:path';
import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
test: split cli tests from source tree (#216) * feat(cli): define full warehouse dialect contract * test(cli): keep dialect edge tests focused * fix(cli): stabilize dialect contract foundation * refactor(connectors): own read-only query preparation * refactor(connectors): resolve dialects through registry * refactor(connectors): keep concrete dialect classes internal * chore(workspace): enforce dialect import boundary * refactor(cli): resolve relationship dialect at scan boundary * refactor(cli): use dialect display parsing for entity details * refactor(cli): use dialect display parsing for warehouse catalog * refactor(cli): use dialect SQL in relationship workflows * test(cli): verify solid dialect scan workflow closure * test: split cli tests from source tree * refactor(cli): standardize BigQuery scope listing * feat(sqlite): implement connector scope listing * test(connectors): cover required table listing * feat(cli): add warehouse driver registry * refactor(setup): route scope discovery through driver registry * refactor(cli): route local query execution through driver registry * refactor(historic-sql): route dialect support through driver registry * refactor(cli): test warehouse connections through driver registry * fix(cli): close driver registry type export gaps * Improve setup daemon diagnostics * refactor(setup): centralize rail-prefixed diagnostics + query-history fallback Extract errorMessage, writePrefixedLines, and flushPrefixedBufferedCommandOutput into clack.ts so the setup wizard, managed daemons, and embedding/agent steps share one rail-formatted writer. setup-databases.ts also adds a "disable query history and retry" option when the schema-context build fails and query history is the likely culprit, surfaced via a new failed-query-history-unavailable status. * fix(cli): carry catalog through the picker so BigQuery/Snowflake/SQL Server scope filters match The setup picker's KtxTableListEntry was a 2-level { schema, name }, so qualifiedTableId always wrote db.name into enabled_tables. When BigQuery, Snowflake, or SQL Server later ran fast ingest, their introspect step filtered the scope set with scopedTableNames(scope, { catalog: projectId|database, db }) — catalog was non-null on the introspect side but null in the scope refs, so every entry was rejected, the live-database adapter staged zero table files, and detect() failed with 'Adapter "live-database" did not recognize fetched source output'. Align the picker boundary with the canonical 3-level KtxTableRef: - Add catalog: string | null to KtxTableListEntry. - BigQuery/Snowflake/SQL Server listTables populate catalog from the resolved projectId / database; Postgres/MySQL/ClickHouse/SQLite set null. - qualifiedTableId emits catalog.schema.name when catalog is non-null (resolveEnabledTables already accepts the 3-part shape) and schemasFromEnabledTables now goes through parseDottedTableEntry so it recovers the schema correctly from both 2-part and 3-part entries. - Export parseDottedTableEntry from enabled-tables.ts (@internal) for picker reuse. Update listTables expectations in all seven connector tests and the setup / picker test fixtures. Add a picker regression test that covers the catalog-bearing round-trip (save + refine). * fix(cli): allow debug telemetry under opt-out env
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import type { KtxSemanticLayerComputePort } from '../../../src/context/daemon/semantic-layer-compute.js';
import { initKtxProject, type KtxLocalProject } from '../../../src/context/project/project.js';
import { compileLocalSlQuery } from '../../../src/context/sl/local-query.js';
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describe('compileLocalSlQuery', () => {
let tempDir: string;
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let project: KtxLocalProject;
let compute: KtxSemanticLayerComputePort;
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beforeEach(async () => {
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tempDir = await mkdtemp(join(tmpdir(), 'ktx-local-query-'));
project = await initKtxProject({ projectDir: join(tempDir, 'project') });
project.config.connections.warehouse = { driver: 'postgres' };
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await project.fileStore.writeFile(
'semantic-layer/warehouse/orders.yaml',
`name: orders
table: public.orders
grain:
- id
columns:
- name: id
type: number
- name: status
type: string
measures:
- name: order_count
expr: count(*)
joins: []
`,
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'ktx',
'ktx@example.com',
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'Add orders source',
);
await project.fileStore.writeFile(
'semantic-layer/warehouse/orders_overlay.yaml',
`name: orders_overlay
inherits_columns_from: orders
columns:
- name: paid_at
type: timestamp
joins: []
measures: []
grain: []
`,
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'ktx',
'ktx@example.com',
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'Add overlay source',
);
compute = {
query: vi.fn(async (input) => ({
sql: 'select status, count(*) as order_count from public.orders group by status',
dialect: input.dialect,
columns: [{ name: 'orders.status' }, { name: 'orders.order_count' }],
plan: { measures: input.query.measures, dimensions: input.query.dimensions },
})),
validateSources: vi.fn(),
generateSources: vi.fn(),
};
});
afterEach(async () => {
await rm(tempDir, { recursive: true, force: true });
});
it('compiles a local semantic-layer query with computable sources only', async () => {
const result = await compileLocalSlQuery(project, {
connectionId: 'warehouse',
query: {
measures: ['orders.order_count'],
dimensions: ['orders.status'],
limit: 25,
},
compute,
});
expect(compute.query).toHaveBeenCalledWith({
sources: [
{
name: 'orders',
table: 'public.orders',
grain: ['id'],
columns: [
{ name: 'id', type: 'number' },
{ name: 'status', type: 'string' },
],
measures: [{ name: 'order_count', expr: 'count(*)' }],
joins: [],
},
],
dialect: 'postgres',
query: {
measures: ['orders.order_count'],
dimensions: ['orders.status'],
limit: 25,
},
});
expect(result).toEqual({
connectionId: 'warehouse',
dialect: 'postgres',
sql: 'select status, count(*) as order_count from public.orders group by status',
headers: ['orders.status', 'orders.order_count'],
rows: [],
totalRows: 0,
plan: {
measures: ['orders.order_count'],
dimensions: ['orders.status'],
execution: {
mode: 'compile_only',
reason: 'Local semantic-layer query compiled SQL but no data-source execution adapter is configured.',
},
},
});
});
it('compiles a local semantic-layer query from manifest-backed scan sources', async () => {
await project.fileStore.writeFile(
'semantic-layer/warehouse/_schema/public.yaml',
`tables:
payments:
table: public.payments
columns:
- name: payment_id
type: number
pk: true
- name: amount
type: number
`,
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'ktx',
'ktx@example.com',
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'Add manifest shard',
);
await compileLocalSlQuery(project, {
connectionId: 'warehouse',
query: {
measures: ['sum(payments.amount)'],
dimensions: [],
},
compute,
});
expect(compute.query).toHaveBeenLastCalledWith({
sources: expect.arrayContaining([
{
name: 'payments',
table: 'public.payments',
grain: ['payment_id'],
columns: [
{
name: 'payment_id',
type: 'number',
role: undefined,
descriptions: undefined,
constraints: undefined,
enum_values: undefined,
tests: undefined,
},
{
name: 'amount',
type: 'number',
role: undefined,
descriptions: undefined,
constraints: undefined,
enum_values: undefined,
tests: undefined,
},
],
joins: [],
measures: [],
},
]),
dialect: 'postgres',
query: {
measures: ['sum(payments.amount)'],
dimensions: [],
},
});
});
feat(setup): add Claude Desktop target and MCP-first agent setup (#114) * feat(setup): add Claude Desktop target and MCP-first agent setup Adds `ktx mcp stdio` and a `claude-desktop` setup target that generates a local plugin ZIP wiring the analytics skill and a stdio MCP config. Replaces the CLI-only agent install mode with MCP+analytics (default) and an optional admin CLI skill, renames the research skill to analytics, and lets interactive setup pick project vs global scope when every target supports it. Extracts a shared MCP server factory used by both HTTP and stdio entrypoints. * Add MCP agent client setup support * Polish setup output formatting * Add MCP tool polish design spec Design for slimming the MCP-registered surface from 25 to 11 tools, introducing memory_ingest, applying the per-tool polish kit (annotations, outputSchema, .describe(), in-band error wrapping, union-drift fixes, type-narrowed jsonToolResult), emitting progress notifications on sql_execution + sl_query, and refining the ktx-analytics SKILL.md to match. * Refine MCP tool polish design spec after adversarial review iteration 1 * Refine MCP tool polish design spec after adversarial review iteration 2 * Refine MCP tool polish design spec after adversarial review iteration 3 * refactor(context): rename memory capture service to ingest * feat(mcp): slim research tool surface * refactor(mcp): remove admin ports from server factory * refactor(cli): rename text ingest memory port * docs: update analytics skill for memory ingest * chore: verify mcp surface rename * Add MCP tool polish v1 surface change plan * feat(context): polish mcp tool metadata * fix(context): enforce resolved semantic layer compute sources * feat(context): emit mcp query progress stages * fix(context): keep mcp progress event internal * Add MCP tool polish v1 metadata & progress plan * Fix CI snapshot and docs checks
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it('strips authoring-only fields (usage, inherits_columns_from) before sending sources to the daemon', async () => {
await project.fileStore.writeFile(
'semantic-layer/warehouse/_schema/public.yaml',
`tables:
invoices:
table: public.invoices
columns:
- name: invoice_id
type: number
pk: true
- name: amount
type: number
usage:
narrative: Activation policy windows table for invoice analytics.
frequencyTier: mid
commonFilters:
- amount
commonGroupBys: []
commonJoins: []
staleSince: null
`,
'ktx',
'ktx@example.com',
'Add manifest shard with usage',
);
await compileLocalSlQuery(project, {
connectionId: 'warehouse',
query: { measures: ['sum(invoices.amount)'], dimensions: [] },
compute,
});
const lastCall = (compute.query as ReturnType<typeof vi.fn>).mock.calls.at(-1)?.[0];
const invoices = lastCall?.sources.find((s: Record<string, unknown>) => s.name === 'invoices');
expect(invoices).toBeDefined();
expect(invoices).not.toHaveProperty('usage');
expect(invoices).not.toHaveProperty('inherits_columns_from');
expect(invoices).not.toHaveProperty('source_type');
});
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it('resolves the only configured connection when connectionId is omitted', async () => {
await compileLocalSlQuery(project, {
query: { measures: ['orders.order_count'], dimensions: [] },
compute,
});
expect(compute.query).toHaveBeenCalledWith(
expect.objectContaining({
dialect: 'postgres',
}),
);
});
it('executes compiled SQL through a local query executor when requested', async () => {
const queryExecutor = {
execute: vi.fn(async () => ({
headers: ['status', 'order_count'],
rows: [['paid', 2]],
totalRows: 1,
command: 'SELECT',
rowCount: 1,
})),
};
const result = await compileLocalSlQuery(project, {
connectionId: 'warehouse',
query: {
measures: ['orders.order_count'],
dimensions: ['orders.status'],
limit: 25,
},
compute,
execute: true,
maxRows: 10,
queryExecutor,
});
expect(queryExecutor.execute).toHaveBeenCalledWith({
connectionId: 'warehouse',
projectDir: project.projectDir,
connection: { driver: 'postgres' },
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sql: 'select status, count(*) as order_count from public.orders group by status',
maxRows: 10,
});
expect(result.rows).toEqual([['paid', 2]]);
expect(result.totalRows).toBe(1);
expect(result.plan.execution).toEqual({
mode: 'executed',
driver: 'postgres',
maxRows: 10,
rowCount: 1,
});
});
feat(setup): add Claude Desktop target and MCP-first agent setup (#114) * feat(setup): add Claude Desktop target and MCP-first agent setup Adds `ktx mcp stdio` and a `claude-desktop` setup target that generates a local plugin ZIP wiring the analytics skill and a stdio MCP config. Replaces the CLI-only agent install mode with MCP+analytics (default) and an optional admin CLI skill, renames the research skill to analytics, and lets interactive setup pick project vs global scope when every target supports it. Extracts a shared MCP server factory used by both HTTP and stdio entrypoints. * Add MCP agent client setup support * Polish setup output formatting * Add MCP tool polish design spec Design for slimming the MCP-registered surface from 25 to 11 tools, introducing memory_ingest, applying the per-tool polish kit (annotations, outputSchema, .describe(), in-band error wrapping, union-drift fixes, type-narrowed jsonToolResult), emitting progress notifications on sql_execution + sl_query, and refining the ktx-analytics SKILL.md to match. * Refine MCP tool polish design spec after adversarial review iteration 1 * Refine MCP tool polish design spec after adversarial review iteration 2 * Refine MCP tool polish design spec after adversarial review iteration 3 * refactor(context): rename memory capture service to ingest * feat(mcp): slim research tool surface * refactor(mcp): remove admin ports from server factory * refactor(cli): rename text ingest memory port * docs: update analytics skill for memory ingest * chore: verify mcp surface rename * Add MCP tool polish v1 surface change plan * feat(context): polish mcp tool metadata * fix(context): enforce resolved semantic layer compute sources * feat(context): emit mcp query progress stages * fix(context): keep mcp progress event internal * Add MCP tool polish v1 metadata & progress plan * Fix CI snapshot and docs checks
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it('emits progress while compiling and executing a local semantic-layer query', async () => {
const progress: Array<{ progress: number; message: string }> = [];
const queryExecutor = {
execute: vi.fn(async () => ({
headers: ['status', 'order_count'],
rows: [['paid', 2]],
totalRows: 1,
command: 'SELECT',
rowCount: 1,
})),
};
const result = await compileLocalSlQuery(project, {
connectionId: 'warehouse',
query: {
measures: ['orders.order_count'],
dimensions: ['orders.status'],
limit: 25,
},
compute,
execute: true,
maxRows: 10,
queryExecutor,
onProgress: (event) => {
progress.push({ progress: event.progress, message: event.message });
},
});
expect(result.totalRows).toBe(1);
expect(progress).toEqual([
{ progress: 0, message: 'Compiling query' },
{ progress: 0.3, message: 'Generating SQL' },
{ progress: 0.6, message: 'Executing' },
{ progress: 1, message: 'Fetched 1 rows' },
]);
});
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it('requires a query executor for executed mode', async () => {
await expect(
compileLocalSlQuery(project, {
connectionId: 'warehouse',
query: { measures: ['orders.order_count'], dimensions: [] },
compute,
execute: true,
}),
).rejects.toThrow('Local semantic-layer execution requires a query executor.');
});
it('requires connectionId when multiple connections are configured', async () => {
project.config.connections.analytics = { driver: 'bigquery' };
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await expect(
compileLocalSlQuery(project, {
query: { measures: ['orders.order_count'], dimensions: [] },
compute,
}),
).rejects.toThrow('connectionId is required when the local project has zero or multiple connections.');
});
});