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

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import { access, mkdtemp, rm } from 'node:fs/promises';
import { tmpdir } from 'node:os';
import { join } from 'node:path';
import { afterEach, beforeEach, describe, expect, it } 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 { initKtxProject, type KtxLocalProject } from '../../../src/context/project/project.js';
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import {
listLocalSlSources,
readLocalSlSource,
searchLocalSlSources,
validateLocalSlSource,
writeLocalSlSource,
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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} from '../../../src/context/sl/local-sl.js';
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const ORDERS_YAML = [
'name: orders',
'table: public.orders',
'grain:',
' - order_id',
'columns:',
' - name: order_id',
' type: string',
' - name: revenue',
' type: number',
'measures:',
' - name: total_revenue',
' expr: sum(revenue)',
'',
].join('\n');
const SUPPORT_YAML = [
'name: tickets',
'descriptions:',
' user: Support tickets grouped by priority.',
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'table: public.tickets',
'grain:',
' - ticket_id',
'columns:',
' - name: ticket_id',
' type: string',
' - name: priority',
' type: string',
'measures:',
' - name: ticket_count',
' expr: count(*)',
'',
].join('\n');
describe('local semantic-layer helpers', () => {
let tempDir: string;
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let project: KtxLocalProject;
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beforeEach(async () => {
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tempDir = await mkdtemp(join(tmpdir(), 'ktx-local-sl-'));
project = await initKtxProject({ projectDir: join(tempDir, 'project') });
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});
afterEach(async () => {
await rm(tempDir, { recursive: true, force: true });
});
it('writes, reads, lists, and validates semantic-layer sources', async () => {
const write = await writeLocalSlSource(project, {
connectionId: 'warehouse',
sourceName: 'orders',
yaml: ORDERS_YAML,
});
expect(write.path).toBe('semantic-layer/warehouse/orders.yaml');
await expect(
readLocalSlSource(project, { connectionId: 'warehouse', sourceName: 'orders' }),
).resolves.toMatchObject({
connectionId: 'warehouse',
name: 'orders',
path: 'semantic-layer/warehouse/orders.yaml',
yaml: ORDERS_YAML,
});
await expect(listLocalSlSources(project, { connectionId: 'warehouse' })).resolves.toEqual([
{
columnCount: 2,
connectionId: 'warehouse',
joinCount: 0,
measureCount: 1,
name: 'orders',
path: 'semantic-layer/warehouse/orders.yaml',
},
]);
await expect(validateLocalSlSource(ORDERS_YAML)).resolves.toEqual({ valid: true, errors: [] });
});
it('validates table-backed sources against matching physical manifests when project context is provided', async () => {
await project.fileStore.writeFile(
'semantic-layer/postgres-warehouse/_schema/orbit_analytics.yaml',
`tables:
int_active_contract_arr:
table: orbit_analytics.int_active_contract_arr
columns:
- { name: contract_id, type: string }
- { name: contract_arr_cents, type: number }
`,
'ktx',
'ktx@example.com',
'Add warehouse manifest',
);
const invalidDbtSource = [
'name: int_active_contract_arr',
'table: orbit_analytics.int_active_contract_arr',
'grain: [contract_id]',
'columns:',
' - { name: contract_id, type: string }',
' - { name: arr_cents, type: number }',
'measures:',
' - { name: arr, expr: sum(arr_cents) }',
'',
].join('\n');
const result = await validateLocalSlSource(invalidDbtSource, { project, connectionId: 'dbt-main' });
expect(result.valid).toBe(false);
expect(result.errors.join('\n')).toContain('arr_cents');
expect(result.errors.join('\n')).toContain('absent from physical table');
});
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it('lists and reads manifest-backed scan sources as queryable 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 expect(listLocalSlSources(project, { connectionId: 'warehouse' })).resolves.toEqual([
{
columnCount: 2,
connectionId: 'warehouse',
joinCount: 0,
measureCount: 0,
name: 'payments',
path: 'semantic-layer/warehouse/_schema/public.yaml#payments',
},
]);
await expect(readLocalSlSource(project, { connectionId: 'warehouse', sourceName: 'payments' })).resolves.toEqual(
expect.objectContaining({
columnCount: 2,
connectionId: 'warehouse',
joinCount: 0,
measureCount: 0,
name: 'payments',
path: 'semantic-layer/warehouse/_schema/public.yaml#payments',
yaml: expect.stringContaining('table: public.payments'),
}),
);
});
it('expands manifest-backed scan sources when listing all connections', 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 expect(listLocalSlSources(project)).resolves.toEqual([
{
columnCount: 2,
connectionId: 'warehouse',
joinCount: 0,
measureCount: 0,
name: 'payments',
path: 'semantic-layer/warehouse/_schema/public.yaml#payments',
},
]);
});
it('searches local semantic-layer source text through SQLite FTS', async () => {
await writeLocalSlSource(project, {
connectionId: 'warehouse',
sourceName: 'orders',
yaml: ORDERS_YAML,
});
await writeLocalSlSource(project, {
connectionId: 'warehouse',
sourceName: 'tickets',
yaml: SUPPORT_YAML,
});
const results = await searchLocalSlSources(project, { connectionId: 'warehouse', query: 'total revenue' });
expect(results).toEqual([
expect.objectContaining({
connectionId: 'warehouse',
name: 'orders',
path: 'semantic-layer/warehouse/orders.yaml',
score: expect.any(Number),
}),
]);
expect(results[0]?.score).toBeGreaterThan(0);
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await expect(access(join(project.projectDir, '.ktx/db.sqlite'))).resolves.toBeUndefined();
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});
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it('searches historic SQL usage and returns frequency tier plus FTS snippet', async () => {
await project.fileStore.writeFile(
'semantic-layer/warehouse/_schema/public.yaml',
`tables:
orders:
table: public.orders
usage:
narrative: Analysts inspect paid order lifecycle by customer segment.
frequencyTier: high
commonFilters:
- status
- created_at
commonGroupBys:
- customer_segment
commonJoins:
- table: public.customers
on:
- customer_id
columns:
- name: order_id
type: string
- name: status
type: string
`,
'ktx',
'ktx@example.com',
'Add usage-backed manifest shard',
);
const results = await searchLocalSlSources(project, {
connectionId: 'warehouse',
query: 'paid lifecycle customer segment',
});
expect(results).toEqual([
expect.objectContaining({
connectionId: 'warehouse',
name: 'orders',
path: 'semantic-layer/warehouse/_schema/public.yaml#orders',
frequencyTier: 'high',
snippet: expect.stringContaining('<mark>'),
matchReasons: expect.arrayContaining(['lexical']),
}),
]);
expect(results[0]?.snippet).toContain('lifecycle');
});
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it('searches all connections with one global hybrid ranking pass', async () => {
await writeLocalSlSource(project, {
connectionId: 'warehouse',
sourceName: 'orders',
yaml: ORDERS_YAML,
});
await writeLocalSlSource(project, {
connectionId: 'finance',
sourceName: 'orders',
yaml: [
'name: orders',
'descriptions:',
' user: Finance orders used for invoice reconciliation.',
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'table: finance.orders',
'grain:',
' - order_id',
'columns:',
' - name: order_id',
' type: string',
' - name: invoice_status',
' type: string',
'',
].join('\n'),
});
const results = await searchLocalSlSources(project, { query: 'orders' });
expect(results.map((result) => `${result.connectionId}/${result.name}`)).toEqual([
'finance/orders',
'warehouse/orders',
]);
expect(results[0]).toMatchObject({
score: expect.any(Number),
matchReasons: expect.arrayContaining(['lexical']),
lanes: expect.arrayContaining([expect.objectContaining({ lane: 'lexical', status: 'available' })]),
});
});
it('returns dictionary evidence when collected sample values explain a match', async () => {
await writeLocalSlSource(project, {
connectionId: 'warehouse',
sourceName: 'orders',
yaml: ORDERS_YAML,
});
await project.fileStore.writeFile(
'raw-sources/warehouse/live-database/sync-1/enrichment/relationship-profile.json',
`${JSON.stringify(
{
connectionId: 'warehouse',
driver: 'postgres',
sqlAvailable: true,
queryCount: 2,
tables: [],
columns: {
'orders.status': {
table: { catalog: null, db: 'public', name: 'orders' },
column: 'status',
nativeType: 'text',
normalizedType: 'string',
rowCount: 10,
nullCount: 0,
distinctCount: 2,
uniquenessRatio: 0.2,
nullRate: 0,
sampleValues: ['paid', 'refunded'],
minTextLength: 4,
maxTextLength: 8,
},
},
warnings: [],
},
null,
2,
)}\n`,
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'ktx',
'ktx@example.com',
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'Seed dictionary profile',
);
const results = await searchLocalSlSources(project, { connectionId: 'warehouse', query: 'refunded' });
expect(results).toEqual([
expect.objectContaining({
connectionId: 'warehouse',
name: 'orders',
matchReasons: ['dictionary'],
dictionaryMatches: [{ column: 'status', values: ['refunded'] }],
}),
]);
});
it('adds the token lane alongside lexical matches for normalized query terms', async () => {
await writeLocalSlSource(project, {
connectionId: 'warehouse',
sourceName: 'orders',
yaml: ORDERS_YAML,
});
const results = await searchLocalSlSources(project, { connectionId: 'warehouse', query: 'orders---' });
expect(results[0]).toMatchObject({
connectionId: 'warehouse',
name: 'orders',
matchReasons: expect.arrayContaining(['token']),
});
});
it('reports schema validation errors without writing invalid YAML', async () => {
const invalidYaml = ['name: broken', 'table: public.orders', 'columns: []', ''].join('\n');
await expect(validateLocalSlSource(invalidYaml)).resolves.toMatchObject({
valid: false,
errors: expect.arrayContaining([expect.stringContaining('grain')]),
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});
await expect(
writeLocalSlSource(project, {
connectionId: 'warehouse',
sourceName: 'broken',
yaml: invalidYaml,
}),
).rejects.toThrow('Invalid semantic-layer source');
});
it('reports overlay columns that are not computed columns', async () => {
fix(context): merge overlay columns onto manifest columns by name (#94) * fix(context): merge overlay columns onto manifest columns by name composeOverlay was appending overlay columns to the manifest column list, producing duplicate entries when dbt/metabase overlays declared a column just to attach descriptions. The duplicates carried no `type`, so the pydantic SourceDefinition rejected them at semantic-query time and broke `ktx sl query` for every overlay-backed measure. Now overlay columns match base columns by name (case-insensitive): same-name entries merge onto the manifest (overlay fields win, type/role fall back to the base, descriptions merge per source key) and only new names append. * refactor(sl): split overlay columns from column_overrides and enforce TS/Python wire contract Overlay sources now have two distinct collections: `columns:` for computed columns (requiring `expr` + `type`) and `column_overrides:` for metadata patches to inherited manifest columns. Composing or loading an overlay that mixes the two — or references an unknown column — fails with a typed error. Introduce `ResolvedSemanticLayerSource` / `resolvedSourceSchema` / `toResolvedWire` as the strict shape sent to the Python engine, and add a schema contract test that diffs Zod against the Pydantic JSON schema dumped by `python -m semantic_layer dump-schema`. `SourceDefinition` is now `extra="forbid"` on the Python side. `loadAllSources` surfaces per-file load errors instead of swallowing them, so validation/query paths can report manifest shard parse failures. * fix(context): make scan description generation resilient and quiet A transient sampleTable failure during ingest used to take out every table in a connection: generateTableDescription returned a hardcoded 'Table not found' string into descriptions.ai, and KtxDescriptionGenerator was constructed without a logger, so the failure left no trail anywhere. - sampleTable / sampleColumn calls retry 3x with 200/400/800ms backoff, honouring KtxScanContext.signal via a new KtxAbortedError. - On retry exhaustion or missing capability, table generation falls back to a metadata-only prompt built from column name / native type / comment / rawDescriptions. The column path follows the same rule -- call the LLM when any of samples or rawDescriptions are available; skip only when both are absent. - Logger is now threaded from KtxScanContext into the generator. Failures emit structured KtxScanWarning entries (new description_fallback_used code, plus existing sampling_failed / enrichment_failed / connector_capability_missing). ktx scan groups warnings by code so a batch of identical failures collapses to one summary line plus sample. - Returns null on failure instead of the 'Table not found' sentinel; the manifest writer's existing guard already skips empty descriptions, so schema YAML no longer carries misleading text. SCAN_MANAGED_DESCRIPTION_KEYS already strips stale 'ai' on merge, so existing YAML clears on next run. Also suppress AI SDK v6 'system in messages' warning: pull system messages out of KtxMessageBuilder.wrapSimple's output via a new splitKtxSystemMessages helper and pass them top-level to generateText (preserves cacheControl providerOptions on the SystemModelMessage). Agent-runner's local splitSystemPromptMessages dedupes onto the shared helper. * test(docs): align examples-docs assertions with revamped docs PR #103 (setup/guide doc revamp) reworded several CLI examples and connection labels; the assertions in scripts/examples-docs.test.mjs still referenced the pre-revamp wording and were failing in CI on main. Update the regexes to match the post-revamp content: - drop the `--json` flag from the sl-query example expectation - move the `Driver:` / `Status: ok` probe to the connection reference, which is where that output now lives (driver id is lowercase `postgres`, not the display name `PostgreSQL`) - drop the obsolete `Install \`uv\`...` troubleshooting line - accept `<connectionId>` everywhere; the docs no longer use the hyphenated `<connection-id>` form - match the `warehouse` connection id used in the quickstart instead of the `postgres-warehouse` id only used in the README and setup ref * fix(sl): skip TS/Python schema contract test when uv is unavailable The TypeScript checks CI job does not install uv or Python, so the module-level `execFileSync('uv', ...)` in schemas.contract.test.ts threw ENOENT and failed the suite. Wrap the schema dump in a try/catch and guard the describe block with `describe.skipIf` so the test skips in environments without uv. Local dev and any CI job that has uv on PATH still runs the cross-language contract assertion.
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const invalidYaml = [
'name: orders',
'columns:',
' - name: status',
' descriptions:',
' user: Order status.',
'',
].join('\n');
await expect(
validateLocalSlSource(invalidYaml, { project, connectionId: 'warehouse', sourceName: 'orders' }),
).resolves.toEqual({
valid: false,
errors: expect.arrayContaining([expect.stringContaining('columns.0.type')]),
fix(context): merge overlay columns onto manifest columns by name (#94) * fix(context): merge overlay columns onto manifest columns by name composeOverlay was appending overlay columns to the manifest column list, producing duplicate entries when dbt/metabase overlays declared a column just to attach descriptions. The duplicates carried no `type`, so the pydantic SourceDefinition rejected them at semantic-query time and broke `ktx sl query` for every overlay-backed measure. Now overlay columns match base columns by name (case-insensitive): same-name entries merge onto the manifest (overlay fields win, type/role fall back to the base, descriptions merge per source key) and only new names append. * refactor(sl): split overlay columns from column_overrides and enforce TS/Python wire contract Overlay sources now have two distinct collections: `columns:` for computed columns (requiring `expr` + `type`) and `column_overrides:` for metadata patches to inherited manifest columns. Composing or loading an overlay that mixes the two — or references an unknown column — fails with a typed error. Introduce `ResolvedSemanticLayerSource` / `resolvedSourceSchema` / `toResolvedWire` as the strict shape sent to the Python engine, and add a schema contract test that diffs Zod against the Pydantic JSON schema dumped by `python -m semantic_layer dump-schema`. `SourceDefinition` is now `extra="forbid"` on the Python side. `loadAllSources` surfaces per-file load errors instead of swallowing them, so validation/query paths can report manifest shard parse failures. * fix(context): make scan description generation resilient and quiet A transient sampleTable failure during ingest used to take out every table in a connection: generateTableDescription returned a hardcoded 'Table not found' string into descriptions.ai, and KtxDescriptionGenerator was constructed without a logger, so the failure left no trail anywhere. - sampleTable / sampleColumn calls retry 3x with 200/400/800ms backoff, honouring KtxScanContext.signal via a new KtxAbortedError. - On retry exhaustion or missing capability, table generation falls back to a metadata-only prompt built from column name / native type / comment / rawDescriptions. The column path follows the same rule -- call the LLM when any of samples or rawDescriptions are available; skip only when both are absent. - Logger is now threaded from KtxScanContext into the generator. Failures emit structured KtxScanWarning entries (new description_fallback_used code, plus existing sampling_failed / enrichment_failed / connector_capability_missing). ktx scan groups warnings by code so a batch of identical failures collapses to one summary line plus sample. - Returns null on failure instead of the 'Table not found' sentinel; the manifest writer's existing guard already skips empty descriptions, so schema YAML no longer carries misleading text. SCAN_MANAGED_DESCRIPTION_KEYS already strips stale 'ai' on merge, so existing YAML clears on next run. Also suppress AI SDK v6 'system in messages' warning: pull system messages out of KtxMessageBuilder.wrapSimple's output via a new splitKtxSystemMessages helper and pass them top-level to generateText (preserves cacheControl providerOptions on the SystemModelMessage). Agent-runner's local splitSystemPromptMessages dedupes onto the shared helper. * test(docs): align examples-docs assertions with revamped docs PR #103 (setup/guide doc revamp) reworded several CLI examples and connection labels; the assertions in scripts/examples-docs.test.mjs still referenced the pre-revamp wording and were failing in CI on main. Update the regexes to match the post-revamp content: - drop the `--json` flag from the sl-query example expectation - move the `Driver:` / `Status: ok` probe to the connection reference, which is where that output now lives (driver id is lowercase `postgres`, not the display name `PostgreSQL`) - drop the obsolete `Install \`uv\`...` troubleshooting line - accept `<connectionId>` everywhere; the docs no longer use the hyphenated `<connection-id>` form - match the `warehouse` connection id used in the quickstart instead of the `postgres-warehouse` id only used in the README and setup ref * fix(sl): skip TS/Python schema contract test when uv is unavailable The TypeScript checks CI job does not install uv or Python, so the module-level `execFileSync('uv', ...)` in schemas.contract.test.ts threw ENOENT and failed the suite. Wrap the schema dump in a try/catch and guard the describe block with `describe.skipIf` so the test skips in environments without uv. Local dev and any CI job that has uv on PATH still runs the cross-language contract assertion.
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});
});
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it('rejects unsafe source paths', async () => {
await expect(
readLocalSlSource(project, {
connectionId: 'warehouse',
sourceName: '../orders',
}),
).rejects.toThrow('Unsafe semantic-layer source name');
});
});