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https://github.com/Kaelio/ktx.git
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56985b7e09
* 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
67 lines
1.9 KiB
TypeScript
67 lines
1.9 KiB
TypeScript
import { describe, expect, test } from 'vitest';
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import { kmeans, pickK } from '../../../../src/context/ingest/clustering/kmeans.js';
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describe('pickK', () => {
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test('uses ceil(N/8) heuristic clamped to [1, 10]', () => {
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expect(pickK(0)).toBe(0);
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expect(pickK(1)).toBe(1);
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expect(pickK(8)).toBe(1);
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expect(pickK(9)).toBe(2);
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expect(pickK(24)).toBe(3);
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expect(pickK(81)).toBe(10);
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expect(pickK(1000)).toBe(10);
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});
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});
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describe('kmeans', () => {
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test('separates two well-spaced gaussians', () => {
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const points = [
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[0, 0],
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[0.1, 0.1],
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[-0.1, 0.05],
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[10, 10],
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[10.1, 9.9],
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[9.95, 10.05],
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];
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const { assignments } = kmeans(points, 2, { seed: 42 });
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expect(assignments[0]).toBe(assignments[1]);
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expect(assignments[0]).toBe(assignments[2]);
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expect(assignments[3]).toBe(assignments[4]);
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expect(assignments[3]).toBe(assignments[5]);
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expect(assignments[0]).not.toBe(assignments[3]);
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});
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test('is deterministic with same seed', () => {
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const points = Array.from({ length: 30 }, (_, i) => [Math.sin(i), Math.cos(i)]);
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const a = kmeans(points, 4, { seed: 7 }).assignments;
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const b = kmeans(points, 4, { seed: 7 }).assignments;
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expect(a).toEqual(b);
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});
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test('k=1 puts everything in one cluster', () => {
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const points = [
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[1, 0],
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[0, 1],
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[-1, 0],
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[0, -1],
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];
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const { assignments } = kmeans(points, 1, { seed: 1 });
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expect(new Set(assignments).size).toBe(1);
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});
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test('k>=N produces N singleton clusters', () => {
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const points = [
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[1, 0],
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[0, 1],
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[-1, 0],
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];
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const { assignments } = kmeans(points, 3, { seed: 1 });
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expect(new Set(assignments).size).toBe(3);
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});
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test('handles empty input', () => {
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const { assignments, centroids } = kmeans([], 3, { seed: 1 });
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expect(assignments).toEqual([]);
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expect(centroids).toEqual([]);
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});
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});
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