mirror of
https://github.com/Kaelio/ktx.git
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* 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
106 lines
3.1 KiB
TypeScript
106 lines
3.1 KiB
TypeScript
import { describe, expect, it, vi } from 'vitest';
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import { runKtxEmbeddingHealthCheck } from '../../src/llm/embedding-health.js';
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describe('KTX embedding health check', () => {
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it('runs a one-shot OpenAI embedding check through the configured provider', async () => {
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const createOpenAIClient = vi.fn(() => ({
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embeddings: {
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create: vi.fn().mockResolvedValue({
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data: [{ index: 0, embedding: [0.1, 0.2, 0.3] }],
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}),
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},
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}));
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await expect(
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runKtxEmbeddingHealthCheck(
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{
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backend: 'openai',
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model: 'text-embedding-3-small',
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dimensions: 3,
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openai: { apiKey: 'sk-openai-test' }, // pragma: allowlist secret
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},
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{ deps: { createOpenAIClient } },
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),
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).resolves.toEqual({ ok: true });
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expect(createOpenAIClient).toHaveBeenCalledWith({ apiKey: 'sk-openai-test', baseURL: undefined }); // pragma: allowlist secret
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});
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it('returns failed when the provider returns the wrong dimensions', async () => {
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const createOpenAIClient = vi.fn(() => ({
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embeddings: {
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create: vi.fn().mockResolvedValue({
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data: [{ index: 0, embedding: [0.1, 0.2] }],
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}),
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},
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}));
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await expect(
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runKtxEmbeddingHealthCheck(
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{
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backend: 'openai',
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model: 'text-embedding-3-small',
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dimensions: 3,
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openai: { apiKey: 'sk-openai-test' }, // pragma: allowlist secret
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},
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{ deps: { createOpenAIClient } },
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),
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).resolves.toEqual({
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ok: false,
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message: 'Embedding provider openai returned vector with 2 dimensions; expected 3',
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});
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});
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it('redacts credential values from health-check failures', async () => {
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const createOpenAIClient = vi.fn(() => ({
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embeddings: {
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create: vi.fn(async () => {
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throw new Error('401 invalid api key sk-openai-secret');
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}),
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},
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}));
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await expect(
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runKtxEmbeddingHealthCheck(
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{
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backend: 'openai',
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model: 'text-embedding-3-small',
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dimensions: 3,
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openai: { apiKey: 'sk-openai-secret' }, // pragma: allowlist secret
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},
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{ deps: { createOpenAIClient } },
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),
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).resolves.toEqual({
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ok: false,
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message: '401 invalid api key [redacted]',
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});
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});
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it('returns failed when the health check times out', async () => {
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const createOpenAIClient = vi.fn(() => ({
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embeddings: {
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create: vi.fn(
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() =>
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new Promise<{ data: Array<{ index?: number; embedding: number[] }>; usage?: { total_tokens?: number } }>(
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() => undefined,
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),
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),
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},
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}));
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await expect(
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runKtxEmbeddingHealthCheck(
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{
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backend: 'openai',
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model: 'text-embedding-3-small',
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dimensions: 3,
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openai: { apiKey: 'sk-openai-test' }, // pragma: allowlist secret
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},
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{ timeoutMs: 1, deps: { createOpenAIClient } },
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),
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).resolves.toEqual({
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ok: false,
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message: 'Embedding health check timed out after 1ms',
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});
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});
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});
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