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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
52 lines
1.6 KiB
TypeScript
52 lines
1.6 KiB
TypeScript
import { describe, expect, it } from 'vitest';
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import { compareFusedSearchCandidates, DEFAULT_SEARCH_LANE_WEIGHTS, rrfContribution } from '../../../src/context/search/rrf.js';
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import type { FusedSearchCandidate } from '../../../src/context/search/types.js';
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describe('RRF scoring', () => {
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it('uses the shared lane weights from the hybrid search spec', () => {
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expect(DEFAULT_SEARCH_LANE_WEIGHTS).toEqual({
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semantic: 2,
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dictionary: 2,
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lexical: 1.5,
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token: 0.75,
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});
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});
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it('calculates a weighted RRF contribution with k=60 by default', () => {
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expect(rrfContribution(2, 1)).toBeCloseTo(2 / 61, 12);
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expect(rrfContribution(1.5, 2)).toBeCloseTo(1.5 / 62, 12);
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});
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it('sorts fused candidates by score, lane count, and stable id', () => {
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const first: FusedSearchCandidate = {
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id: 'orders',
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score: 0.05,
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matchReasons: ['lexical'],
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ranksByLane: { lexical: 1 },
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rawScoresByLane: {},
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evidenceByLane: {},
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};
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const second: FusedSearchCandidate = {
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id: 'customers',
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score: 0.05,
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matchReasons: ['lexical', 'semantic'],
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ranksByLane: { lexical: 2, semantic: 1 },
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rawScoresByLane: {},
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evidenceByLane: {},
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};
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const third: FusedSearchCandidate = {
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id: 'accounts',
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score: 0.04,
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matchReasons: ['semantic'],
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ranksByLane: { semantic: 1 },
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rawScoresByLane: {},
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evidenceByLane: {},
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};
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expect([first, second, third].sort(compareFusedSearchCandidates).map((candidate) => candidate.id)).toEqual([
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'customers',
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'orders',
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'accounts',
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]);
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});
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});
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