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* 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.
264 lines
9.1 KiB
TypeScript
264 lines
9.1 KiB
TypeScript
import { readFile } from 'node:fs/promises';
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import { createDefaultLocalQueryExecutor, type KtxSqlQueryExecutorPort } from '@ktx/context/connections';
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import {
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createLocalKtxEmbeddingProviderFromConfig,
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KtxIngestEmbeddingPortAdapter,
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type KtxEmbeddingPort,
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} from '@ktx/context';
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import type { KtxSemanticLayerComputePort } from '@ktx/context/daemon';
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import { loadKtxProject, type KtxLocalProject } from '@ktx/context/project';
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import {
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compileLocalSlQuery,
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listLocalSlSources,
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readLocalSlSource,
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searchLocalSlSources,
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validateLocalSlSource,
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type LocalSlSourceSearchResult,
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type LocalSlSourceSummary,
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type SemanticLayerQueryInput,
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} from '@ktx/context/sl';
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import type { PrintListColumn } from './io/print-list.js';
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import {
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createManagedPythonSemanticLayerComputePort,
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type KtxManagedPythonInstallPolicy,
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} from './managed-python-command.js';
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import { profileMark } from './startup-profile.js';
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profileMark('module:sl');
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type SlQueryFormat = 'json' | 'sql';
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export type KtxSlArgs =
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| { command: 'list'; projectDir: string; connectionId?: string; output?: string; json?: boolean }
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| {
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command: 'search';
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projectDir: string;
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connectionId?: string;
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query: string;
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limit?: number;
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output?: string;
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json?: boolean;
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}
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| { command: 'validate'; projectDir: string; connectionId: string; sourceName: string }
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| {
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command: 'query';
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projectDir: string;
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connectionId?: string;
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query?: SemanticLayerQueryInput;
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queryFile?: string;
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format: SlQueryFormat;
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execute: boolean;
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maxRows?: number;
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cliVersion: string;
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runtimeInstallPolicy: KtxManagedPythonInstallPolicy;
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};
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interface KtxSlIo {
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stdout: { write(chunk: string): void };
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stderr: { write(chunk: string): void };
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}
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interface KtxSlDeps {
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loadProject?: typeof loadKtxProject;
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embeddingService?: KtxEmbeddingPort | null;
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createEmbeddingProvider?: typeof createLocalKtxEmbeddingProviderFromConfig;
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createSemanticLayerCompute?: () => KtxSemanticLayerComputePort;
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createManagedSemanticLayerCompute?: (options: {
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cliVersion: string;
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installPolicy: KtxManagedPythonInstallPolicy;
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io: KtxSlIo;
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}) => Promise<KtxSemanticLayerComputePort>;
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createQueryExecutor?: () => KtxSqlQueryExecutorPort;
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}
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function slSearchEmbeddingService(project: KtxLocalProject, deps: KtxSlDeps): KtxEmbeddingPort | null {
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if ('embeddingService' in deps) {
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return deps.embeddingService ?? null;
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}
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const provider = (deps.createEmbeddingProvider ?? createLocalKtxEmbeddingProviderFromConfig)(
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project.config.ingest.embeddings,
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);
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return provider ? new KtxIngestEmbeddingPortAdapter(provider) : null;
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}
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async function printSlSources(input: {
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rows: ReadonlyArray<LocalSlSourceSummary>;
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command: 'sl list';
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output?: string;
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json?: boolean;
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io: KtxSlIo;
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emptyMessage: string;
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emptyHint?: string;
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}): Promise<void>;
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async function printSlSources(input: {
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rows: ReadonlyArray<LocalSlSourceSearchResult>;
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command: 'sl search';
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output?: string;
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json?: boolean;
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io: KtxSlIo;
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emptyMessage: string;
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emptyHint?: string;
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}): Promise<void>;
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async function printSlSources(input: {
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rows: ReadonlyArray<LocalSlSourceSummary | LocalSlSourceSearchResult>;
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command: 'sl list' | 'sl search';
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output?: string;
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json?: boolean;
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io: KtxSlIo;
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emptyMessage: string;
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emptyHint?: string;
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}): Promise<void> {
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const { resolveOutputMode } = await import('./io/mode.js');
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const { printList } = await import('./io/print-list.js');
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const mode = resolveOutputMode({ explicit: input.output, json: input.json, io: input.io });
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if (input.command === 'sl search') {
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const searchColumns: ReadonlyArray<PrintListColumn<LocalSlSourceSearchResult>> = [
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{
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key: 'score',
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label: 'SCORE',
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plain: 'score=',
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role: 'badge',
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prettyFormat: (value) => `${Math.round(Number(value) * 100)}%`,
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dim: true,
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},
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{ key: 'connectionId', label: 'CONNECTION', plain: '' },
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{ key: 'name', label: 'NAME', plain: '' },
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{ key: 'columnCount', label: 'COLS', plain: 'columns=', dim: true },
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{ key: 'measureCount', label: 'MEASURES', plain: 'measures=', dim: true },
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{ key: 'joinCount', label: 'JOINS', plain: 'joins=', dim: true },
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{ key: 'description', label: 'DESCRIPTION', plain: false, optional: true, dim: true },
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];
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printList<LocalSlSourceSearchResult>({
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rows: input.rows as ReadonlyArray<LocalSlSourceSearchResult>,
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columns: searchColumns,
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groupBy: 'connectionId',
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emptyMessage: input.emptyMessage,
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emptyHint: input.emptyHint,
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unit: 'source',
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command: input.command,
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mode,
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io: input.io,
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});
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return;
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}
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const listColumns: ReadonlyArray<PrintListColumn<LocalSlSourceSummary>> = [
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{ key: 'connectionId', label: 'CONNECTION', plain: '' },
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{ key: 'name', label: 'NAME', plain: '' },
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{ key: 'columnCount', label: 'COLS', plain: 'columns=', dim: true },
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{ key: 'measureCount', label: 'MEASURES', plain: 'measures=', dim: true },
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{ key: 'joinCount', label: 'JOINS', plain: 'joins=', dim: true },
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{ key: 'description', label: 'DESCRIPTION', plain: false, optional: true, dim: true },
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];
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printList<LocalSlSourceSummary>({
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rows: input.rows as ReadonlyArray<LocalSlSourceSummary>,
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columns: listColumns,
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groupBy: 'connectionId',
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emptyMessage: input.emptyMessage,
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emptyHint: input.emptyHint,
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unit: 'source',
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command: input.command,
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mode,
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io: input.io,
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});
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}
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async function readSlQueryFile(path: string): Promise<SemanticLayerQueryInput> {
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const parsed = JSON.parse(await readFile(path, 'utf-8')) as unknown;
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if (!parsed || typeof parsed !== 'object' || Array.isArray(parsed)) {
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throw new Error(`${path} must contain a JSON object.`);
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}
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return parsed as SemanticLayerQueryInput;
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}
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export async function runKtxSl(args: KtxSlArgs, io: KtxSlIo = process, deps: KtxSlDeps = {}): Promise<number> {
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try {
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const project = await (deps.loadProject ?? loadKtxProject)({ projectDir: args.projectDir });
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if (args.command === 'list') {
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const sources = await listLocalSlSources(project, { connectionId: args.connectionId });
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await printSlSources({
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rows: sources,
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emptyMessage: `No semantic-layer sources found in ${project.projectDir}`,
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command: 'sl list',
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output: args.output,
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json: args.json,
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io,
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});
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return 0;
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}
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if (args.command === 'search') {
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const sources = await searchLocalSlSources(project, {
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connectionId: args.connectionId,
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query: args.query,
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embeddingService: slSearchEmbeddingService(project, deps),
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limit: args.limit,
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});
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await printSlSources({
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rows: sources,
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emptyMessage: `No semantic-layer sources matched "${args.query}" in ${project.projectDir}`,
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emptyHint: 'Run `ktx sl list` to inspect available sources.',
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command: 'sl search',
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output: args.output,
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json: args.json,
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io,
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});
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return 0;
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}
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if (args.command === 'validate') {
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const source = await readLocalSlSource(project, {
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connectionId: args.connectionId,
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sourceName: args.sourceName,
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});
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if (!source) {
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throw new Error(`Semantic-layer source "${args.connectionId}/${args.sourceName}" was not found`);
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}
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const result = await validateLocalSlSource(source.yaml, {
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project,
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connectionId: args.connectionId,
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sourceName: args.sourceName,
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});
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if (!result.valid) {
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for (const error of result.errors) {
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io.stderr.write(`${error}\n`);
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}
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return 1;
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}
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io.stdout.write(`Valid semantic-layer source: ${args.connectionId}/${args.sourceName}\n`);
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return 0;
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}
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if (args.command === 'query') {
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const query = args.query ?? (args.queryFile ? await readSlQueryFile(args.queryFile) : undefined);
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if (!query) {
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throw new Error('sl query requires query input from --query-file or at least one --measure');
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}
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const compute = deps.createSemanticLayerCompute
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? deps.createSemanticLayerCompute()
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: await (deps.createManagedSemanticLayerCompute ?? createManagedPythonSemanticLayerComputePort)({
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cliVersion: args.cliVersion,
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installPolicy: args.runtimeInstallPolicy,
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io,
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});
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const queryExecutor = args.execute ? (deps.createQueryExecutor ?? createDefaultLocalQueryExecutor)() : undefined;
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const result = await compileLocalSlQuery(project as KtxLocalProject, {
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connectionId: args.connectionId,
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query,
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compute,
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execute: args.execute,
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maxRows: args.maxRows,
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queryExecutor,
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});
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if (args.format === 'sql') {
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io.stdout.write(`${result.sql}\n`);
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return 0;
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}
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io.stdout.write(`${JSON.stringify(result, null, 2)}\n`);
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return 0;
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}
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const _exhaustive: never = args;
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throw new Error(`Unsupported sl command: ${JSON.stringify(_exhaustive)}`);
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} catch (error) {
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io.stderr.write(`${error instanceof Error ? error.message : String(error)}\n`);
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return 1;
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}
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}
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