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fix(snowflake): unblock multi-schema ingest and relationship discovery (#204)
* feat(setup): drop redundant Snowflake schema prompt; fall back to free-text on listSchemas failure Snowflake setup previously asked for a single schema as free text, then ran a multiselect against the discovered schemas — two schema questions back-to-back, with the first being only a session bootstrap. The SDK's `schema` is optional, so the bootstrap step is unnecessary. - Remove the free-text Snowflake schema prompt; only pass `schema` to snowflake-sdk when one is configured. - When `listSchemas()` fails (e.g. role lacks SHOW SCHEMAS), prompt the user for a comma-separated list, persist it as `schema_names`, and use it as both the table-list filter and the multiselect default. Applies to every driver with a scope-discovery spec, not just Snowflake. - Update docs to lead with `schema_names`; keep `schema_name` as a documented single-schema shorthand. * fix(snowflake): keep introspecting when primary-key discovery is denied The PK query joins INFORMATION_SCHEMA.TABLE_CONSTRAINTS and INFORMATION_SCHEMA.KEY_COLUMN_USAGE, which require grants the connection role may not have. Previously a 'SQL compilation error: Object ANALYTICS.INFORMATION_SCHEMA.KEY_COLUMN_USAGE does not exist or not authorized' aborted the entire introspect — schemas, columns, and row counts were all discarded over a missing nice-to-have. Wrap the constraint query in try/catch, log a one-line warning per schema, and return an empty PK map. Columns end up with primaryKey=false; relationship inference still has FK and profiling to fall back on. * fix(scan): unblock relationship discovery on Snowflake Two adjacent bugs prevented the scan's relationship pipeline from producing any joins on a Snowflake warehouse: - relationship-profiling.ts fell through to a default `GROUP_CONCAT` branch for unknown drivers. Snowflake has no GROUP_CONCAT, so every per-table profile query failed with "Unknown function GROUP_CONCAT". Add an explicit Snowflake branch that uses LISTAGG with a literal '\x1f' delimiter (Snowflake requires the delimiter to be a constant, so CHR(31) is rejected). - description-generation.ts destructured `connector.sampleTable` and `connector.sampleColumn` into bare locals, losing the `this` binding when the class-method connectors (Snowflake, Postgres, MySQL) were invoked. Every sample call threw "Cannot read properties of undefined (reading 'assertConnection')" and degraded LLM descriptions to metadata-only prompts. Call the methods through the connector instead. Without these, even after the primary-key probe is allowed to fail softly, the scan ends up with 0 validated relationships and an empty `joins:` block in every shard YAML. * test(scan): cover table-ref helpers * feat(scan): plumb tableScope through live-database introspection port * feat(scan): apply tableScope during metadata fetch * feat(scan): enforce table scope at fetch boundary * feat(scan): pool Snowflake sessions and batch enrichment for faster ingest (#206) * feat(cli): add RSA key-pair auth option to Snowflake setup wizard Extends the interactive Snowflake setup flow with an authentication-method prompt (password vs RSA/JWT key-pair). The RSA branch collects a private-key path (env/file/absolute) and an optional passphrase; the resulting connection config records `authMethod: 'rsa'` with `privateKey` and `passphrase` instead of `password`. * feat(scan): pool Snowflake sessions * fix(scan): reuse structural snapshots and cleanup connectors * feat(scan): parallelize relationship profiling * feat(scan): batch table description generation * docs: document Snowflake ingest concurrency knobs * fix(scan): close Snowflake ingest perf verification gaps * fix(scan): keep batched description failure bounded * feat(scan): dispatch query-history probes by connection driver Extract historic-sql dialect resolution into a shared helper so the status-project readiness check and the local ingest factory agree on which connections enable query history and which probe to run. The status command now picks the postgres/snowflake/bigquery probe based on the connection's driver instead of always reporting against postgres, which previously caused snowflake connections with queryHistory.enabled to surface a misleading "driver is snowflake" failure. Also drops a noisy console.warn from Snowflake primary-key discovery — INFORMATION_SCHEMA.KEY_COLUMN_USAGE is commonly ungranted for read-only roles and the FK + profiling paths handle the empty PK map already. * fix(llm): allow StructuredOutput tool and raise maxTurns for generateObject The Claude Code agent SDK announces an internal pseudo-tool named StructuredOutput in the system/init message whenever outputFormat is set to { type: 'json_schema' }. The runtime's isolation check built its allowedToolIds set only from MCP tool ids and treated StructuredOutput as an unexpected host-injected tool, so every generateObject call threw "Claude Code runtime isolation failed: tools=StructuredOutput ..." and the table-descriptions and relationship-LLM-proposal enrichment stages recorded null output across the board. Whitelist StructuredOutput specifically in generateObject's allowedToolIds — the check also enforces missing_tools symmetry, so generateText and runAgentLoop, which do not see StructuredOutput, must not require it. generateObject also ran with maxTurns: 1, which the model intermittently breached when it emitted thinking text before the structured response. Raised to 5 to give the schema-bound call enough headroom without allowing unbounded loops. The existing tests now exercise the path with an init message that announces StructuredOutput so the regression cannot slip back in. * chore(scripts): add ktx-reset.sh project-cleanup helper Convenience script for repeatable ingest testing: takes a project directory and prunes everything except ktx.yaml and .ktx/secrets/, so the next ktx setup or ktx ingest run starts from a known-clean state.
This commit is contained in:
Andrey Avtomonov
GitHub
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72 changed files with 3508 additions and 655 deletions
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@ -378,6 +378,121 @@ describe('KtxDescriptionGenerator', () => {
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expect(cache.set).toHaveBeenCalledWith('warehouse.public.orders', 'Commerce orders');
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expect(cache.set).toHaveBeenCalledWith('__connection:Warehouse', 'Commerce orders');
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});
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it('generates one structured table description and reuses table samples for all columns', async () => {
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const llmRuntime = createLlmProvider('unused');
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llmRuntime.generateObject = vi.fn(async () => ({
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tableDescription: 'Commerce orders',
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columns: [
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{ name: 'status', description: 'Current order state' },
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{ name: 'amount', description: 'Order amount in dollars' },
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],
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}));
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const connector = createConnector();
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const generator = new KtxDescriptionGenerator({
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llmRuntime,
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settings: { columnMaxWords: 12, tableMaxWords: 18, dataSourceMaxWords: 24 },
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});
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const result = await generator.generateBatchedTableDescriptions({
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connectionId: 'conn-1',
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connector,
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context: { runId: 'run-1' },
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dataSourceType: 'POSTGRESQL',
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supportsNestedAnalysis: false,
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table: {
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catalog: null,
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db: 'public',
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name: 'orders',
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rawDescriptions: { db: 'Orders fact table' },
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columns: [
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{ name: 'status', type: 'text' },
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{ name: 'amount', type: 'numeric' },
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],
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},
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});
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expect(result.tableDescription).toBe('Commerce orders');
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expect(Object.fromEntries(result.columnDescriptions)).toEqual({
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status: 'Current order state',
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amount: 'Order amount in dollars',
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});
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expect(connector.sampleTable).toHaveBeenCalledTimes(1);
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expect(connector.sampleColumn).not.toHaveBeenCalled();
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expect(llmRuntime.generateObject).toHaveBeenCalledTimes(1);
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expect(llmRuntime.generateText).not.toHaveBeenCalled();
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});
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it('falls back to one column generateText call for each missing structured column', async () => {
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const llmRuntime = createLlmProvider('Fallback status');
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llmRuntime.generateObject = vi.fn(async () => ({
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tableDescription: 'Commerce orders',
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columns: [{ name: 'amount', description: 'Order amount in dollars' }],
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}));
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const connector = createConnector();
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const generator = new KtxDescriptionGenerator({
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llmRuntime,
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settings: { columnMaxWords: 12, tableMaxWords: 18, dataSourceMaxWords: 24 },
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});
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const result = await generator.generateBatchedTableDescriptions({
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connectionId: 'conn-1',
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connector,
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context: { runId: 'run-1' },
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dataSourceType: 'POSTGRESQL',
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supportsNestedAnalysis: false,
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table: {
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catalog: null,
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db: 'public',
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name: 'orders',
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columns: [
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{ name: 'status', type: 'text' },
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{ name: 'amount', type: 'numeric' },
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],
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},
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});
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expect(Object.fromEntries(result.columnDescriptions)).toEqual({
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status: 'Fallback status',
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amount: 'Order amount in dollars',
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});
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expect(connector.sampleColumn).not.toHaveBeenCalled();
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expect(llmRuntime.generateObject).toHaveBeenCalledTimes(1);
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expect(llmRuntime.generateText).toHaveBeenCalledTimes(1);
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});
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it('does not run per-column fallback when structured object generation throws', async () => {
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const llmRuntime = createLlmProvider('Fallback description');
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llmRuntime.generateObject = vi.fn(async () => {
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throw new Error('object output unavailable');
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});
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const warnings: string[] = [];
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const generator = new KtxDescriptionGenerator({
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llmRuntime,
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onWarning: (warning) => warnings.push(warning.code),
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settings: { columnMaxWords: 12, tableMaxWords: 18, dataSourceMaxWords: 24 },
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});
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const result = await generator.generateBatchedTableDescriptions({
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connectionId: 'conn-1',
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connector: createConnector(),
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context: { runId: 'run-1' },
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dataSourceType: 'POSTGRESQL',
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supportsNestedAnalysis: false,
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table: {
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catalog: null,
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db: 'public',
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name: 'orders',
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columns: [{ name: 'status', type: 'text' }],
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},
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});
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expect(result.tableDescription).toBeNull();
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expect(Object.fromEntries(result.columnDescriptions)).toEqual({ status: null });
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expect(warnings).toContain('enrichment_failed');
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expect(llmRuntime.generateObject).toHaveBeenCalledTimes(1);
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expect(llmRuntime.generateText).not.toHaveBeenCalled();
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});
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});
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describe('KtxDescriptionGenerator resilience', () => {
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