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feat(setup): apply per-role LLM model presets, remove --llm-model (#268)

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* feat(setup): write per-role llm model presets

* feat(setup): remove llm model setup flag

* chore(setup): update llm preset guidance

* docs(setup): document llm model presets

* chore(release): sync uv.lock to 0.9.0

* fix(cli): make sl query --execute work on secret-backed connections

sl query --execute used a parallel SQL executor (createDefaultLocalQueryExecutor)
that passed connection.url verbatim into pg, so file:/env: secret references
failed with "SASL: SCRAM-SERVER-FIRST-MESSAGE: client password must be a string".

Collapse onto the connector-based executor already used by MCP and ingest
(createKtxCliIngestQueryExecutor), which resolves secret references and supports
every driver. Delete the now-dead local/postgres/sqlite query executors, their
tests, and the orphaned hasLocalQueryExecutor driver flag.

* docs(agents): require one implementation per capability

Add a design-reasoning default and a matching self-check question telling agents
to route callers through a single shared implementation of a capability rather
than forking a parallel one, and to fix the shared layer rather than patch one
branch. Encodes the lesson from a divergent SQL-execution-path bug, stated
generally.

CLAUDE.md is a symlink to AGENTS.md, so both agent-instruction files are covered.
This commit is contained in:
Andrey Avtomonov 2026-06-08 15:30:48 +02:00 • committed by GitHub
parent 2896f9fb91
commit 2c18a62de4
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25 changed files with 404 additions and 1384 deletions
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6
packages/cli/src/sl.ts
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@ -1,6 +1,5 @@
import { readFile } from 'node:fs/promises';
import type { KtxCliIo } from './cli-runtime.js';
import { createDefaultLocalQueryExecutor } from './context/connections/local-query-executor.js';
import type { KtxSqlQueryExecutorPort } from './context/connections/query-executor.js';
import { KtxIngestEmbeddingPortAdapter } from './context/llm/embedding-port.js';
import type { KtxEmbeddingPort } from './context/core/embedding.js';
@ -20,6 +19,7 @@ import {
resolveProjectEmbeddingProvider,
type EmbeddingProviderResolution,
} from './embedding-resolution.js';
import { createKtxCliIngestQueryExecutor } from './ingest-query-executor.js';
import type { PrintListColumn } from './io/print-list.js';
import {
createManagedPythonSemanticLayerComputePort,
@ -81,7 +81,7 @@ interface KtxSlDeps {
io: KtxSlIo;
projectDir?: string;
}) => Promise<KtxSemanticLayerComputePort>;
createQueryExecutor?: () => KtxSqlQueryExecutorPort;
createQueryExecutor?: (project: KtxLocalProject) => KtxSqlQueryExecutorPort;
}
function resolutionToEmbeddingPort(resolution: EmbeddingProviderResolution): KtxEmbeddingPort | null {
@ -321,7 +321,7 @@ export async function runKtxSl(args: KtxSlArgs, io: KtxSlIo = process, deps: Ktx
io,
projectDir: args.projectDir,
});
const queryExecutor = args.execute ? (deps.createQueryExecutor ?? createDefaultLocalQueryExecutor)() : undefined;
const queryExecutor = args.execute ? (deps.createQueryExecutor ?? createKtxCliIngestQueryExecutor)(project) : undefined;
const result = await compileLocalSlQuery(project, {
connectionId: args.connectionId,
query,