Files
windmill/ai_evals/adapters/frontend/mockBackend.ts
centdix 4296a6ae1f feat(ai-chat): cap read_app_file + search_app grep tool to bound context in large raw apps (#9653)
* docs: add global AI chat context-optimization plan for raw apps

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* test(ai-evals): add global raw-app debugging cases on a large fixture

Adds a ~20-file analytics_dashboard raw-app fixture (incl. a 5k-line data module
and a planted wrong-totals bug), two global cases (read-heavy debug + small-edit
baseline), app-seed support in the mock backend, directory-fixture loading, and a
decorateHelpers seam so read-dedupe is measurable. Records tokenUsage for before/
after comparison of the read-tool optimization.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(ai-chat): cap and dedupe read_app_file to bound context in large apps

read_app_file now defaults to a head slice (1500 lines / 50k chars) with offset/
limit to page further, and skips resending a file whose earlier read is still in
context (per-conversation ledger keyed off the originating tool-call id, so it
self-heals after compaction). Bounds the file-content portion of global-chat
context when working in large raw apps.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* test(ai-evals): add read-heavy raw-app debug case (large data module)

global-test31 induces the model to inspect the 5k-line seedData module, exercising
the read_app_file cap/offset path. Baseline ~262k tokens vs ~200k with the cap+dedupe
change (-24%).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* docs: record A+B benchmark results and fixed-overhead finding

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* fix(ai-chat): clearer read_app_file past-EOF message + unit tests for cap/dedupe

Addresses local-review nits: out-of-range offset now reports 'offset N is past the
end of the file' instead of a backwards 'lines 11-10' label; adds unit coverage for
the slicing (line cap, offset/limit window, char budget, past-EOF) and re-read dedupe
(hit + miss-when-not-retained).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(ai-chat): char-level paging + per-range dedupe for read_app_file

Adds char_offset/char_limit so minified/long-line files can be paged within a line
window, keys the re-read ledger by range (so reading different ranges no longer
collides), and dedupes on the full-file hash (a cached range stub is invalidated
when any byte of the file changes, not just the returned range). Tests updated for
the char-slice behavior plus single-line capping, char paging, and out-of-window
change detection.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* test(ai-chat): add read_app_file context micro-benchmark + re-read eval case

Adds a deterministic micro-benchmark (no LLM) that drives read_app_file through a
realistic big-project read pattern (large file, re-read, minified bundle, paging)
and asserts the cap+dedupe cut returned context >50% vs the old whole-file behavior
— isolating the feature's effect from model nondeterminism and guarding against
silent weakening. Adds global-test32, a cross-file consistency investigation that
revisits overlapping files so re-read dedupe is exercised in a real run.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* test(ai-evals): clarify test32 measures the read cap, not dedupe

Verified: sonnet and haiku both read each file once per conversation and retain
it, so test32 never triggers read_app_file re-read dedupe. Dedupe is measured
deterministically by the micro-benchmark instead. Comment corrected to match.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(ai-chat): drop read_app_file re-read dedupe, ship the cap only

Benchmarking showed the per-conversation re-read dedupe never fires in practice:
across sonnet/opus/gpt-5.5/haiku, every model reads each file once per conversation
and keeps it in context (0 within-conversation re-reads). It was a correct but unused
guard, so this removes the ledger, full-file hash, retention predicate, the
AIChatManager wiring, and the eval decorateHelpers seam — keeping the read cap +
offset/limit/char paging (A), which is the lever that actually bounds context. The
micro-benchmark is now cap-only; test32 is kept as a multi-file read-load case.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(ai-chat): add search_app grep tool for global raw-app chat (experimental)

Client-side grep over a raw app's frontend files and inline runnables (literal,
case-insensitive, optional file_glob/context_lines/max_matches, head-capped).
Completes the list -> search -> ranged-read triad. Includes the eval A/B gate
(WMILL_AI_EVAL_DISABLE_SEARCH_APP), unit tests + micro-benchmark, and a
find-all-usages eval case (global-test33).

Experimental: A/B benchmarking shows it is not an unconditional win — it helps
on find-all-usages but adds agentic iterations on navigable apps.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* test(ai-evals): accept search_app as a valid file-inspection tool in raw-app cases

Add requiredToolsAnyOf alternatives-group to ToolValidationSpec and switch
global-test29..32 to it so a model that locates files via search_app instead
of read_app_file no longer false-fails the tool assertion.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* docs: remove stale ai-chat context-optimization planning doc

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(ai-chat): drop read_app_file char paging for a hard char cap

The char_offset/char_limit params guarded minified files (a single line over
the char budget) but were effectively unused in benchmarks. Remove them and the
in-window char paging; keep the hard 50k-char budget and, when a read hits it,
tell the model to narrow the line limit (or treat the file as unreadable if a
single line exceeds the budget). Proper long-line handling is left as a TODO.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(ai-chat): bake search_app context to 1 line, clarify query is literal

Drop the context_lines param (models varied it to little effect) for a fixed
SEARCH_APP_CONTEXT_LINES=1, and cap on matching lines instead of pushed rows so
max_matches stays accurate with context always on. Sharpen the query description
to state it is a literal (non-regex) substring and to suggest the call form
(e.g. formatCurrency() to hit call sites and skip formatCurrencyPrecise.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(ai-chat): widen baked search_app context to 2 lines

Models that set the old context_lines param leaned to 2; match the lean.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* fix(ai-chat): count every file with a match in search_app header

Move fileHadMatch ahead of the render cap so files whose matches fall past max_matches are still counted (with a regression test). Also swap the raw NUL globstar sentinel for a printable escape (the NUL bytes made core.ts read as binary to grep) and reword two comments to describe current constraints instead of drafting history.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* fix(ai-chat): drop redundant input echoes from app tool results

read_app_file and search_app no longer prefix results with the tool name or echo back the caller's own inputs (file path, query, file_glob) — the model already has them from the call args, and the unbounded query echo could push the search result past its output budget. Keeps the useful signals (line range, match/file counts, truncation) and the actionable advice. Also reword max_matches to 'matching lines' since it caps lines (each expands to context rows). Unit tests updated to the new format.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-19 13:33:54 +00:00

675 lines
20 KiB
TypeScript

import { randomUUID } from 'node:crypto'
import type {
AppWithLastVersion,
CompletedJob,
Flow,
Job,
ListableApp,
Script
} from '../../../frontend/src/lib/gen'
import type {
DataTableTables,
DataTableTableSchema,
GetDraftForUserResponse,
ListDraftsResponse,
ScriptLang,
UpdateDraftResponse,
UserDraftItemKind
} from '../../../frontend/src/lib/gen/types.gen'
import { buildScriptLintResult } from './core/script/preview'
import { applyDatatableSql, type BenchmarkDatatableSeed } from './datatableSqlEngine'
export type { BenchmarkDatatableSeed, BenchmarkDatatableTableSeed } from './datatableSqlEngine'
const BENCHMARK_TIMESTAMP = '1970-01-01T00:00:00.000Z'
export interface BenchmarkWorkspaceScript {
path: string
summary: string
description?: string
language: Script['language']
schema?: Record<string, unknown>
content: string
}
export interface BenchmarkWorkspaceFlow {
path: string
summary: string
description?: string
schema?: Record<string, unknown>
value: Flow['value']
}
export interface BenchmarkWorkspaceApp {
path: string
summary: string
value: {
files: Record<string, string>
runnables: Record<string, unknown>
data?: unknown
policy?: unknown
custom_path?: unknown
}
}
export interface BenchmarkWorkspaceJob {
/** Stable id so a case prompt can reference a specific run (e.g. for get_job_logs). */
id?: string
jobKind?: CompletedJob['job_kind']
scriptPath?: string
createdBy?: string
label?: string
success?: boolean
logs?: string
}
export interface BenchmarkWorkspaceRunnables {
scripts?: BenchmarkWorkspaceScript[]
flows?: BenchmarkWorkspaceFlow[]
apps?: BenchmarkWorkspaceApp[]
datatables?: BenchmarkDatatableSeed[]
jobs?: BenchmarkWorkspaceJob[]
}
type BenchmarkCompletedJob = CompletedJob & { type: 'CompletedJob' }
const benchmarkWorkspaces = new Set<string>()
const benchmarkWorkspaceRunnables = new Map<string, BenchmarkWorkspaceRunnables>()
// Keyed by `${workspace}::${jobId}` so concurrent attempts (or distinct cases)
// can seed the same fixed job id without clobbering each other's entry.
const benchmarkJobs = new Map<string, { workspace: string; job: BenchmarkCompletedJob }>()
function benchmarkJobKey(workspace: string, jobId: string): string {
return `${workspace}::${jobId}`
}
export function resetBenchmarkMockBackend(): void {
benchmarkWorkspaces.clear()
benchmarkWorkspaceRunnables.clear()
benchmarkJobs.clear()
benchmarkDrafts.clear()
}
export function registerBenchmarkWorkspace(workspace: string): void {
benchmarkWorkspaces.add(workspace)
}
export function registerBenchmarkWorkspaceRunnables(
workspace: string,
runnables: BenchmarkWorkspaceRunnables
): void {
benchmarkWorkspaces.add(workspace)
// Fresh case: drop any drafts left from a prior run on this workspace id.
clearBenchmarkDrafts(workspace)
// Datatables are mutated in place by exec_datatable_sql (a write must be visible
// to later reads), so store an isolated deep copy — never mutate the caller's seed.
benchmarkWorkspaceRunnables.set(workspace, {
...runnables,
datatables: runnables.datatables ? structuredClone(runnables.datatables) : undefined
})
// Seed any fixture jobs so list_runs / get_job_logs have data to return.
for (const seed of runnables.jobs ?? []) {
createBenchmarkCompletedJob({
workspace,
id: seed.id,
jobKind: seed.jobKind ?? 'script',
success: seed.success,
scriptPath: seed.scriptPath,
createdBy: seed.createdBy,
label: seed.label,
logs: seed.logs
})
}
}
export function unregisterBenchmarkWorkspace(workspace: string): void {
benchmarkWorkspaces.delete(workspace)
benchmarkWorkspaceRunnables.delete(workspace)
clearBenchmarkDrafts(workspace)
for (const [jobId, entry] of benchmarkJobs.entries()) {
if (entry.workspace === workspace) {
benchmarkJobs.delete(jobId)
}
}
}
export function unregisterBenchmarkWorkspaceRunnables(workspace: string): void {
unregisterBenchmarkWorkspace(workspace)
}
export function hasBenchmarkWorkspace(workspace: string): boolean {
return benchmarkWorkspaces.has(workspace)
}
export function listBenchmarkScripts(workspace: string): Script[] | null {
const runnables = benchmarkWorkspaceRunnables.get(workspace)
if (!runnables) {
return null
}
return (runnables.scripts ?? []).map(buildBenchmarkScript)
}
export function listBenchmarkFlows(workspace: string): Flow[] | null {
const runnables = benchmarkWorkspaceRunnables.get(workspace)
if (!runnables) {
return null
}
return (runnables.flows ?? []).map(buildBenchmarkFlow)
}
export function getBenchmarkScriptByPath(workspace: string, path: string): Script | null {
const script = benchmarkWorkspaceRunnables
.get(workspace)
?.scripts?.find((entry) => entry.path === path)
return script ? buildBenchmarkScript(script) : null
}
export function getBenchmarkScriptByHash(workspace: string, hash: string): Script | null {
const script = benchmarkWorkspaceRunnables
.get(workspace)
?.scripts?.find((entry) => buildBenchmarkScriptHash(entry.path) === hash)
return script ? buildBenchmarkScript(script) : null
}
export function getBenchmarkFlowByPath(workspace: string, path: string): Flow | null {
const flow = benchmarkWorkspaceRunnables
.get(workspace)
?.flows?.find((entry) => entry.path === path)
return flow ? buildBenchmarkFlow(flow) : null
}
export function listBenchmarkApps(workspace: string): ListableApp[] | null {
const runnables = benchmarkWorkspaceRunnables.get(workspace)
if (!runnables) {
return null
}
return (runnables.apps ?? []).map(buildBenchmarkListableApp)
}
export function getBenchmarkAppByPath(workspace: string, path: string): AppWithLastVersion | null {
const app = benchmarkWorkspaceRunnables
.get(workspace)
?.apps?.find((entry) => entry.path === path)
return app ? buildBenchmarkApp(app) : null
}
export function createBenchmarkCompletedJob(input: {
workspace: string
jobKind: CompletedJob['job_kind']
success?: boolean
result?: unknown
logs?: string
scriptPath?: string
scriptHash?: string
args?: Record<string, unknown>
id?: string
createdBy?: string
label?: string
}): string {
const jobId = input.id ?? `benchmark-job-${randomUUID()}`
const now = new Date().toISOString()
const job: BenchmarkCompletedJob = {
type: 'CompletedJob',
id: jobId,
workspace_id: input.workspace,
created_by: input.createdBy ?? 'ai-evals',
created_at: now,
started_at: now,
completed_at: now,
duration_ms: 0,
success: input.success ?? true,
script_path: input.scriptPath,
script_hash: input.scriptHash,
args: input.args,
result: input.result,
logs: input.logs,
canceled: false,
job_kind: input.jobKind,
permissioned_as: 'u/ai-evals',
is_flow_step: false,
is_skipped: false,
email: 'ai-evals@local',
visible_to_owner: true,
tag: 'benchmark',
labels: input.label ? [input.label] : undefined
}
benchmarkJobs.set(benchmarkJobKey(input.workspace, jobId), { workspace: input.workspace, job })
return jobId
}
export function getBenchmarkCompletedJob(
workspace: string,
jobId: string
): BenchmarkCompletedJob | null {
const entry = benchmarkJobs.get(benchmarkJobKey(workspace, jobId))
if (!entry) {
return null
}
return structuredClone(entry.job)
}
/**
* List seeded/recorded jobs for a benchmark workspace, most recent first —
* the shape `JobService.listJobs` returns. Returns `null` for a non-benchmark
* workspace so the caller can fall through to the real backend. Server-side
* filters (path/creator/status/limit) are intentionally not applied: global
* eval cases assert on the recorded `list_runs` tool call, not on filtering.
*/
export function listBenchmarkJobs(workspace: string): Job[] | null {
if (!hasBenchmarkWorkspace(workspace)) {
return null
}
return [...benchmarkJobs.values()]
.filter((entry) => entry.workspace === workspace)
.map((entry) => structuredClone(entry.job) as Job)
.sort((a, b) => (b.created_at ?? '').localeCompare(a.created_at ?? ''))
}
/**
* Mirror `JobService.getJobLogs` (response is the raw log string). Throws a
* "not found" error for an unknown id, matching the backend 404.
*/
export function getBenchmarkJobLogs(workspace: string, jobId: string): string {
const job = getBenchmarkCompletedJob(workspace, jobId)
if (!job) {
throw new Error(`Job Logs not found for "${jobId}"`)
}
return job.logs ?? ''
}
// ============= Drafts (per-user, DB-backed in production) =============
/**
* In-memory stand-in for the per-user draft backend (`DraftService`). The global
* AI chat now persists and reads drafts through the backend DB instead of an
* in-tab `UserDraft` cell, so the eval mocks the three draft endpoints it
* exercises (`updateDraft` / `getDraftForUser` / `listDrafts`) and keeps the
* saved values here, keyed by workspace + draft kind + storage path. Mirrors the
* semantics of the production unit test's mock in
* `frontend/src/lib/components/copilot/chat/global/core.test.ts`.
*/
const benchmarkDrafts = new Map<
string,
{ workspace: string; kind: UserDraftItemKind; path: string; value: unknown }
>()
// Fixed timestamp so artifacts stay deterministic. No eval simulates a
// concurrent writer, so every save is accepted and the conflict branch is
// never taken — the syncer just records this as its `last_sync` baseline.
const BENCHMARK_DRAFT_TIMESTAMP = '1970-01-01T00:00:00.000Z'
function benchmarkDraftKey(workspace: string, kind: string, path: string): string {
return `${workspace}::${kind}::${path}`
}
export function clearBenchmarkDrafts(workspace: string): void {
for (const [key, entry] of benchmarkDrafts.entries()) {
if (entry.workspace === workspace) {
benchmarkDrafts.delete(key)
}
}
}
/**
* Seed a draft straight into the store — used by the eval's live-editor draft
* fixtures, which model "the user already has this draft open/saved". Writing it
* here (instead of through `UserDraft.save`) keeps it a backend draft row with no
* shadowing in-tab cell, so a model edit that persists to the backend is what the
* output read-back captures — not the stale seed.
*/
export function seedBenchmarkDraft(
workspace: string,
kind: UserDraftItemKind,
path: string,
value: unknown
): void {
benchmarkDrafts.set(benchmarkDraftKey(workspace, kind, path), {
workspace,
kind,
path,
value
})
}
/** Mirror `DraftService.updateDraft`: a `null`/omitted value deletes the row. */
export function updateBenchmarkDraft(input: {
workspace: string
kind: UserDraftItemKind
path: string
requestBody?: { value?: unknown }
}): UpdateDraftResponse {
const key = benchmarkDraftKey(input.workspace, input.kind, input.path)
const value = input.requestBody?.value
if (value == null) {
benchmarkDrafts.delete(key)
} else {
benchmarkDrafts.set(key, {
workspace: input.workspace,
kind: input.kind,
path: input.path,
value
})
}
return { status: 'saved', current_timestamp: BENCHMARK_DRAFT_TIMESTAMP }
}
/** Mirror `DraftService.getDraftForUser`: 404-shaped throw when absent so the
* adapter's narrowed catch treats it as "no draft" instead of re-throwing. */
export function getBenchmarkDraftForUser(input: {
workspace: string
kind: UserDraftItemKind
path: string
}): GetDraftForUserResponse {
const entry = benchmarkDrafts.get(benchmarkDraftKey(input.workspace, input.kind, input.path))
if (!entry) {
throw Object.assign(new Error(`no draft for "${input.path}"`), { status: 404 })
}
return { value: entry.value, created_at: BENCHMARK_DRAFT_TIMESTAMP }
}
/** Mirror `DraftService.listDrafts`: metadata rows (no value) for a workspace. */
export function listBenchmarkDrafts(workspace: string): ListDraftsResponse {
return [...benchmarkDrafts.values()]
.filter((entry) => entry.workspace === workspace)
.map((entry) => ({
kind: entry.kind,
path: entry.path,
summary: (entry.value as { summary?: string } | null)?.summary,
draft_only: true,
legacy_draft: false,
created_at: BENCHMARK_DRAFT_TIMESTAMP
}))
}
// ============= Datatables (best-effort in-memory SQL) =============
/**
* Project the seeded datatables down to the `list_datatable_tables` response:
* `datatable_name` + `schema -> table_names`, with no column detail.
* Returns `null` for a non-benchmark workspace so callers can fall through to
* the real backend; an empty seed yields `[]`.
*/
export function listBenchmarkDatatables(workspace: string): DataTableTables[] | null {
const runnables = benchmarkWorkspaceRunnables.get(workspace)
if (!runnables) {
return null
}
return (runnables.datatables ?? []).map((datatable) => ({
datatable_name: datatable.datatable_name,
schemas: Object.fromEntries(
Object.entries(datatable.schemas).map(([schema, tables]) => [schema, Object.keys(tables)])
)
}))
}
export function getBenchmarkDatatableSchema(input: {
workspace: string
datatableName: string
schemaName: string
tableName: string
}): DataTableTableSchema {
const runnables = benchmarkWorkspaceRunnables.get(input.workspace)
const datatable = (runnables?.datatables ?? []).find(
(entry) => entry.datatable_name === input.datatableName
)
if (!datatable) {
// Message MUST match the production `isDatatableNotConfiguredError` regex
// (/datatable\s+\S+\s+not found/i in datatableTools.ts) so the
// get_datatable_table_schema not-configured mapping is actually exercised.
throw new Error(`datatable "${input.datatableName}" not found`)
}
const table = datatable.schemas?.[input.schemaName]?.[input.tableName]
if (!table) {
throw new Error(
`table "${input.schemaName}.${input.tableName}" not found in datatable "${input.datatableName}"`
)
}
return {
datatable_name: input.datatableName,
schema_name: input.schemaName,
table_name: input.tableName,
columns: table.columns
}
}
/**
* Execute SQL against a seeded datatable through the best-effort in-memory engine
* (`applyDatatableSql`). Writes (CREATE/INSERT/UPDATE/DELETE/DROP) mutate the
* stored datatable in place so a later list/schema/SELECT reflects them; SELECT
* (and RETURNING) yield rows, other statements yield `[]`. Creates a benchmark
* completed job and returns its id, like `runBenchmarkScriptPreview`.
*/
export function runBenchmarkDatatableSql(input: {
workspace: string
datatableName: string
sql: string
}): string {
const runnables = benchmarkWorkspaceRunnables.get(input.workspace)
const datatable = (runnables?.datatables ?? []).find(
(entry) => entry.datatable_name === input.datatableName
)
const rows = datatable ? applyDatatableSql(datatable, input.sql).rows : []
return createBenchmarkCompletedJob({
workspace: input.workspace,
jobKind: 'preview',
success: true,
args: { database: `datatable://${input.datatableName}` },
result: rows
})
}
/**
* Mirror `JobService.getCompletedJobResultMaybe` for benchmark workspaces — the
* shape `pollJobResult` consumes. The job is created synchronously before
* polling, so it is always present and completed.
*/
export function getBenchmarkCompletedJobResultMaybe(input: {
workspace: string
id: string
}): { success: boolean; completed: boolean; result: unknown } {
const job = getBenchmarkCompletedJob(input.workspace, input.id)
if (!job) {
throw new Error(`Job "${input.id}" not found in benchmark workspace`)
}
return { success: job.success, completed: true, result: job.result }
}
export function runBenchmarkScriptPreview(input: {
workspace: string
requestBody: {
content?: string
language?: ScriptLang | 'bunnative'
args?: Record<string, unknown>
path?: string
}
}): string {
const content = input.requestBody.content ?? ''
const language = input.requestBody.language ?? 'bun'
const lintResult = buildScriptLintResult(content, language)
const success = lintResult.errorCount === 0
return createBenchmarkCompletedJob({
workspace: input.workspace,
jobKind: 'preview',
success,
scriptPath: input.requestBody.path,
args: input.requestBody.args,
result: success
? {
path: input.requestBody.path,
args: input.requestBody.args ?? {},
validated: true
}
: {
path: input.requestBody.path,
args: input.requestBody.args ?? {},
errorCount: lintResult.errorCount,
errors: lintResult.errors.map((entry) => ({
line: entry.startLineNumber,
message: entry.message
}))
}
})
}
export function runBenchmarkFlowByPath(input: {
workspace: string
path: string
args?: Record<string, unknown>
}): string {
const flow = getBenchmarkFlowByPath(input.workspace, input.path)
return createBenchmarkCompletedJob({
workspace: input.workspace,
jobKind: 'flowpreview',
success: flow !== null,
args: input.args,
result:
flow !== null
? {
path: input.path,
args: input.args ?? {},
mocked: true
}
: {
error: `Flow "${input.path}" not found in benchmark workspace`
},
logs:
flow !== null
? 'Mock benchmark flow run completed successfully.'
: `Flow "${input.path}" not found in benchmark workspace.`
})
}
export function previewBenchmarkSchedule(input: {
requestBody?: Record<string, unknown>
}): Record<string, unknown> {
const schedule = input.requestBody?.schedule
if (typeof schedule !== 'string' || schedule.trim().split(/\s+/).length !== 6) {
throw new Error(`schedule must use a six-field cron expression, got ${JSON.stringify(schedule)}`)
}
return {
next_runs: ['1970-01-02T00:00:00.000Z']
}
}
export function createBenchmarkSchedule(input: {
workspace: string
requestBody: Record<string, unknown>
}): Record<string, unknown> {
assertBenchmarkWorkspacePath('schedule', input.requestBody.path)
assertBenchmarkWorkspacePath('target', input.requestBody.script_path)
return {
path: input.requestBody.path,
target_path: input.requestBody.script_path,
is_flow: input.requestBody.is_flow,
mocked: true
}
}
export function createBenchmarkHttpTrigger(input: {
workspace: string
requestBody: Record<string, unknown>
}): Record<string, unknown> {
assertBenchmarkWorkspacePath('trigger', input.requestBody.path)
assertBenchmarkWorkspacePath('target', input.requestBody.script_path)
if (
typeof input.requestBody.route_path === 'string' &&
input.requestBody.route_path.startsWith('/')
) {
throw new Error(`HTTP trigger route_path must not start with /, got "${input.requestBody.route_path}"`)
}
return {
path: input.requestBody.path,
target_path: input.requestBody.script_path,
route_path: input.requestBody.route_path,
is_flow: input.requestBody.is_flow,
mocked: true
}
}
function assertBenchmarkWorkspacePath(label: string, value: unknown): void {
if (typeof value !== 'string' || (!value.startsWith('f/') && !value.startsWith('u/'))) {
throw new Error(`${label} path must start with f/ or u/, got ${JSON.stringify(value)}`)
}
}
function buildBenchmarkScriptHash(path: string): string {
return `benchmark:${path}`
}
function buildBenchmarkScript(script: BenchmarkWorkspaceScript): Script {
return {
workspace_id: 'benchmark',
hash: buildBenchmarkScriptHash(script.path),
path: script.path,
parent_hashes: [],
summary: script.summary,
description: script.description ?? '',
content: script.content,
created_by: 'benchmark',
created_at: BENCHMARK_TIMESTAMP,
archived: false,
schema: script.schema ?? {},
deleted: false,
is_template: false,
extra_perms: {},
language: script.language,
kind: 'script',
starred: false,
has_preprocessor: false,
modules: null
}
}
function buildBenchmarkFlow(flow: BenchmarkWorkspaceFlow): Flow {
return {
path: flow.path,
summary: flow.summary,
description: flow.description ?? '',
value: flow.value,
schema: flow.schema ?? {},
edited_by: 'benchmark',
edited_at: BENCHMARK_TIMESTAMP,
archived: false,
extra_perms: {}
} as Flow
}
function buildBenchmarkListableApp(app: BenchmarkWorkspaceApp): ListableApp {
return {
id: 0,
workspace_id: 'benchmark',
path: app.path,
summary: app.summary,
version: 1,
extra_perms: {},
edited_at: BENCHMARK_TIMESTAMP,
execution_mode: 'viewer',
raw_app: true
}
}
function buildBenchmarkApp(app: BenchmarkWorkspaceApp): AppWithLastVersion {
return {
id: 0,
workspace_id: 'benchmark',
path: app.path,
summary: app.summary,
versions: [1],
created_by: 'benchmark',
created_at: BENCHMARK_TIMESTAMP,
value: app.value,
policy: (app.value.policy ?? {}) as AppWithLastVersion['policy'],
execution_mode: 'viewer',
extra_perms: {},
custom_path: app.value.custom_path as string | undefined,
raw_app: true
}
}