Scaffold: fork of Chambers architecture, renamed to Boardroom Map
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
Jonathan Kirkwood
@@ -0,0 +1,113 @@
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import { sdk } from '../sdk'
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import { configFile } from '../file-models/config'
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const { InputSpec, Value, List } = sdk
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const inputSpec = InputSpec.of({
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reviewers: Value.list(
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List.obj(
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{
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name: 'Review Panel',
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description:
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'Who sits on the panel — one entry per review. Add as many as you like. ' +
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'Each reviewer is a model from your catalog plus a PERSONA: the lens it ' +
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'reads through, so the same document gets examined from different angles.',
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default: [],
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minLength: 1,
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maxLength: 32,
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},
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{
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uniqueBy: 'name',
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displayAs: '{{name}} ({{model}})',
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spec: InputSpec.of({
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name: Value.text({
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name: 'Name',
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description: 'Unique reviewer name. Becomes its container and report filename.',
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required: true,
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default: null,
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placeholder: 'risk-counsel',
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patterns: [
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{ regex: '^[A-Za-z0-9][A-Za-z0-9 _-]{0,40}$',
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description: 'Letters, numbers, spaces, dashes, underscores (max 41 chars).' },
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],
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}),
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model: Value.text({
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name: 'Model Alias',
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description: 'Which catalog model this reviewer uses (must match an alias from "Configure Models").',
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required: true,
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default: null,
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placeholder: 'reviewer-a',
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}),
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persona: Value.textarea({
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name: 'Persona / Lens',
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description:
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'How THIS reviewer should read the documents — its priorities and ' +
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'weighting. Injected into its system prompt. e.g. "You are skeptical ' +
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'legal counsel: weight liability, ambiguous obligations, and missing ' +
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'clauses above everything." Leave empty for a neutral reviewer.',
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required: false,
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default: null,
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minRows: 3,
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maxRows: 16,
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placeholder:
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'You are a financial-controls reviewer. Focus on numbers that do not ' +
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'reconcile, unstated assumptions behind projections, and anything that ' +
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'would concern an auditor. Organize findings by severity.',
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}),
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temperature: Value.number({
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name: 'Sampling Temperature (optional)',
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description:
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'Best-effort per-reviewer sampling temperature for extra diversity. ' +
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'Persona is the primary lever. Leave empty to use the model default.',
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required: false,
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default: null,
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integer: false,
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min: 0,
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max: 2,
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}),
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}),
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},
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),
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),
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})
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export const configureReviewers = sdk.Action.withInput(
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'configure-reviewers',
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async ({ effects }) => ({
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name: 'Configure Reviewers',
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description: 'Define the review panel: which models and which personas, and how many reviews.',
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warning: null,
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allowedStatuses: 'any',
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group: null,
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visibility: 'enabled',
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}),
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inputSpec,
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async ({ effects }) => {
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const cfg = await configFile.read().const(effects)
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if (!cfg) return {}
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return { reviewers: cfg.reviewers }
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},
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async ({ effects, input }) => {
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await configFile.merge(effects, { reviewers: input.reviewers })
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return {
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version: '1',
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title: 'Panel Configured',
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message:
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'Saved a panel of ' + input.reviewers.length + ' reviewer(s). Each model ' +
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'alias must exist in "Configure Models". Set the rubric in "Configure ' +
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'Review", then drop documents and run a review.',
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result: {
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type: 'single',
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value: input.reviewers.map((r) => r.name).join(', '),
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copyable: false,
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qr: false,
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masked: false,
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},
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}
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},
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)
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@@ -0,0 +1,132 @@
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import { sdk } from '../sdk'
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import { configFile } from '../file-models/config'
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const { InputSpec, Value } = sdk
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const inputSpec = InputSpec.of({
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reviewInstructions: Value.textarea({
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name: 'Review Rubric',
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description:
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'What every reviewer should look for and produce. Layered above each ' +
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'reviewer\'s persona. Be concrete about the structure you want back.',
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required: true,
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default: null,
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minRows: 5,
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maxRows: 20,
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placeholder:
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'Review the attached document(s). Produce: a short summary, key findings, ' +
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'risks/red flags, open questions, and recommendations. Cite the document and ' +
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'section for each point. Never invent facts not present in the documents.',
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}),
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networkMode: Value.select({
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name: 'Network Mode',
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description:
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'Air-gapped: reviewers reach ONLY the on-Spark model proxy — zero internet, ' +
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'documents never leave your hardware (models must be pre-pulled into the ' +
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'Spark HF cache, all on the head Spark). Local services: reviewers may also ' +
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'reach LAN services like SearXNG and the second Spark (this network has ' +
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'egress unless you firewall it).',
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default: 'airgapped',
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values: {
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airgapped: 'Air-gapped (no network, recommended)',
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local_services: 'Local services (SearXNG / 2nd Spark)',
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},
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}),
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searxngUrl: Value.text({
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name: 'SearXNG URL (local-services only)',
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description: 'JSON-search endpoint to give reviewers a web_search tool. Ignored in air-gapped mode. Empty = no web search.',
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required: false,
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default: null,
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placeholder: 'https://searxng.local',
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}),
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synthesisEnabled: Value.toggle({
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name: 'Synthesize a Consolidated Report',
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description:
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'After the panel finishes, run a local "lead reviewer" that reads all the ' +
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'individual reports and writes one consolidated report (themes, conflicts, ' +
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'consensus, recommendation). No frontier model — stays on the Sparks.',
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default: true,
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}),
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synthesisModel: Value.text({
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name: 'Lead Reviewer Model (optional)',
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description: 'Catalog alias of the model that writes the consolidated report. Empty = use the first model.',
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required: false,
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default: null,
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placeholder: 'reviewer-a',
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}),
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synthesisPersona: Value.textarea({
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name: 'Lead Reviewer Instructions (optional)',
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description: 'Override how the consolidated report is written. Empty = a sensible built-in default.',
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required: false,
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default: null,
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minRows: 3,
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maxRows: 14,
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}),
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wipeRemoteDocs: Value.toggle({
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name: 'Wipe Documents From Sparks After Review',
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description:
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'Delete the extracted document text from the Sparks when a job finishes. ' +
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'Reports are always kept on this StartOS box. Recommended for confidential material.',
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default: true,
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}),
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autoRunOnDrop: Value.toggle({
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name: 'Auto-run When Documents Are Dropped',
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description:
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'Start a review automatically (after a short debounce) whenever new files ' +
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'land in the inbox. Off by default so you trigger reviews explicitly.',
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default: false,
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}),
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})
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export const configureReview = sdk.Action.withInput(
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'configure-review',
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async ({ effects }) => ({
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name: 'Configure Review',
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description: 'Set the rubric, air-gap mode, synthesis, and document retention.',
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warning: null,
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allowedStatuses: 'any',
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group: null,
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visibility: 'enabled',
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}),
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inputSpec,
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async ({ effects }) => {
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const cfg = await configFile.read().const(effects)
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if (!cfg) return {}
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return {
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reviewInstructions: cfg.reviewInstructions,
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networkMode: cfg.networkMode,
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searxngUrl: cfg.searxngUrl || undefined,
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synthesisEnabled: cfg.synthesisEnabled,
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synthesisModel: cfg.synthesisModel || undefined,
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synthesisPersona: cfg.synthesisPersona || undefined,
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wipeRemoteDocs: cfg.wipeRemoteDocs,
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autoRunOnDrop: cfg.autoRunOnDrop,
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}
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},
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async ({ effects, input }) => {
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await configFile.merge(effects, {
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reviewInstructions: input.reviewInstructions,
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networkMode: input.networkMode,
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searxngUrl: input.searxngUrl ?? '',
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synthesisEnabled: input.synthesisEnabled,
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synthesisModel: input.synthesisModel ?? '',
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synthesisPersona: input.synthesisPersona ?? '',
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wipeRemoteDocs: input.wipeRemoteDocs,
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autoRunOnDrop: input.autoRunOnDrop,
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})
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return {
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version: '1',
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title: 'Review Settings Saved',
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message:
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input.networkMode === 'airgapped'
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? 'Saved. Reviewers will run air-gapped (no internet). Ensure all models are on the head Spark and pre-pulled into its HF cache.'
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: 'Saved. Reviewers run in local-services mode and may reach the network — make sure that is acceptable for these documents.',
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result: { type: 'single', value: input.networkMode, copyable: false, qr: false, masked: false },
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}
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},
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)
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@@ -0,0 +1,159 @@
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import { sdk } from '../sdk'
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import { configFile } from '../file-models/config'
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const { InputSpec, Value, List } = sdk
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const inputSpec = InputSpec.of({
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models: Value.list(
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List.obj(
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{
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name: 'Model Catalog',
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description:
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'The local models this service can serve on your Sparks. Each reviewer ' +
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'references one of these by its alias. The job runner loads models in ' +
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'waves so you can run a panel across more models than fit in GPU memory ' +
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'at once.',
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default: [],
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minLength: 1,
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maxLength: 16,
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},
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{
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uniqueBy: 'alias',
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displayAs: '{{alias}} → {{hfModel}}',
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spec: InputSpec.of({
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alias: Value.text({
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name: 'Alias',
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description: 'Short name reviewers use to pick this model (e.g. "qwen-32b").',
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required: true,
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default: null,
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placeholder: 'reviewer-a',
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patterns: [
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{ regex: '^[a-z0-9][a-z0-9-]{0,30}$',
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description: 'Lowercase letters, numbers, dashes (max 31 chars).' },
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],
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}),
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hfModel: Value.text({
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name: 'Hugging Face Model ID',
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description: 'The model vLLM serves. Must be present in the Spark HF cache for air-gapped mode.',
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required: true,
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default: null,
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placeholder: 'Qwen/Qwen3-32B-FP8',
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||||
}),
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spark: Value.select({
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name: 'Served On',
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||||
description:
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||||
'Which Spark serves this model. Air-gapped review mode requires the ' +
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'head (primary) Spark; the secondary is used only in local-services mode.',
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default: 'primary',
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values: { primary: 'Primary (head) Spark', secondary: 'Secondary Spark' },
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}),
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port: Value.number({
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name: 'vLLM Port',
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description: 'Host port the vLLM container for this model listens on. Unique per Spark.',
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||||
required: true,
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||||
default: 8001,
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||||
integer: true,
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||||
min: 1,
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||||
max: 65535,
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||||
}),
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||||
}),
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||||
},
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||||
),
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||||
),
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||||
gpuMemoryUtilization: Value.text({
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||||
name: 'GPU Memory Utilization',
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||||
description: 'vLLM --gpu-memory-utilization (0–1). Lower it if you co-resident multiple models per Spark.',
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required: true,
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||||
default: '0.85',
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}),
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maxModelLen: Value.number({
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||||
name: 'Max Model Length',
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||||
description: 'vLLM --max-model-len (context window). Documents are chunked to fit.',
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||||
required: true,
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||||
default: 32768,
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integer: true,
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||||
min: 2048,
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||||
}),
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toolCallParser: Value.text({
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||||
name: 'Tool-Call Parser',
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||||
description:
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||||
'vLLM tool-call parser for the reviewer\'s read-file tool loop. Match the ' +
|
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'served model family (Qwen3 → "hermes"). Empty disables native tool-calling.',
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required: false,
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||||
default: 'hermes',
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}),
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||||
maxConcurrentModels: Value.number({
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||||
name: 'Max Co-resident Models (head Spark)',
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||||
description:
|
||||
'How many distinct models may load on the head Spark at once. The job runner ' +
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'loads models in waves so it never exceeds this. 1 is safest.',
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||||
required: true,
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||||
default: 1,
|
||||
integer: true,
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||||
min: 1,
|
||||
max: 8,
|
||||
}),
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proxyPort: Value.number({
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||||
name: 'Model Proxy Port',
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||||
description: 'Port for the on-Spark LiteLLM router that exposes every model alias on one endpoint.',
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||||
required: true,
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||||
default: 4000,
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||||
integer: true,
|
||||
min: 1,
|
||||
max: 65535,
|
||||
}),
|
||||
})
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||||
|
||||
export const configureModels = sdk.Action.withInput(
|
||||
'configure-models',
|
||||
|
||||
async ({ effects }) => ({
|
||||
name: 'Configure Models',
|
||||
description: 'Define the local model catalog served on your Sparks and the serving knobs.',
|
||||
warning: null,
|
||||
allowedStatuses: 'any',
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||||
group: null,
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||||
visibility: 'enabled',
|
||||
}),
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inputSpec,
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||||
|
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async ({ effects }) => {
|
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const cfg = await configFile.read().const(effects)
|
||||
if (!cfg) return {}
|
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return {
|
||||
models: cfg.models,
|
||||
gpuMemoryUtilization: cfg.gpuMemoryUtilization,
|
||||
maxModelLen: cfg.maxModelLen,
|
||||
toolCallParser: cfg.toolCallParser,
|
||||
maxConcurrentModels: cfg.maxConcurrentModels,
|
||||
proxyPort: cfg.proxyPort,
|
||||
}
|
||||
},
|
||||
|
||||
async ({ effects, input }) => {
|
||||
await configFile.merge(effects, {
|
||||
models: input.models,
|
||||
gpuMemoryUtilization: input.gpuMemoryUtilization,
|
||||
maxModelLen: input.maxModelLen,
|
||||
toolCallParser: input.toolCallParser ?? '',
|
||||
maxConcurrentModels: input.maxConcurrentModels,
|
||||
proxyPort: input.proxyPort,
|
||||
})
|
||||
|
||||
return {
|
||||
version: '1',
|
||||
title: 'Models Configured',
|
||||
message:
|
||||
'Saved ' + input.models.length + ' model(s). Make sure each is present in ' +
|
||||
'the Spark HF cache for air-gapped runs, then set "Configure Reviewers".',
|
||||
result: {
|
||||
type: 'single',
|
||||
value: input.models.map((m) => m.alias).join(', '),
|
||||
copyable: false,
|
||||
qr: false,
|
||||
masked: false,
|
||||
},
|
||||
}
|
||||
},
|
||||
)
|
||||
@@ -0,0 +1,161 @@
|
||||
import { sdk } from '../sdk'
|
||||
import { configFile } from '../file-models/config'
|
||||
import { sshKeyFile, hfTokenFile } from '../file-models/secrets'
|
||||
|
||||
const { InputSpec, Value } = sdk
|
||||
|
||||
const inputSpec = InputSpec.of({
|
||||
primarySparkHost: Value.text({
|
||||
name: 'Primary Spark Host',
|
||||
description: 'Hostname or IP of the head DGX Spark (reachable over SSH). Serves models, hosts the model proxy, and runs the reviewer panel.',
|
||||
required: true,
|
||||
default: null,
|
||||
placeholder: 'spark-01.local',
|
||||
}),
|
||||
primarySparkUser: Value.text({
|
||||
name: 'SSH User',
|
||||
description: 'The login user on the Spark (DGX OS default is "nvidia").',
|
||||
required: true,
|
||||
default: 'nvidia',
|
||||
}),
|
||||
sshPort: Value.number({
|
||||
name: 'SSH Port',
|
||||
description: 'SSH port on the Spark.',
|
||||
required: true,
|
||||
default: 22,
|
||||
integer: true,
|
||||
min: 1,
|
||||
max: 65535,
|
||||
}),
|
||||
sshPrivateKey: Value.textarea({
|
||||
name: 'SSH Private Key',
|
||||
description:
|
||||
'A private key (PEM/OpenSSH) whose public half is in the Spark user\'s ' +
|
||||
'~/.ssh/authorized_keys. Stored in this service\'s private volume and ' +
|
||||
'used only to reach your Sparks. Paste the FULL key including header/footer.',
|
||||
warning:
|
||||
'This is a credential. It is written to the service volume and never ' +
|
||||
'shown again. Use a dedicated key for this service.',
|
||||
required: true,
|
||||
default: null,
|
||||
minRows: 6,
|
||||
maxRows: 14,
|
||||
placeholder: '-----BEGIN OPENSSH PRIVATE KEY-----\n...\n-----END OPENSSH PRIVATE KEY-----',
|
||||
}),
|
||||
useBothSparks: Value.toggle({
|
||||
name: 'Use Both Sparks',
|
||||
description:
|
||||
'Allow models to be served on a second Spark (over ConnectX/200GbE) for ' +
|
||||
'extra capacity. NOTE: in air-gapped review mode all models must run on the ' +
|
||||
'head Spark; the second Spark is used only in local-services mode.',
|
||||
default: false,
|
||||
}),
|
||||
secondarySparkHost: Value.text({
|
||||
name: 'Secondary Spark Host',
|
||||
description: 'Hostname/IP of the second Spark. Required only if "Use Both Sparks" is on.',
|
||||
required: false,
|
||||
default: null,
|
||||
placeholder: 'spark-02.local',
|
||||
}),
|
||||
headInternalHost: Value.text({
|
||||
name: 'Head Internal Host',
|
||||
description:
|
||||
'Address the head Spark uses for its own preflight checks against the local ' +
|
||||
'model proxy. Single Spark: 127.0.0.1 is fine.',
|
||||
required: true,
|
||||
default: '127.0.0.1',
|
||||
}),
|
||||
remoteWorkDir: Value.text({
|
||||
name: 'Remote Work Directory',
|
||||
description: 'Absolute path on the head Spark for staged document text, the HF cache, and logs.',
|
||||
required: true,
|
||||
default: '/home/nvidia/boardroom-map',
|
||||
}),
|
||||
servingImage: Value.text({
|
||||
name: 'vLLM Image Tag',
|
||||
description: 'The vLLM serving image built on the Sparks (e.g. via spark-vllm-docker).',
|
||||
required: true,
|
||||
default: 'boardroom-vllm:latest',
|
||||
}),
|
||||
graderImage: Value.text({
|
||||
name: 'Reviewer Image Tag',
|
||||
description: 'The sandboxed reviewer image built on the head Spark from sandbox/build.sh.',
|
||||
required: true,
|
||||
default: 'boardroom-grader:latest',
|
||||
}),
|
||||
hfToken: Value.text({
|
||||
name: 'Hugging Face Token (optional)',
|
||||
description:
|
||||
'Only needed for gated/private models or to warm a model the first time. ' +
|
||||
'Leave empty to keep the existing token unchanged. Passed to the vLLM ' +
|
||||
'container at serve time.',
|
||||
required: false,
|
||||
default: null,
|
||||
masked: true,
|
||||
}),
|
||||
})
|
||||
|
||||
export const configureSparks = sdk.Action.withInput(
|
||||
'configure-sparks',
|
||||
|
||||
async ({ effects }) => ({
|
||||
name: 'Configure Sparks',
|
||||
description: 'Set the DGX Spark connection details, SSH credentials, and image tags.',
|
||||
warning: null,
|
||||
allowedStatuses: 'any',
|
||||
group: null,
|
||||
visibility: 'enabled',
|
||||
}),
|
||||
|
||||
inputSpec,
|
||||
|
||||
// Prefill non-secret fields from existing config. Never prefill the key/token.
|
||||
async ({ effects }) => {
|
||||
const cfg = await configFile.read().const(effects)
|
||||
if (!cfg) return {}
|
||||
return {
|
||||
primarySparkHost: cfg.primarySparkHost || undefined,
|
||||
primarySparkUser: cfg.primarySparkUser,
|
||||
sshPort: cfg.sshPort,
|
||||
useBothSparks: cfg.useBothSparks,
|
||||
secondarySparkHost: cfg.secondarySparkHost ?? undefined,
|
||||
headInternalHost: cfg.headInternalHost,
|
||||
remoteWorkDir: cfg.remoteWorkDir,
|
||||
servingImage: cfg.servingImage,
|
||||
graderImage: cfg.graderImage,
|
||||
}
|
||||
},
|
||||
|
||||
async ({ effects, input }) => {
|
||||
// Persist the private key to its own file (600 enforced in-container).
|
||||
await sshKeyFile.write(effects, input.sshPrivateKey.trim() + '\n')
|
||||
|
||||
let hfTokenSet = (await configFile.read().const(effects))?.hfTokenSet ?? false
|
||||
if (input.hfToken && input.hfToken.trim()) {
|
||||
await hfTokenFile.write(effects, input.hfToken.trim())
|
||||
hfTokenSet = true
|
||||
}
|
||||
|
||||
await configFile.merge(effects, {
|
||||
primarySparkHost: input.primarySparkHost,
|
||||
primarySparkUser: input.primarySparkUser,
|
||||
sshPort: input.sshPort,
|
||||
useBothSparks: input.useBothSparks,
|
||||
secondarySparkHost: input.secondarySparkHost,
|
||||
headInternalHost: input.headInternalHost,
|
||||
remoteWorkDir: input.remoteWorkDir,
|
||||
servingImage: input.servingImage,
|
||||
graderImage: input.graderImage,
|
||||
hfTokenSet,
|
||||
})
|
||||
|
||||
return {
|
||||
version: '1',
|
||||
title: 'Sparks Configured',
|
||||
message:
|
||||
'Saved. Use "Test Spark Connection" to verify SSH + GPU access, then set ' +
|
||||
'"Configure Models" and "Configure Reviewers".',
|
||||
result: { type: 'single', value: input.primarySparkHost, copyable: false, qr: false, masked: false },
|
||||
}
|
||||
},
|
||||
)
|
||||
@@ -0,0 +1,59 @@
|
||||
import { startSdk } from '@start9labs/start-sdk'
|
||||
import { sdk } from '../sdk'
|
||||
|
||||
/**
|
||||
* Trigger a review of whatever is currently in the inbox. The running job-runner
|
||||
* thread polls for /data/state/run_request and starts a job when it appears, so
|
||||
* this action just drops that request file (decoupled from the daemon — no need
|
||||
* to reach its HTTP port from the action's one-shot container).
|
||||
*/
|
||||
export const runReview = sdk.Action.withoutInput(
|
||||
'run-review',
|
||||
|
||||
async ({ effects }) => ({
|
||||
name: 'Run Review',
|
||||
description: 'Convene the panel now over the documents currently in the inbox.',
|
||||
warning: null,
|
||||
allowedStatuses: 'only-running',
|
||||
group: null,
|
||||
visibility: 'enabled',
|
||||
}),
|
||||
|
||||
async ({ effects }) => {
|
||||
const mounts = sdk.Mounts.of().mountVolume({
|
||||
volumeId: 'main',
|
||||
mountpoint: '/data',
|
||||
subpath: null,
|
||||
readonly: false,
|
||||
})
|
||||
|
||||
let output: string
|
||||
try {
|
||||
const { stdout } = await startSdk.runCommand<typeof sdk.manifest>(
|
||||
effects,
|
||||
{ imageId: 'main' },
|
||||
[
|
||||
'sh',
|
||||
'-c',
|
||||
'mkdir -p /data/state && date +%s > /data/state/run_request && ' +
|
||||
'n=$(ls -1 /data/inbox 2>/dev/null | wc -l | tr -d " "); ' +
|
||||
'echo "Review requested. $n file(s) in the inbox."',
|
||||
],
|
||||
{ mounts, env: { BM_DATA_DIR: '/data' } },
|
||||
'run-review',
|
||||
)
|
||||
output = (stdout?.toString() || '').trim() || 'Review requested.'
|
||||
} catch (e: any) {
|
||||
output = 'Could not request a review: ' + (e?.message || String(e))
|
||||
}
|
||||
|
||||
return {
|
||||
version: '1',
|
||||
title: 'Review Requested',
|
||||
message:
|
||||
output +
|
||||
' Watch the Web UI for progress; reports appear there and via "View Latest Report".',
|
||||
result: { type: 'single', value: output, copyable: false, qr: false, masked: false },
|
||||
}
|
||||
},
|
||||
)
|
||||
@@ -0,0 +1,17 @@
|
||||
import { sdk } from '../sdk'
|
||||
import { configureSparks } from './configure-sparks'
|
||||
import { configureModels } from './configure-models'
|
||||
import { configureReviewers } from './configure-reviewers'
|
||||
import { configureReview } from './configure-review'
|
||||
import { runReview } from './run-review'
|
||||
import { testConnection } from './test-connection'
|
||||
import { latestReport } from './latest-report'
|
||||
|
||||
export const actions = sdk.Actions.of()
|
||||
.addAction(configureSparks)
|
||||
.addAction(configureModels)
|
||||
.addAction(configureReviewers)
|
||||
.addAction(configureReview)
|
||||
.addAction(runReview)
|
||||
.addAction(testConnection)
|
||||
.addAction(latestReport)
|
||||
@@ -0,0 +1,51 @@
|
||||
import { startSdk } from '@start9labs/start-sdk'
|
||||
import { sdk } from '../sdk'
|
||||
|
||||
/**
|
||||
* Returns the latest report as a copyable result, so you can read it straight
|
||||
* from the StartOS service page without opening the Web UI. The job runner saves
|
||||
* the most recent report (consolidated if synthesis is on, else the panel
|
||||
* digest) to /data/reports/latest.md on the StartOS host — no Spark round-trip.
|
||||
*/
|
||||
export const latestReport = sdk.Action.withoutInput(
|
||||
'latest-report',
|
||||
|
||||
async ({ effects }) => ({
|
||||
name: 'View Latest Report',
|
||||
description: 'Show the most recent review report produced by the panel.',
|
||||
warning: null,
|
||||
allowedStatuses: 'any',
|
||||
group: null,
|
||||
visibility: 'enabled',
|
||||
}),
|
||||
|
||||
async ({ effects }) => {
|
||||
const mounts = sdk.Mounts.of().mountVolume({
|
||||
volumeId: 'main',
|
||||
mountpoint: '/data',
|
||||
subpath: null,
|
||||
readonly: true,
|
||||
})
|
||||
|
||||
let report: string
|
||||
try {
|
||||
const { stdout } = await startSdk.runCommand<typeof sdk.manifest>(
|
||||
effects,
|
||||
{ imageId: 'main' },
|
||||
['sh', '-c', 'cat /data/reports/latest.md 2>/dev/null || echo "(no report yet — drop documents in the inbox and run a review)"'],
|
||||
{ mounts, env: { BM_DATA_DIR: '/data' } },
|
||||
'latest-report',
|
||||
)
|
||||
report = (stdout?.toString() || '').trim() || '(no report yet)'
|
||||
} catch (e: any) {
|
||||
report = 'Could not read report: ' + (e?.message || String(e))
|
||||
}
|
||||
|
||||
return {
|
||||
version: '1',
|
||||
title: 'Latest Boardroom Map Report',
|
||||
message: 'The panel\'s most recent review.',
|
||||
result: { type: 'single', value: report, copyable: true, qr: false, masked: false },
|
||||
}
|
||||
},
|
||||
)
|
||||
@@ -0,0 +1,55 @@
|
||||
import { startSdk } from '@start9labs/start-sdk'
|
||||
import { sdk } from '../sdk'
|
||||
|
||||
/**
|
||||
* Runs a one-shot in the orchestrator image that SSHes into the configured
|
||||
* Spark(s) and reports `nvidia-smi` plus whether the vLLM + reviewer images are
|
||||
* built. Reuses orchestrator/spark_client.py so SSH logic lives in one place.
|
||||
*/
|
||||
export const testConnection = sdk.Action.withoutInput(
|
||||
'test-connection',
|
||||
|
||||
async ({ effects }) => ({
|
||||
name: 'Test Spark Connection',
|
||||
description: 'SSH into the configured Spark(s) and verify GPU + serving/reviewer image access.',
|
||||
warning: null,
|
||||
allowedStatuses: 'any',
|
||||
group: null,
|
||||
visibility: 'enabled',
|
||||
}),
|
||||
|
||||
async ({ effects }) => {
|
||||
const mounts = sdk.Mounts.of().mountVolume({
|
||||
volumeId: 'main',
|
||||
mountpoint: '/data',
|
||||
subpath: null,
|
||||
readonly: true,
|
||||
})
|
||||
|
||||
let output: string
|
||||
try {
|
||||
const { stdout, stderr } = await startSdk.runCommand<typeof sdk.manifest>(
|
||||
effects,
|
||||
{ imageId: 'main' },
|
||||
['python3', '/app/spark_client.py', 'test'],
|
||||
{ mounts, env: { BM_DATA_DIR: '/data' } },
|
||||
'spark-test',
|
||||
)
|
||||
output =
|
||||
(stdout?.toString() || '').trim() +
|
||||
(stderr?.toString().trim() ? '\n\n[stderr]\n' + stderr.toString().trim() : '')
|
||||
} catch (e: any) {
|
||||
output =
|
||||
'Connection test failed.\n\n' +
|
||||
(e?.stdout?.toString() || '') +
|
||||
(e?.stderr?.toString() || e?.message || String(e))
|
||||
}
|
||||
|
||||
return {
|
||||
version: '1',
|
||||
title: 'Spark Connection Test',
|
||||
message: 'Result of probing your Spark(s) over SSH.',
|
||||
result: { type: 'single', value: output || '(no output)', copyable: true, qr: false, masked: false },
|
||||
}
|
||||
},
|
||||
)
|
||||
@@ -0,0 +1,132 @@
|
||||
import { FileHelper, z } from '@start9labs/start-sdk'
|
||||
|
||||
/**
|
||||
* Boardroom Map configuration, persisted to the `main` volume as config.json.
|
||||
*
|
||||
* Written by the StartOS actions (Configure Sparks / Models / Reviewers /
|
||||
* Review) and read by the Python orchestrator inside the container, which mounts
|
||||
* the same volume at /data and reads /data/config.json. Keep field names in sync
|
||||
* with orchestrator/bm_config.py (CONFIG_DEFAULTS).
|
||||
*
|
||||
* Boardroom Map is a CONTROL PLANE: a GPU-free orchestrator on StartOS that SSHes into
|
||||
* one or two DGX Sparks to serve local models and run a panel of sandboxed
|
||||
* "reviewer" containers over confidential documents you drop in. There is NO
|
||||
* frontier model and NO cloud key — everything stays on your hardware. The only
|
||||
* secrets are the Spark SSH key and an optional Hugging Face token (secrets.ts).
|
||||
*/
|
||||
export const configShape = z.object({
|
||||
// --- Spark connection (mirrors LLaMA-Factory / Nightshift) ---
|
||||
primarySparkHost: z.string().default(''),
|
||||
primarySparkUser: z.string().default('nvidia'),
|
||||
sshPort: z.number().int().positive().default(22),
|
||||
// Second Spark for extra model capacity. null = single node.
|
||||
secondarySparkHost: z.string().nullable().default(null),
|
||||
useBothSparks: z.boolean().default(false),
|
||||
// Address the head Spark's own preflight checks use to reach the local proxy.
|
||||
headInternalHost: z.string().default('127.0.0.1'),
|
||||
|
||||
// --- Remote execution ---
|
||||
remoteWorkDir: z.string().default('/home/nvidia/boardroom-map'),
|
||||
|
||||
// --- Images (built ON the Sparks; not packed into the s9pk) ---
|
||||
servingImage: z.string().default('boardroom-vllm:latest'),
|
||||
graderImage: z.string().default('boardroom-grader:latest'),
|
||||
|
||||
// --- Serving (vLLM on the Sparks) ---
|
||||
gpuMemoryUtilization: z.string().default('0.85'),
|
||||
maxModelLen: z.number().int().positive().default(32768),
|
||||
// vLLM tool-call parser for native function-calling (the reviewer's read-file
|
||||
// tool loop relies on it). Must match the served model family — Qwen3 →
|
||||
// 'hermes'. Empty disables native tool-calling (reviewers fall back to a
|
||||
// JSON-action text protocol).
|
||||
toolCallParser: z.string().default('hermes'),
|
||||
// LiteLLM router exposing every model alias on one OpenAI-compatible endpoint.
|
||||
proxyPort: z.number().int().positive().default(4000),
|
||||
// How many distinct models may be co-resident on the HEAD Spark at once. The
|
||||
// job runner loads models in WAVES so it never exceeds this — letting you run a
|
||||
// panel across more models than fit in GPU memory simultaneously. 1 is safest.
|
||||
maxConcurrentModels: z.number().int().positive().default(1),
|
||||
|
||||
// The MODEL CATALOG: the set of local models the service can serve. Each
|
||||
// reviewer (below) references one of these by `alias`. Mirrors
|
||||
// bm_config.py CONFIG_DEFAULTS["models"].
|
||||
models: z
|
||||
.array(
|
||||
z.object({
|
||||
alias: z.string(),
|
||||
hfModel: z.string(),
|
||||
// Which Spark serves this model. In `airgapped` network mode all models
|
||||
// must be on the head Spark (see networkMode).
|
||||
spark: z.enum(['primary', 'secondary']).default('primary'),
|
||||
port: z.number().int().positive().default(8001),
|
||||
}),
|
||||
)
|
||||
.default([
|
||||
{ alias: 'reviewer-a', hfModel: 'Qwen/Qwen3-32B-FP8', spark: 'primary', port: 8001 },
|
||||
]),
|
||||
|
||||
// --- The review panel: one entry per reviewer ("number of reviews") ---
|
||||
// Each reviewer is a model + a persona (the lens it reads through) + an
|
||||
// optional temperature. Mirrors bm_config.py CONFIG_DEFAULTS["reviewers"].
|
||||
reviewers: z
|
||||
.array(
|
||||
z.object({
|
||||
name: z.string(),
|
||||
// Must match one of the model catalog aliases above.
|
||||
model: z.string(),
|
||||
persona: z.string().nullable().default(''),
|
||||
temperature: z.number().nullable().default(null),
|
||||
}),
|
||||
)
|
||||
.default([
|
||||
{ name: 'reviewer-1', model: 'reviewer-a', persona: '', temperature: null },
|
||||
]),
|
||||
|
||||
// --- Review job settings ---
|
||||
// The rubric: what every reviewer should look for / produce. Layered above
|
||||
// each reviewer's persona.
|
||||
reviewInstructions: z.string().default(
|
||||
'Review the attached document(s). Produce a structured report: a 3-5 sentence ' +
|
||||
'summary, the key findings and insights, risks or red flags, open questions, ' +
|
||||
'and concrete recommendations. Cite the document and section for each point. ' +
|
||||
'Be honest about uncertainty; never invent facts not present in the documents.',
|
||||
),
|
||||
// Confidentiality posture for the reviewer containers:
|
||||
// 'airgapped' — reviewers join an --internal Docker network: they can
|
||||
// reach ONLY the on-Spark model proxy, with zero internet
|
||||
// egress. Models must be pre-pulled into the Spark's HF
|
||||
// cache (no live download). All models must be on the head
|
||||
// Spark. Strongest confidentiality.
|
||||
// 'local_services' — reviewers may also reach configured LAN services
|
||||
// (e.g. SearXNG) and the second Spark. NOTE: this network
|
||||
// has egress unless you firewall it — use only when you
|
||||
// accept that reviewers can reach the network.
|
||||
networkMode: z.enum(['airgapped', 'local_services']).default('airgapped'),
|
||||
// SearXNG JSON endpoint, used ONLY in local_services mode to give reviewers a
|
||||
// web_search tool. Empty = no web search.
|
||||
searxngUrl: z.string().default(''),
|
||||
|
||||
// --- Synthesis (a local lead reviewer; no frontier model) ---
|
||||
synthesisEnabled: z.boolean().default(true),
|
||||
// Alias of the model that writes the consolidated report. Empty = first model.
|
||||
synthesisModel: z.string().default(''),
|
||||
// Optional persona/instructions for the lead reviewer. Empty = built-in default.
|
||||
synthesisPersona: z.string().default(''),
|
||||
|
||||
// --- Document handling ---
|
||||
// After a job, wipe the extracted document text from the Sparks. Reports are
|
||||
// kept on the StartOS box regardless. Default true for confidentiality.
|
||||
wipeRemoteDocs: z.boolean().default(true),
|
||||
// Watch /data/inbox and auto-start a review when files land (debounced).
|
||||
// Default false: you trigger reviews explicitly with "Run Review".
|
||||
autoRunOnDrop: z.boolean().default(false),
|
||||
// Name of the per-job Docker network created on the head Spark.
|
||||
networkName: z.string().default('boardroom-net'),
|
||||
|
||||
// --- Auth flags (the secret itself lives in secrets.ts) ---
|
||||
hfTokenSet: z.boolean().default(false),
|
||||
})
|
||||
|
||||
export type Config = z.infer<typeof configShape>
|
||||
|
||||
export const configFile = FileHelper.json('./config.json', configShape)
|
||||
@@ -0,0 +1,24 @@
|
||||
import { FileHelper } from '@start9labs/start-sdk'
|
||||
|
||||
/**
|
||||
* SSH private key used to reach the Sparks, stored as a standalone file in the
|
||||
* `main` volume. Kept out of config.json so it is never returned in plaintext
|
||||
* config reads. The container copies it to a 600 path at runtime.
|
||||
*
|
||||
* Volume path './ssh/id_spark' -> /data/ssh/id_spark inside the container.
|
||||
*/
|
||||
export const sshKeyFile = FileHelper.string('./ssh/id_spark')
|
||||
|
||||
/**
|
||||
* Optional Hugging Face token (for gated/private model pulls on the Sparks).
|
||||
* Passed to the vLLM serving container as HF_TOKEN at launch. In `airgapped`
|
||||
* network mode models are served from a pre-pulled cache, so this is only used
|
||||
* the first time you warm a model (or in local_services mode).
|
||||
*
|
||||
* Volume path './secrets/hf_token' -> /data/secrets/hf_token.
|
||||
*
|
||||
* NOTE: Boardroom Map has NO frontier/cloud key. There is deliberately no Anthropic
|
||||
* key and no Gitea token — the whole point is that confidential documents and
|
||||
* their reviews never leave your hardware.
|
||||
*/
|
||||
export const hfTokenFile = FileHelper.string('./secrets/hf_token')
|
||||
@@ -0,0 +1,24 @@
|
||||
import { buildManifest } from '@start9labs/start-sdk'
|
||||
import { sdk } from './sdk'
|
||||
import { versions } from './versions'
|
||||
import { actions } from './actions'
|
||||
import { setInterfaces } from './interfaces'
|
||||
import { manifest as sdkManifest } from './manifest'
|
||||
|
||||
// Required ABI exports for a StartOS service package. The PUBLISHABLE manifest is
|
||||
// the static manifest combined with version-graph metadata (version, release
|
||||
// notes, migration ranges) — start-cli reads this `manifest` export, and it must
|
||||
// include `version`, which buildManifest() supplies from the VersionGraph.
|
||||
export const manifest = buildManifest(versions, sdkManifest)
|
||||
export { main } from './main'
|
||||
export { actions } from './actions'
|
||||
|
||||
// Back up the whole volume (config, ssh key, optional HF token, inbox, reports).
|
||||
export const { createBackup, restoreInit } = sdk.setupBackups(async () =>
|
||||
sdk.Backups.ofVolumes('main'),
|
||||
)
|
||||
|
||||
// init composes: version migrations, action registration, interface export,
|
||||
// and backup restore.
|
||||
export const init = sdk.setupInit(versions, actions, setInterfaces, restoreInit)
|
||||
export const uninit = sdk.setupUninit(versions)
|
||||
@@ -0,0 +1,26 @@
|
||||
import { sdk } from './sdk'
|
||||
|
||||
export const WEB_UI_PORT = 8080
|
||||
|
||||
/**
|
||||
* Expose the orchestrator web UI as a StartOS interface (Tor + LAN), so the
|
||||
* user can open the Boardroom Map control panel from the StartOS dashboard.
|
||||
*/
|
||||
export const setInterfaces = sdk.setupInterfaces(async ({ effects }) => {
|
||||
const multi = sdk.MultiHost.of(effects, 'web')
|
||||
const origin = await multi.bindPort(WEB_UI_PORT, { protocol: 'http' })
|
||||
const ui = sdk.createInterface(effects, {
|
||||
name: 'Web UI',
|
||||
id: 'webui',
|
||||
description:
|
||||
'The Boardroom Map control panel: drop documents in, convene the reviewer ' +
|
||||
'panel, watch the job run on your Sparks, and read the reports.',
|
||||
type: 'ui',
|
||||
username: null,
|
||||
path: '',
|
||||
query: {},
|
||||
schemeOverride: null,
|
||||
masked: false,
|
||||
})
|
||||
return [await origin.export([ui])]
|
||||
})
|
||||
@@ -0,0 +1,49 @@
|
||||
import { sdk } from './sdk'
|
||||
import { WEB_UI_PORT } from './interfaces'
|
||||
|
||||
export const main = sdk.setupMain(async ({ effects }) => {
|
||||
// Mount the persistent volume at /data: config.json, ssh key, optional HF
|
||||
// token, the dropped-document inbox, job run state, and saved reports all live
|
||||
// here.
|
||||
const mounts = sdk.Mounts.of().mountVolume({
|
||||
volumeId: 'main',
|
||||
mountpoint: '/data',
|
||||
subpath: null,
|
||||
readonly: false,
|
||||
})
|
||||
|
||||
const sub = await sdk.SubContainer.of(
|
||||
effects,
|
||||
{ imageId: 'main' },
|
||||
mounts,
|
||||
'boardroom-webui',
|
||||
)
|
||||
|
||||
// The web UI runs the FastAPI app AND, in a background thread, the Boardroom Map job
|
||||
// runner (which extracts dropped documents, serves the chosen models on the
|
||||
// Sparks in waves, runs the reviewer panel, and synthesizes a report).
|
||||
return sdk.Daemons.of(effects).addDaemon('webui', {
|
||||
subcontainer: sub,
|
||||
exec: {
|
||||
command: [
|
||||
'uvicorn',
|
||||
'app:app',
|
||||
'--host',
|
||||
'0.0.0.0',
|
||||
'--port',
|
||||
String(WEB_UI_PORT),
|
||||
],
|
||||
cwd: '/app',
|
||||
env: { BM_DATA_DIR: '/data' },
|
||||
},
|
||||
ready: {
|
||||
display: 'Web Interface',
|
||||
fn: () =>
|
||||
sdk.healthCheck.checkPortListening(effects, WEB_UI_PORT, {
|
||||
successMessage: 'The control panel is ready',
|
||||
errorMessage: 'The control panel is not yet listening',
|
||||
}),
|
||||
},
|
||||
requires: [],
|
||||
})
|
||||
})
|
||||
@@ -0,0 +1,75 @@
|
||||
import { setupManifest } from '@start9labs/start-sdk'
|
||||
|
||||
/**
|
||||
* Boardroom Map manifest.
|
||||
*
|
||||
* Like the LLaMA-Factory and Nightshift services, this is a CONTROL PLANE — it
|
||||
* does not run any GPU workload itself. It is a small web UI + job runner that
|
||||
* SSHes into one or two NVIDIA DGX Sparks to:
|
||||
* 1. serve a panel of local LLMs with vLLM (loaded in waves to fit GPU memory),
|
||||
* 2. extract text from documents you drop in (PDF/DOCX/TXT/MD — done on the
|
||||
* StartOS box), ship it to the Sparks, and launch a panel of sandboxed
|
||||
* "reviewer" containers (each a model + a persona) that read the documents
|
||||
* and write a report,
|
||||
* 3. optionally run a local "lead reviewer" that synthesizes the panel's
|
||||
* reports into one consolidated report.
|
||||
*
|
||||
* There is NO frontier model and NO cloud API key. In the default `airgapped`
|
||||
* network mode the reviewer containers can reach ONLY the on-Spark model proxy —
|
||||
* the documents and their reviews never touch the internet.
|
||||
*
|
||||
* NOTE: s9pk.mk extracts the package identifier from the single-quoted value on
|
||||
* the line below, so keep that field on one line and avoid stray quotes above it.
|
||||
*/
|
||||
export const manifest = setupManifest({
|
||||
id: 'boardroom-map',
|
||||
title: 'Boardroom Map',
|
||||
license: 'Apache-2.0',
|
||||
packageRepo: 'https://github.com/ten31/boardroom-map',
|
||||
upstreamRepo: 'https://github.com/ten31/boardroom-map',
|
||||
marketingUrl: 'https://github.com/ten31/boardroom-map',
|
||||
donationUrl: null,
|
||||
description: {
|
||||
short: 'A private panel of local LLMs that reviews your confidential documents on your DGX Sparks',
|
||||
long:
|
||||
'Boardroom Map lets you drop confidential documents in and convene a panel of ' +
|
||||
'local LLMs running on your NVIDIA DGX Sparks to review them. You choose ' +
|
||||
'which models and which personas (lenses) sit on the panel and how many ' +
|
||||
'reviews to run. Each reviewer reads the documents and writes a report; an ' +
|
||||
'optional local lead reviewer synthesizes them into one consolidated ' +
|
||||
'report. There is no frontier model and no cloud key: in the default ' +
|
||||
'air-gapped mode the reviewers reach only the on-Spark model endpoint, so ' +
|
||||
'your documents and their reviews never leave your hardware. No GPU is ' +
|
||||
'needed on the StartOS host.',
|
||||
},
|
||||
// Arch-agnostic orchestrator. Docker build paths are relative to the PROJECT
|
||||
// ROOT (where the Makefile runs), matching the Start9 convention.
|
||||
images: {
|
||||
main: {
|
||||
source: {
|
||||
dockerBuild: {
|
||||
dockerfile: './orchestrator.Dockerfile',
|
||||
workdir: '.',
|
||||
},
|
||||
},
|
||||
arch: ['x86_64', 'aarch64'],
|
||||
// The orchestrator only SSHes out + extracts document text on CPU; it never
|
||||
// touches a local GPU.
|
||||
nvidiaContainer: false,
|
||||
},
|
||||
},
|
||||
volumes: ['main'],
|
||||
dependencies: {},
|
||||
hardwareRequirements: {
|
||||
ram: 2048,
|
||||
},
|
||||
alerts: {
|
||||
install:
|
||||
'Boardroom Map drives work on REMOTE machines (your DGX Sparks) over SSH; ' +
|
||||
'nothing serves or runs on your StartOS server. After install: ' +
|
||||
'(1) "Configure Sparks" for SSH access, (2) "Configure Models" for the ' +
|
||||
'local models to serve, (3) "Configure Reviewers" for the panel + personas, ' +
|
||||
'(4) "Configure Review" for the rubric and air-gap mode. Then drop ' +
|
||||
'documents in the inbox and run "Run Review".',
|
||||
},
|
||||
})
|
||||
@@ -0,0 +1,8 @@
|
||||
import { StartSdk } from '@start9labs/start-sdk'
|
||||
import { manifest } from './manifest'
|
||||
|
||||
/**
|
||||
* The bound SDK facade. Import `sdk` everywhere else to reach actions, daemons,
|
||||
* interfaces, health checks, file helpers, and the input-form builders.
|
||||
*/
|
||||
export const sdk = StartSdk.of().withManifest(manifest).build(true)
|
||||
@@ -0,0 +1,8 @@
|
||||
import { VersionGraph } from '@start9labs/start-sdk'
|
||||
import { v_0_1_0 } from './v_0_1_0'
|
||||
|
||||
/** The current version MUST be the first argument (`current`). */
|
||||
export const versions = VersionGraph.of({
|
||||
current: v_0_1_0,
|
||||
other: [],
|
||||
})
|
||||
@@ -0,0 +1,16 @@
|
||||
import { VersionInfo } from '@start9labs/start-sdk'
|
||||
|
||||
/**
|
||||
* Initial release. ExVer form `<upstream>:<downstream>` — we track our own
|
||||
* packaging revision since Boardroom Map has no separate upstream semver.
|
||||
*/
|
||||
export const v_0_1_0 = VersionInfo.of({
|
||||
version: '0.1.0:0',
|
||||
releaseNotes:
|
||||
'Initial release: drop confidential documents in and convene a panel of ' +
|
||||
'local LLMs on your DGX Sparks to review them. Choose the models, the ' +
|
||||
'personas, and how many reviews; an optional local lead reviewer ' +
|
||||
'synthesizes a consolidated report. Default air-gapped mode keeps documents ' +
|
||||
'and reviews entirely on your hardware.',
|
||||
migrations: {},
|
||||
})
|
||||
Reference in New Issue
Block a user