Jonathan KirkwoodandClaude Fable 5
1dde915540
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
76 lines
3.3 KiB
TypeScript
76 lines
3.3 KiB
TypeScript
import { setupManifest } from '@start9labs/start-sdk'
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/**
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* Boardroom Map manifest.
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*
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* Like the LLaMA-Factory and Nightshift services, this is a CONTROL PLANE — it
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* does not run any GPU workload itself. It is a small web UI + job runner that
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* SSHes into one or two NVIDIA DGX Sparks to:
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* 1. serve a panel of local LLMs with vLLM (loaded in waves to fit GPU memory),
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* 2. extract text from documents you drop in (PDF/DOCX/TXT/MD — done on the
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* StartOS box), ship it to the Sparks, and launch a panel of sandboxed
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* "reviewer" containers (each a model + a persona) that read the documents
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* and write a report,
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* 3. optionally run a local "lead reviewer" that synthesizes the panel's
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* reports into one consolidated report.
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*
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* There is NO frontier model and NO cloud API key. In the default `airgapped`
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* network mode the reviewer containers can reach ONLY the on-Spark model proxy —
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* the documents and their reviews never touch the internet.
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*
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* NOTE: s9pk.mk extracts the package identifier from the single-quoted value on
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* the line below, so keep that field on one line and avoid stray quotes above it.
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*/
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export const manifest = setupManifest({
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id: 'boardroom-map',
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title: 'Boardroom Map',
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license: 'Apache-2.0',
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packageRepo: 'https://github.com/ten31/boardroom-map',
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upstreamRepo: 'https://github.com/ten31/boardroom-map',
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marketingUrl: 'https://github.com/ten31/boardroom-map',
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donationUrl: null,
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description: {
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short: 'A private panel of local LLMs that reviews your confidential documents on your DGX Sparks',
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long:
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'Boardroom Map lets you drop confidential documents in and convene a panel of ' +
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'local LLMs running on your NVIDIA DGX Sparks to review them. You choose ' +
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'which models and which personas (lenses) sit on the panel and how many ' +
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'reviews to run. Each reviewer reads the documents and writes a report; an ' +
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'optional local lead reviewer synthesizes them into one consolidated ' +
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'report. There is no frontier model and no cloud key: in the default ' +
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'air-gapped mode the reviewers reach only the on-Spark model endpoint, so ' +
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'your documents and their reviews never leave your hardware. No GPU is ' +
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'needed on the StartOS host.',
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},
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// Arch-agnostic orchestrator. Docker build paths are relative to the PROJECT
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// ROOT (where the Makefile runs), matching the Start9 convention.
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images: {
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main: {
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source: {
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dockerBuild: {
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dockerfile: './orchestrator.Dockerfile',
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workdir: '.',
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},
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},
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arch: ['x86_64', 'aarch64'],
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// The orchestrator only SSHes out + extracts document text on CPU; it never
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// touches a local GPU.
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nvidiaContainer: false,
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},
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},
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volumes: ['main'],
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dependencies: {},
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hardwareRequirements: {
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ram: 2048,
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},
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alerts: {
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install:
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'Boardroom Map drives work on REMOTE machines (your DGX Sparks) over SSH; ' +
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'nothing serves or runs on your StartOS server. After install: ' +
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'(1) "Configure Sparks" for SSH access, (2) "Configure Models" for the ' +
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'local models to serve, (3) "Configure Reviewers" for the panel + personas, ' +
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'(4) "Configure Review" for the rubric and air-gap mode. Then drop ' +
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'documents in the inbox and run "Run Review".',
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},
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})
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