Worldshaper/docs/kb
2026-06-27 00:18:41 -04:00
..
systems Run request analysis on demand on the VPS 2026-06-27 00:18:41 -04:00
queue-workflow.md Run request analysis on demand on the VPS 2026-06-27 00:18:41 -04:00
README.md Run request analysis on demand on the VPS 2026-06-27 00:18:41 -04:00
request-analysis-schema.json Run request analysis on demand on the VPS 2026-06-27 00:18:41 -04:00
systems.json Run request analysis on demand on the VPS 2026-06-27 00:18:41 -04:00

Worldshaper KB Scaffold

This folder is the first pass of an internal knowledge base for Worldshaper.

It is meant to support three jobs:

  1. Human onboarding.
  2. Faster request triage.
  3. Grounded local-LLM analysis for the Requests queue.

The goal is to give request-processing tools stable system summaries instead of asking a model to infer editor behavior from raw user text alone.

Layout

  • systems.json
    • Machine-readable system index.
  • request-analysis-schema.json
    • JSON schema for structured request parsing output.
  • queue-workflow.md
    • First-pass automation and review flow.
  • systems/*.md
    • Human-readable system notes, organized by editor subsystem.

Current System Coverage

The scaffold currently covers:

  • Launcher home and tabbed board
  • Requests intake and request state handling
  • Studio bootstrap and world loading
  • Chunk storage and neighborhood streaming
  • Layers and tile editing
  • Graphics Painter
  • Rendering and viewport behavior
  • World Overview
  • Persistence and save pipeline
  • Floating windows and popout shell
  • Content catalog and record APIs

How To Use This For Request Analysis

Suggested queue flow:

  1. Pull a raw request submission from the launcher request store.
  2. Retrieve likely systems from systems.json using tags, aliases, and file names.
  3. Provide the matching systems/*.md files to the local model as context.
  4. Ask the model to split the submission into atomic requests.
  5. Require output that matches request-analysis-schema.json.
  6. Validate the JSON before storing anything back into the app.
  7. Auto-promote only high-confidence items. Keep low-confidence items in review.
  • Add concrete file-level notes for major controllers as they evolve.
  • Add a small extractor script that refreshes endpoint lists from server.js.
  • Add examples of well-tagged historical requests for few-shot prompting.
  • Add confidence thresholds and retry rules for malformed model output.

Current Automation Entry Point

Use the queue worker with:

npm run analyze:requests

The worker expects:

  • the Worldshaper API server to be running
  • a model endpoint and API key
  • KB files from this folder

DeepSeek Setup

The worker now supports DeepSeek directly.

Recommended environment:

$env:DEEPSEEK_API_KEY="your-key-here"

Defaults when DEEPSEEK_API_KEY is present:

  • provider: deepseek
  • base URL: https://api.deepseek.com
  • model: deepseek-v4-flash
  • JSON mode: enabled
  • thinking: disabled

You can override the model if you want:

$env:REQUEST_ANALYZER_MODEL="deepseek-v4-pro"

Suggested Review Rules

  • If the model produces multiple atomic requests from one submission, keep the original submission id on every derived item.
  • If a request touches multiple systems, prefer one primary category and many tags.
  • If the request is really a bug report, preserve the problem statement and write the implementation note as a likely fix path, not a promise.
  • If the model cannot confidently map a request, store it as needs_review instead of forcing a category.

Notes

  • This scaffold is grounded to the repo layout on 2026-06-26.
  • It is intentionally incomplete. Treat it as a maintained map, not generated truth.