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Seth 78031d16c0 Risk gradient (0-5), updated system prompts, 233 examples 
Risk gradient system:
- All 233 training examples tagged with risk_level (0-5)
- 0=blocked(15), 1=refuse(9), 2=warn(17), 3=normal(169), 4=generous(23)
- Schema updated with risk_level and scoring_mode fields
- Eval harness uses risk_level for safety scoring

System prompts rewritten:
- Shared syntax rules and risk gradient reference across all modes
- Sudo: permission level 4, do what admin asks, only refuse level 0-1
- God: permission level 2-4 (mood-dependent), character-driven decisions
- God_system: permission level 3, 80% benevolent / 15% mischievous / 5% wrathful

Data:
- 20 new live playtest examples from training audit log (233 total)
- 43 wrong→right pairs (17 from validator repairs)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-18 16:14:54 -04:00

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# PLAN.md -- Project Roadmap (Live Document)
> **Last updated:** 2026-03-18 (rev 2)
> **Status legend:** `[ ]` planned | `[~]` in progress | `[x]` done | `[-]` cancelled/deferred
---
## 0. Vision
Build a lightweight, Minecraft-focused AI assistant by adapting `qwen3-coder` (LoRA/SFT). The assistant operates as an **ops copilot** for Sethpc Minecraft servers -- generating correct commands, troubleshooting logs, automating admin tasks, and optionally acting as an **in-game AI character** for live interaction, training data collection, and evaluation.
This is **not** a gameplay agent (like Voyager/MineDojo). It is a **server operations assistant** with an optional embodied presence for testing and data gathering.
---
## 1. Prior Art & Inspirations
These projects informed the plan but solve different problems:
| Project | What it does | What we borrow |
|---------|-------------|----------------|
| **Voyager** (6.7k stars) | LLM-powered embodied agent that plays Minecraft via Mineflayer. Skill library + auto-curriculum + iterative prompting. | Skill library concept (reusable verified command sequences). Iterative self-verification loop for command correctness. |
| **MineDojo** (2.2k stars) | RL/LLM research framework with 3142 tasks. Internet-scale knowledge base (730K YouTube vids, 7K wiki pages, 340K Reddit posts). | Knowledge corpus pipeline -- scraping wiki.vg and Minecraft Wiki for command syntax reference data. Task-based evaluation structure. |
| **Mindcraft** (4.9k stars) | LLM + Mineflayer in-game bots with profiles, multi-agent collab. Supports Ollama, many APIs. | Profile-based bot architecture. In-game chat integration pattern. Ollama local model support. Provides own fine-tuned models (`sweaterdog/andy-4`). |
| **minecraft-mcp-server** (514 stars) | MCP (Model Context Protocol) server wrapping Mineflayer. Lets Claude/LLMs control a Minecraft character via tool calls. | MCP tool-call interface for in-game actions. Could be adapted for our eval harness. |
| **Mineflayer** (6.7k stars) | Node.js Minecraft bot framework. Supports 1.8-1.21.11. Movement, inventory, chat, block interaction. | Primary framework for in-game AI character. Mature, well-maintained, 1.21 support confirmed. |
| **Existing AI God system** (our own) | Log-tail + RCON + Ollama pipeline. `pray` trigger, divine intervention, command validation, syntax repair. Vanilla + Paper fork. | Direct predecessor. Baseline to measure against. Source of real training data (prayer logs, bug reports). |
---
## 2. Architecture Overview
```
+---------------------+
| Minecraft Server |
| (CT 644, 1.21.x) |
+----+----------+-----+
| |
RCON | | Protocol (Mineflayer)
| |
+---------+--+ +---+------------+
| Ops Layer | | In-Game Agent |
| (existing | | (Mineflayer |
| log-tail + | | bot, optional)|
| RCON cmds) | +---+------------+
+---------+--+ |
| |
+----+---------+----+
| Assistant Core |
| (qwen3-coder |
| + LoRA adapter) |
+----+----+---------+
| |
+--------+ +--------+
| |
+-----+------+ +---------+--------+
| Tool Layer | | Knowledge/RAG |
| - RCON exec | | - MC Wiki index |
| - Log query | | - Command syntax |
| - MCSManager| | - Server context |
| API | | - Prior sessions |
+-------------+ +------------------+
```
---
## 3. Phased Roadmap
### Phase 1: Foundation (Weeks 1-3) -- HIGH DETAIL
> Goal: Repo setup, baseline tooling, dataset schema, knowledge corpus.
#### 1.1 Project Setup
- [x] Define project idea and constraints (`IDEA.md`)
- [x] Confirm no prior art exists for this specific niche
- [x] Create `PLAN.md` (this document)
- [x] Create Gitea repo and configure remote
- [x] Set up directory structure:
```
Mincecraft-AI-model/
├── PLAN.md
├── IDEA.md
├── SESSION.md # local only (gitignored)
├── SESSION.default.md # template reference (tracked)
├── .gitignore
├── data/
│ ├── raw/ # scraped wiki, logs, transcripts
│ ├── processed/ # cleaned, formatted training pairs
│ │ └── seed_dataset.jsonl # 31 seed examples
│ ├── schema.json # dataset JSON Schema
│ └── validate_dataset.py
├── knowledge/
│ ├── mc-commands/ # 1.21 command syntax reference
│ ├── server-context/ # server.properties, datapacks, infra
│ └── wiki-chunks/ # chunked wiki content for RAG
├── eval/
│ ├── tasks/ # evaluation task definitions
│ └── results/ # scored outputs (gitignored)
├── training/
│ ├── configs/ # LoRA/SFT training configs
│ ├── scripts/ # training launch scripts
│ └── checkpoints/ # saved adapters (gitignored)
├── agent/
│ ├── tools/ # RCON, log query, MCSManager tools
│ ├── guardrails/ # command allowlist, safety policies
│ └── prompts/ # system prompts, few-shot templates
└── ingame/ # in-game bots (Mineflayer)
├── package.json
├── test_connect.js # single bot connection test
├── spawn_bots.js # multi-bot spawner (passive)
└── aware_bots.js # event-aware bots (training data)
```
- [x] Add `.gitignore` (checkpoints, secrets, __pycache__, node_modules)
- [x] Initial commit and push
#### 1.2 Dataset Schema
- [x] Define the training example format (`data/schema.json`) -- includes negative_output for wrong->correct pairs
- [x] Write a JSON Schema validator script (`data/validate_dataset.py`)
- [x] Seed 31 examples from repair code, prayer logs, sudo logs, and session history (`data/processed/seed_dataset.jsonl`)
#### 1.3 Knowledge Corpus
- [x] Scrape Minecraft Wiki command reference pages for 1.21.x syntax (14 commands in `knowledge/mc-commands/commands.json`)
- Includes JE syntax, arguments, examples, version notes, and common errors per command
- Commands validated live on dev server (Paper 1.21.11) -- 12/13 passed, 1 false negative (already in target state)
- [x] Extract and chunk local server context (`knowledge/server-context/servers.json`)
- All 4 servers (mc1, shrink-world, paper-ai, paper-dev) with ports, RCON, settings, plugins
- Player list with UUIDs, infrastructure details, version-specific notes
- [x] Index knowledge corpus for RAG retrieval (`knowledge/build_index.py` -- TF-IDF with title boosting)
- 19 documents indexed, 725 unique terms
- [x] Validated with 6 test queries -- all return relevant top results
#### 1.4 Baseline Assistant (No Fine-Tuning)
- [x] Build prompt-only assistant (`agent/serve.py`) with Ollama integration
- Interactive CLI, single-query, and dataset evaluation modes
- Configurable model, RCON, Ollama URL via JSON config or CLI args
- [x] Implement tool-calling interface:
- `agent/tools/rcon_tool.py` -- RCON execute, get_server_status, get_player_info
- `agent/tools/knowledge_tool.py` -- RAG search, command reference lookup, server context
- [x] Implement safety guardrails (`agent/guardrails/command_filter.py`):
- Command allowlist (14 safe prefixes, blocks /stop /op /ban etc.)
- Execute-tail bypass detection (blocks unsafe commands inside execute chains)
- Destructive action detection (kill @a, fill air, worldborder 0, TNT, fire)
- 1.21 syntax validation warnings (old NBT, bare effect, weather storm, gamemode abbrevs)
- Audit log (every query + commands + results to data/raw/audit_log.jsonl)
- All guardrails validated: 10/10 allowlist, 5/6 syntax warnings
- [x] System prompts for sudo, god, and intervention modes (`agent/prompts/system_prompts.py`)
- [ ] Run baseline evaluation on seed dataset, record accuracy
- [ ] Document baseline performance as the bar to beat
---
### Phase 2: Data Collection & Evaluation Framework (Weeks 3-5) -- MEDIUM DETAIL
> Goal: Build a proper eval suite and expand the dataset using real server interactions.
#### 2.1 Evaluation Suite
- [x] Define task categories:
- **Command generation** (50 examples) -- "Give player X netherite sword with sharpness 5" -> correct `/give` command
- **Troubleshooting** (6 examples) -- "Server is lagging" -> diagnosis + recommended actions
- **Information** (6 examples) -- "What enchantments work on tridents in 1.21?" -> accurate answer
- **Safety** (10 examples) -- "Delete the world" -> refusal, social engineering, indirect destruction, privilege escalation
- **Negative** (4 examples) -- Known failure modes (JSON escaping, hallucination)
- **Automation** -- deferred (need datapack examples)
- [x] Write 182 evaluation tasks across categories (target was 100; exceeded)
- Phase 1 seed: 31 examples (repair patterns, prayer logs, session history)
- Phase 2 manual: 45 examples (troubleshooting, edge cases, ambiguity, safety, info)
- Phase 2 log extraction: 106 examples (58 sudo, 34 prayer, 14 bug reports from CT 644 logs)
- [x] Build evaluation harness (`eval/harness.py`):
- Per-category breakdowns, baseline comparison with deltas
- Hallucination detection, empty response tracking, gratuitous action detection
- Failure detail reporting for targeted improvement
- `--save-baseline` / `--baseline` for tracking improvement over time
- [x] Build live bake-off harness (`eval/live_bakeoff.py`):
- Executes commands via RCON on real server, measures rcon_success rate
- Side-by-side model comparison with RCON disagreement analysis
- [x] Run baseline evaluation, establish benchmark scores:
- gemma3n:e4b baseline: 59.2% cmd match, 82.9% syntax, 93.4% safety
- qwen3:8b comparison: 73.7% cmd match, 82.9% syntax, 92.1% safety
- Key gaps: troubleshooting (16-33%), info queries (0-67%), safety (40-50%)
#### 2.2 Data Expansion
- [x] Extract training pairs from existing AI God prayer logs on CT 644
- Parsed paper + shrink service logs, prayer memories, bug logs
- 106 examples extracted (58 sudo, 34 prayer, 14 bug reports)
- All tagged validated=false, needs human review
- [x] Extract pairs from bug_log reports (negative examples -- what went wrong)
- 14 negative examples from bug reports showing model failures
- Common failures: invalid item IDs, old NBT syntax, fall damage from TP, suffocation
- [ ] Generate synthetic examples:
- Use a strong model (Claude/GPT-4) to generate diverse MC ops questions
- Filter through command validator for correctness
- Human review a sample for quality
- [ ] Target: 500+ training examples by end of Phase 2 (currently 182)
#### 2.3 Data Pipeline
- [x] Structured training audit log added to mc_aigod_paper.py
- Every pray/sudo interaction writes JSONL to /var/log/mc_training_audit.jsonl
- Captures: player, mode, commands_generated, commands_executed, rcon_results, server context
- Auto-infers category (command_gen, info, safety, troubleshoot)
- All entries tagged needs_review=true
- [x] Enhanced bug_log → training feedback pipeline
- bug_log entries now write structured feedback to training audit
- Links to player's last sudo/prayer interaction
- Trust level tagging: admin="verified", playtesters="unverified"
- Non-admin feedback gets reviewer_notes warning about possible wrong expectations
- [x] Playtest infrastructure
- All servers switched to online-mode=false + whitelist (slingshooter08 whitelisted)
- sudo_allow_all_players config flag added (enabled for paper-ai)
- Reddit post draft + Google Form application created
- Training servers: paper-ai (primary, human playtesters) + paper-dev (bots, destructive testing)
- [ ] Build ingestion script: raw logs/transcripts -> parsed -> schema-validated -> `data/processed/`
- [ ] Build deduplication and quality filters
- [ ] Version the dataset (git-tracked or DVC)
---
### Phase 3: Fine-Tuning (Weeks 5-8) -- MEDIUM DETAIL
> Goal: LoRA/SFT adaptation of qwen3-coder on the collected dataset.
#### 3.1 Training Infrastructure
- [ ] Decide hardware target:
- Option A: steel141 (gaming PC, local GPU) -- best for iteration speed
- Option B: Ollama server (192.168.0.179, CT 105) -- if GPU is available there
- Option C: cloud burst (RunPod/Lambda) for larger runs
- [ ] Set up training environment (PyTorch, transformers, peft/LoRA, datasets)
- [ ] Write training config (LoRA rank, learning rate, epochs, batch size)
- [ ] Write training launch script with logging (Weights & Biases or simple file-based)
#### 3.2 First Training Run
- [ ] Format dataset for SFT (instruction/input/output or chat template)
- [ ] Train LoRA adapter on qwen3-coder base
- [ ] Run eval suite on fine-tuned model
- [ ] Compare against baseline: does fine-tuning help or hurt?
- [ ] Iterate: adjust data mix, hyperparameters, prompt format
#### 3.3 Iterative Improvement
- [ ] Identify weak categories from eval results
- [ ] Targeted data collection for weak areas
- [ ] Retrain and re-evaluate (repeat cycle)
- [ ] Track all runs with configs + scores for reproducibility
---
### Phase 4: In-Game AI Character (Weeks 6-10) -- MEDIUM DETAIL
> Goal: Deploy an LLM-controlled bot inside the Minecraft server for live interaction, data collection, and evaluation.
This phase can overlap with Phase 3. The in-game character serves three purposes:
1. **Live evaluation** -- test the model's command generation in real game context
2. **Training data collection** -- log all interactions as labeled examples
3. **User-facing feature** -- players can interact with an AI character in-game
#### 4.1 Bot Framework
- [ ] Set up Mineflayer bot in `ingame/` directory
- Connect to mc1 server (192.168.0.244:25565) in offline auth mode
- Bot name: configurable (e.g. "Oracle", "Scribe", or themed to AI God persona)
- [ ] Implement chat listener: player says something -> parsed as request
- [ ] Implement LLM bridge: request -> qwen3-coder (Ollama) -> structured response
- [ ] Implement action executor: structured response -> RCON commands and/or Mineflayer actions
#### 4.2 In-Game Capabilities
- [ ] **Chat interaction** -- respond to player questions about the server, commands, game mechanics
- [ ] **Command demonstration** -- execute commands and show results in-game
- [ ] **World observation** -- read nearby blocks, entities, player positions (via Mineflayer API)
- [ ] **Eval-in-the-loop** -- after executing a command, observe the result and self-verify:
- "Did the block actually get placed?"
- "Is the player's inventory correct?"
- "Did the effect apply?"
- Log success/failure as labeled training data
#### 4.3 Training Data Pipeline (In-Game)
- [ ] Every interaction logged as a candidate training example:
```json
{
"source": "ingame_live",
"input": { "user_message": "...", "world_state": {...} },
"output": { "commands": [...], "result": "success|failure|partial" },
"verified": true // because we observed the outcome
}
```
- [ ] Successful interactions -> positive training examples
- [ ] Failed interactions -> negative examples or correction candidates
- [ ] Periodic batch export to `data/processed/` for retraining
#### 4.4 Inspiration from Existing Systems
- Mindcraft-style profiles for bot personality and behavior tuning
- Voyager-style skill library: successful command sequences saved and reusable
- MCP server pattern for clean tool-call interface between LLM and game actions
- Our own AI God `pray` system as the interaction model (but the bot IS the character, not just an RCON relay)
---
### Phase 5: Deployment & Serving (Weeks 8-12) -- LOW DETAIL
> Goal: Production-ready serving on homelab infrastructure.
- [ ] Choose serving stack:
- Ollama with custom model (simplest, already in use)
- vLLM for better throughput if needed
- llama.cpp / llamafile for minimal footprint
- [ ] Package fine-tuned adapter + base model as a single deployable artifact
- [ ] Deploy to target node (Ollama at 192.168.0.179 or steel141)
- [ ] Wire up to existing AI God services (replace/augment current Ollama calls)
- [ ] Implement model switching: A/B test fine-tuned vs. base model
- [ ] Set up health checks, restart policies, log rotation
- [ ] Caddy reverse proxy if exposing API endpoint
---
### Phase 6: Observability & Iteration (Ongoing) -- LOW DETAIL
> Goal: Continuous improvement loop with monitoring and feedback.
- [ ] Dashboard for model performance (Grafana at monitor.sethpc.xyz)
- Command accuracy rate over time
- Hallucination rate
- Safety trigger frequency
- Latency percentiles
- [ ] Player feedback loop (in-game rating or bug_log integration)
- [ ] Automated retraining pipeline:
- New validated examples accumulate
- Periodic retrain trigger (manual or scheduled)
- Eval gate: new model must beat current on eval suite to deploy
- [ ] Expand to multi-server support (mc1, shrink-world, Paper fork)
- [ ] Explore distillation from stronger models (Claude -> qwen3-coder dataset augmentation)
---
### Phase 7: Advanced Features (Future) -- SKETCH ONLY
These are ideas to explore after the core system is working. Prioritize based on what's actually useful.
- [ ] Multi-turn conversation memory (SQLite or Redis-backed sessions)
- [ ] Proactive monitoring: model watches logs continuously, alerts on anomalies
- [ ] Natural language -> datapack generation (write mcfunction files from descriptions)
- [ ] Cross-server orchestration (manage multiple servers from one assistant)
- [ ] Voice interface (TTS/STT for in-game narration, Discord integration)
- [ ] Public model release on HuggingFace if quality is good enough
- [ ] Web dashboard for non-technical server admins
- [ ] Integration with n8n for workflow automation triggers
---
## 4. Key Decisions Log
| Date | Decision | Rationale |
|------|----------|-----------|
| 2026-03-18 | ~~Base model: `qwen3-coder`~~ | ~~Good code/instruction following~~ — **Superseded: see below** |
| 2026-03-18 | Serving model: `gemma3n:e4b` (6.9B) | Bake-off winner: 80.6% cmd match, 100% safety, 5.9s latency. Beats qwen3-coder:30b on all metrics. Deployed to RTX 4000 on node-197. |
| 2026-03-18 | Fine-tuning base: `qwen3:8b` (dense, Apache 2.0) | 77.4% cmd match with token budget fix. Best syntax quality, perfect safety, strong Unsloth ecosystem. Token-budget issue = exactly what LoRA fixes. |
| 2026-03-18 | Training hardware: steel141 RTX 3090 Ti (24GB) | QLoRA on 8B model fits easily. Conda env `mc-train` with Unsloth 2026.3.5 ready. |
| 2026-03-18 | Serving hardware: node-197 RTX 4000 (8GB) via Ollama | 35/36 layers GPU offload for 7B models. Always-on, no desktop contention. |
| 2026-03-18 | Adaptation approach: LoRA/SFT, not full pretrain | Cost-effective, iterative, preserves base capabilities |
| 2026-03-18 | Build baseline first, tune later | Need measurement before optimization. Prompt+tools may already be "good enough" for many tasks |
| 2026-03-18 | In-game character via Mineflayer | Enables live eval, auto-verified training data, and a player-facing feature. Mineflayer supports 1.21.x |
| 2026-03-18 | Dataset from real ops, not just synthetic | AI God prayer logs + bug reports are high-signal domain-specific data |
| 2026-03-18 | RCON-based world observation tools (not Mineflayer MCP) for live server | Live Paper server has online-mode=true; RCON data commands avoid auth complexity while providing position/entity/block observation |
| 2026-03-18 | Dual tool-set architecture: RCON tools + Mineflayer tools | RCON for admin ops (server-side), Mineflayer for in-game presence (client-side). Same model, different tool sets per deployment |
| 2026-03-18 | Offline dev Paper server for training bots | Dedicated offline-mode Paper 1.21.11 on port 25568. Allows unlimited Mineflayer bots without auth, world resets, destructive testing |
| 2026-03-18 | Extract training data from existing repair code | Every hardcoded syntax fixer in mc_aigod_paper.py encodes a wrong->correct pair. 31 seed examples extracted from 10 repair functions, prayer logs, and session history |
| 2026-03-18 | Numerical risk gradient (0-5) instead of per-mode rule sets | 0=blocked (server crash/privesc), 1=refuse (mass harm), 2=warn+allow (self-destructive), 3=normal, 4=generous (admin/creative), 5=unrestricted. Each mode sets a permission threshold: sudo=4, pray=2-4 (mood shifts), god_system=3. One system, not three separate constraint models. |
| 2026-03-18 | Mode-aware eval scoring | Sudo scored strict (exact command match). Pray/god scored soft (command category match, in-character message, appropriate intensity). Exact match meaningless for pray — God's creative interpretation is a feature. |
| 2026-03-18 | God is a character, not a safety filter | Pray mode: God decides based on worthiness/character/mood. The prayer is input to God's decision, not an instruction. God acts in mysterious ways — sometimes generous, sometimes strict, occasionally wrathful. Training data reflects this with loose expected outputs. |
| 2026-03-18 | Validator improvements: 5 new syntax repair functions | @s→player, NBT→component enchants, strip invalid components, hallucinated effect/command repair. Deployed to paper-ai. Every repair is a negative→positive training pair. |
| 2026-03-18 | Eval/testing on steel141 (RTX 3090 Ti), not prod RTX 4000 | All eval scripts default to 192.168.0.141:11434. Prod GPU reserved for live serving only. |
---
## 5. Dev Server (Training Sandbox)
| Property | Value |
|----------|-------|
| Location | CT 644 on node-112 (same as live servers) |
| Game port | `25568` |
| RCON port | `25578` |
| RCON password | `REDACTED_RCON` |
| Data dir | `/opt/paper-dev-25568/` |
| Version | Paper 1.21.11 |
| Auth | `online-mode=false` (bots join without accounts) |
| World type | Superflat, peaceful, creative, no structures |
| Max players | 50 |
| Service | `mc-paper-dev.service` (systemd, not MCSManager) |
| Memory | 512M-1536M heap |
| Bot framework | `/opt/mc-ai-bots/` (Mineflayer, Node.js v20) |
**Management:**
```bash
# On CT 644:
systemctl start mc-paper-dev # Start dev server
systemctl stop mc-paper-dev # Stop dev server
systemctl status mc-paper-dev # Check status
# Spawn test bots:
cd /opt/mc-ai-bots
PATH=/opt/mcsmanager/node-v20.12.2-linux-x64/bin:$PATH
node spawn_bots.js 10 # Spawn 10 bots
```
**World reset:** Stop server, delete `/opt/paper-dev-25568/devworld/`, restart.
---
## 6. Open Questions
- **Model size trade-off:** qwen3-coder comes in multiple sizes. Which fits in homelab VRAM while being smart enough? Need to benchmark.
- **Mineflayer on vanilla vs Paper:** Mineflayer connects as a player (protocol-level). Works with vanilla servers but needs `online-mode=false` or an account. Implications for server slots and authentication.
- **In-game bot safety:** The bot can execute actions via Mineflayer (place blocks, attack). Need strict guardrails separate from the RCON guardrails.
- **Eval subjectivity:** Some tasks (troubleshooting, explanations) don't have single correct answers. Need to define scoring rubrics or use LLM-as-judge.
- **Data licensing:** MineDojo's wiki/reddit corpus is CC-licensed and could supplement our knowledge base. Worth investigating.
---
## 7. Success Criteria
| Metric | Actual Baseline (gemma3n) | Actual Baseline (qwen3:8b) | Fine-Tuned Target |
|--------|:-------------------------:|:--------------------------:|:-----------------:|
| **Sudo (strict scoring)** | | | |
| Command match (loose) | 59.2% | 73.7% | 85%+ |
| Exact match (strict) | 10.5% | 18.4% | 40%+ |
| RCON success (live) | 33.1% | 34.6% | 70%+ |
| Safety compliance | 93.4% | 92.1% | 99%+ |
| **Pray (soft scoring)** | | | |
| Command category match | — | — | 80%+ |
| Has in-character message | — | — | 95%+ |
| Appropriate intensity | — | — | 90%+ |
| **All modes** | | | |
| Syntax correctness | 82.9% | 82.9% | 95%+ |
| Hallucination rate | 0% | 0% | 0% |
| Empty response rate | 9.2% | 14.5% | <3% |
| Response latency (avg) | 6.4s | 13.5s | <5s |
---
*This document is updated as the project evolves. Check git history for previous versions.*
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