Prompt pipeline: 1660 generates, bigger GPUs process via Mortdecai

Architecture: - 1660 Super (qwen3.5:0.8b) generates diverse edge-case prompts - 2080 Ti / RTX 4000 / 3090 Ti process through Mortdecai + RCON validation - File-based queue with locking for multi-GPU coordination - 10 prompt categories targeting known weaknesses Categories: fill_syntax, enchantments, execute_chains, entity_targeting, gamerules_timed, memory_commands, creative_prayers, edge_items, multicommand, natural_language Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-21 00:08:48 -04:00
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+#!/usr/bin/env python3
+"""
+Prompt Pipeline — 1660 generates prompts, bigger GPUs process them through Mortdecai.
+
+Architecture:
+  1660 Super (qwen3.5:0.8b) → generates diverse edge-case prompts
+  → writes to shared queue file
+  2080 Ti / RTX 4000 / 3090 Ti (mortdecai) → reads queue, processes, validates via RCON
+
+Usage:
+    # Terminal 1: prompt generator (runs on 1660)
+    python3 prompt_pipeline.py --mode generate --ollama-url http://192.168.0.235:11434
+
+    # Terminal 2+: processors (one per GPU)
+    python3 prompt_pipeline.py --mode process --ollama-url http://192.168.0.141:11434
+    python3 prompt_pipeline.py --mode process --ollama-url http://192.168.0.179:11434
+
+    # Or run everything:
+    python3 prompt_pipeline.py --mode all
+"""
+
+import argparse
+import json
+import os
+import re
+import random
+import sys
+import time
+import threading
+import fcntl
+from pathlib import Path
+
+import requests
+
+ROOT = Path(__file__).resolve().parent.parent.parent
+sys.path.insert(0, str(ROOT))
+from agent.tools.persistent_rcon import get_rcon
+
+QUEUE_FILE = ROOT / "data" / "processed" / "prompt_queue.jsonl"
+OUTPUT_FILE = ROOT / "data" / "processed" / "pipeline_output.jsonl"
+LOCK_FILE = str(QUEUE_FILE) + ".lock"
+
+# Categories the model needs more training on (based on validator hit rates and bake-off failures)
+GENERATION_PROMPTS = {
+    "fill_syntax": """Generate 5 Minecraft player chat messages that involve filling areas with blocks.
+Include: walls, floors, replacing blocks, clearing areas, building structures.
+The AI often gets fill syntax wrong — adding a trailing count number or wrong coordinates.
+Every message must start with "sudo ". Return ONLY a JSON array of strings.""",
+
+    "enchantments": """Generate 5 Minecraft player chat messages requesting enchanted items.
+Include: multiple enchantments, max level enchants, mutually exclusive combinations, items that cant be enchanted.
+The AI sometimes uses old NBT syntax instead of 1.21 component syntax.
+Every message must start with "sudo " or "pray ". Return ONLY a JSON array of strings.""",
+
+    "execute_chains": """Generate 5 Minecraft player chat messages requiring execute command chains.
+Include: execute as/at/if/positioned/in, targeting by gamemode/team, dimension switching, conditional blocks.
+Every message must start with "sudo ". Return ONLY a JSON array of strings.""",
+
+    "entity_targeting": """Generate 5 Minecraft player chat messages about killing or targeting specific entities.
+Include: "kill THE zombie" vs "kill ALL zombies", distance-based targeting, entity types, nearby vs far.
+Every message must start with "sudo ". Return ONLY a JSON array of strings.""",
+
+    "gamerules_timed": """Generate 5 Minecraft player chat messages requesting temporary gamerule changes.
+Include: specific durations like "for 5 minutes", "for an hour", "briefly", "permanently".
+The AI needs to learn when to set revert timers vs permanent changes.
+Every message must start with "sudo ". Return ONLY a JSON array of strings.""",
+
+    "memory_commands": """Generate 5 Minecraft player chat messages about remembering or recalling locations and facts.
+Include: "remember this as home", "tp me home", "what do you know about me", "forget my base".
+Every message must start with "sudo " or "pray ". Return ONLY a JSON array of strings.""",
+
+    "creative_prayers": """Generate 5 creative Minecraft prayers to an AI God character.
+Include: humble requests, greedy demands, blasphemy, roleplay, emotional appeals, sarcasm.
+Be creative and diverse. Every message must start with "pray ". Return ONLY a JSON array of strings.""",
+
+    "edge_items": """Generate 5 Minecraft player requests for obscure or easily-confused items.
+Include: items with color variants (wool, concrete, glass), items that changed names between versions,
+items people misspell (steak vs cooked_beef, log vs oak_log, bed vs white_bed).
+Every message must start with "sudo ". Return ONLY a JSON array of strings.""",
+
+    "multicommand": """Generate 5 Minecraft player requests that need multiple commands to fulfill.
+Include: "gear me up for the nether", "build a house and tp me inside", "create a pvp arena",
+"prepare me for a boss fight". Complex multi-step requests.
+Every message must start with "sudo " or "pray ". Return ONLY a JSON array of strings.""",
+
+    "natural_language": """Generate 5 Minecraft chat messages phrased in unusual natural language.
+Include: typos, slang, abbreviations, run-on sentences, vague requests, sarcasm, mixed languages.
+The AI should still figure out what the player wants.
+Every message must start with "sudo " or "pray ". Return ONLY a JSON array of strings.""",
+}
+
+
+def ensure_prefix(msg):
+    lower = msg.lower().strip()
+    if lower.startswith("pray ") or lower.startswith("sudo ") or lower.startswith("bug_log"):
+        return msg
+    return ("pray " if random.random() < 0.4 else "sudo ") + msg
+
+
+def generate_prompts(ollama_url, model="qwen3.5:0.8b", count=50):
+    """Use a small model to generate diverse prompts."""
+    prompts = []
+    categories = list(GENERATION_PROMPTS.keys())
+    random.shuffle(categories)
+
+    for cat in categories:
+        if len(prompts) >= count:
+            break
+        try:
+            r = requests.post(f"{ollama_url}/api/chat", json={
+                "model": model,
+                "messages": [
+                    {"role": "system", "content": "You generate Minecraft chat messages for AI training. Return ONLY a JSON array of strings."},
+                    {"role": "user", "content": GENERATION_PROMPTS[cat]},
+                ],
+                "stream": False,
+                "options": {"temperature": 1.1, "num_predict": 400},
+            }, timeout=60)
+            content = r.json()["message"]["content"]
+            content = re.sub(r'<think>[\s\S]*?</think>\s*', '', content)
+            cleaned = content.replace("```json", "").replace("```", "").strip()
+            match = re.search(r'\[[\s\S]*\]', cleaned)
+            if match:
+                items = json.loads(match.group())
+                for item in items:
+                    if isinstance(item, str) and item.strip():
+                        prompts.append({
+                            "prompt": ensure_prefix(item.strip()),
+                            "category": cat,
+                            "generated_at": time.strftime("%Y-%m-%dT%H:%M:%SZ"),
+                        })
+            print(f"  [{cat}] +{len(items) if match else 0} prompts")
+        except Exception as e:
+            print(f"  [{cat}] ERROR: {e}")
+        time.sleep(0.5)
+
+    return prompts
+
+
+def write_queue(prompts):
+    """Append prompts to the queue file with file locking."""
+    os.makedirs(os.path.dirname(QUEUE_FILE), exist_ok=True)
+    with open(LOCK_FILE, "w") as lf:
+        fcntl.flock(lf, fcntl.LOCK_EX)
+        with open(QUEUE_FILE, "a") as f:
+            for p in prompts:
+                f.write(json.dumps(p) + "\n")
+        fcntl.flock(lf, fcntl.LOCK_UN)
+    print(f"  Queued {len(prompts)} prompts")
+
+
+def read_queue(batch_size=5):
+    """Read and remove prompts from the queue."""
+    if not QUEUE_FILE.exists():
+        return []
+
+    with open(LOCK_FILE, "w") as lf:
+        fcntl.flock(lf, fcntl.LOCK_EX)
+        with open(QUEUE_FILE) as f:
+            lines = f.readlines()
+        taken = lines[:batch_size]
+        remaining = lines[batch_size:]
+        with open(QUEUE_FILE, "w") as f:
+            f.writelines(remaining)
+        fcntl.flock(lf, fcntl.LOCK_UN)
+
+    return [json.loads(l) for l in taken if l.strip()]
+
+
+def process_prompt(prompt_data, ollama_url, model, rcon_host, rcon_port, rcon_pass):
+    """Process a single prompt through Mortdecai and validate via RCON."""
+    prompt = prompt_data["prompt"]
+    category = prompt_data.get("category", "unknown")
+    mode = "god" if prompt.lower().startswith("pray ") else "sudo"
+
+    system = ('You are God in a Minecraft server. Return JSON: {"message": "...", "commands": ["cmd1"], "reasoning": "why"}'
+              if mode == "god" else
+              'You are a Minecraft 1.21 command translator. Return JSON: {"commands": ["cmd1"], "reasoning": "why"}\n'
+              'IMPORTANT: Each command is ONE COMPLETE STRING. Use minecraft: prefix. Enchantments: item[enchantments={name:level}].')
+
+    try:
+        r = requests.post(f"{ollama_url}/api/chat", json={
+            "model": model,
+            "messages": [
+                {"role": "system", "content": "/no_think\n" + system},
+                {"role": "user", "content": f"Player slingshooter08: {prompt}"},
+            ],
+            "stream": False, "format": "json",
+            "options": {"temperature": 0.3, "num_predict": 500},
+        }, timeout=120)
+        content = r.json()["message"]["content"]
+        content = re.sub(r'<think>[\s\S]*?</think>\s*', '', content)
+        parsed = json.loads(content)
+    except Exception as e:
+        return {"prompt": prompt, "category": category, "error": str(e), "success": False}
+
+    commands = parsed.get("commands") or []
+    if not commands or not all(isinstance(c, str) and " " in c for c in commands):
+        return {"prompt": prompt, "category": category, "commands": commands, "success": False, "reason": "empty_or_split"}
+
+    # Validate via RCON
+    rcon = get_rcon(rcon_host, rcon_port, rcon_pass)
+    results = []
+    all_ok = True
+    for cmd in commands[:6]:
+        try:
+            result = rcon.command(cmd)
+            is_error = "<--[HERE]" in result or "Unknown" in result or "Incorrect" in result
+            results.append({"cmd": cmd, "result": result[:150], "ok": not is_error})
+            if is_error:
+                all_ok = False
+        except:
+            results.append({"cmd": cmd, "result": "RCON error", "ok": False})
+            all_ok = False
+
+    return {
+        "prompt": prompt,
+        "category": category,
+        "mode": mode,
+        "commands": commands,
+        "rcon_results": results,
+        "success": all_ok,
+        "message": parsed.get("message", ""),
+        "reasoning": parsed.get("reasoning", ""),
+        "processed_at": time.strftime("%Y-%m-%dT%H:%M:%SZ"),
+        "ollama_url": ollama_url,
+    }
+
+
+def save_result(result):
+    """Save a processed result."""
+    os.makedirs(os.path.dirname(OUTPUT_FILE), exist_ok=True)
+    with open(OUTPUT_FILE, "a") as f:
+        f.write(json.dumps(result, ensure_ascii=False) + "\n")
+
+
+def run_generator(ollama_url, model, interval=120):
+    """Continuously generate prompts."""
+    print(f"Prompt Generator started")
+    print(f"  Model: {model} on {ollama_url}")
+    print(f"  Interval: {interval}s between batches")
+
+    while True:
+        print(f"\nGenerating batch at {time.strftime('%H:%M:%S')}...")
+        prompts = generate_prompts(ollama_url, model, count=30)
+        if prompts:
+            write_queue(prompts)
+        print(f"  Queue size: {sum(1 for _ in open(QUEUE_FILE)) if QUEUE_FILE.exists() else 0}")
+        time.sleep(interval)
+
+
+def run_processor(ollama_url, model, rcon_host, rcon_port, rcon_pass):
+    """Continuously process prompts from the queue."""
+    print(f"Processor started")
+    print(f"  Model: {model} on {ollama_url}")
+    print(f"  RCON: {rcon_host}:{rcon_port}")
+
+    stats = {"processed": 0, "success": 0, "failed": 0}
+
+    while True:
+        batch = read_queue(3)
+        if not batch:
+            time.sleep(5)
+            continue
+
+        for prompt_data in batch:
+            result = process_prompt(prompt_data, ollama_url, model, rcon_host, rcon_port, rcon_pass)
+            save_result(result)
+            stats["processed"] += 1
+            if result.get("success"):
+                stats["success"] += 1
+                print(f"  OK [{result['category'][:12]}] {result['prompt'][:50]}")
+            else:
+                stats["failed"] += 1
+                print(f"  FAIL [{result['category'][:12]}] {result['prompt'][:50]}")
+
+            if stats["processed"] % 20 == 0:
+                rate = stats["success"] / max(stats["processed"], 1) * 100
+                print(f"  Stats: {stats['processed']} processed, {rate:.0f}% success")
+
+            time.sleep(0.2)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Prompt generation pipeline")
+    parser.add_argument("--mode", choices=["generate", "process", "all"], required=True)
+    parser.add_argument("--gen-url", default="http://192.168.0.235:11434", help="Generator Ollama URL (small model)")
+    parser.add_argument("--gen-model", default="qwen3.5:0.8b", help="Generator model")
+    parser.add_argument("--proc-urls", default="http://192.168.0.141:11434,http://192.168.0.179:11434",
+                        help="Processor Ollama URLs (comma-separated)")
+    parser.add_argument("--proc-model", default="mortdecai:0.4.0", help="Processor model")
+    parser.add_argument("--rcon-host", default="192.168.0.244")
+    parser.add_argument("--rcon-port", type=int, default=25578)
+    parser.add_argument("--rcon-pass", default="REDACTED_RCON")
+    parser.add_argument("--interval", type=int, default=120, help="Generator batch interval (seconds)")
+    args = parser.parse_args()
+
+    if args.mode == "generate":
+        run_generator(args.gen_url, args.gen_model, args.interval)
+
+    elif args.mode == "process":
+        urls = args.proc_urls.split(",")
+        run_processor(urls[0], args.proc_model, args.rcon_host, args.rcon_port, args.rcon_pass)
+
+    elif args.mode == "all":
+        urls = args.proc_urls.split(",")
+
+        # Start generator in background
+        gen_thread = threading.Thread(target=run_generator,
+                                      args=(args.gen_url, args.gen_model, args.interval), daemon=True)
+        gen_thread.start()
+
+        # Start a processor per URL
+        for url in urls:
+            proc_thread = threading.Thread(target=run_processor,
+                                           args=(url, args.proc_model, args.rcon_host, args.rcon_port, args.rcon_pass),
+                                           daemon=True)
+            proc_thread.start()
+            print(f"Processor started on {url}")
+
+        # Keep main alive
+        while True:
+            time.sleep(60)
+            queue_size = sum(1 for _ in open(QUEUE_FILE)) if QUEUE_FILE.exists() else 0
+            output_size = sum(1 for _ in open(OUTPUT_FILE)) if OUTPUT_FILE.exists() else 0
+            print(f"[Pipeline] Queue: {queue_size} | Output: {output_size}")
+
+
+if __name__ == "__main__":
+    main()