Mortdecai/agent/serve.py

#!/usr/bin/env python3
"""
Minecraft AI Ops Assistant -- Baseline (No Fine-Tuning)

Prompt-only assistant using qwen3-coder via Ollama with tool calling.
This is the Phase 1.4 baseline to measure against future fine-tuned models.

Usage:
    # Interactive CLI mode
    python3 agent/serve.py --mode sudo --player slingshooter08

    # Single query mode
    python3 agent/serve.py --mode sudo --player slingshooter08 --query "give me diamond armor"

    # Evaluate against dataset
    python3 agent/serve.py --eval data/processed/seed_dataset.jsonl
"""

import argparse
import json
import sys
import time
from pathlib import Path
from typing import Dict, Any, List, Optional

import requests

# Add project root to path
ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(ROOT))

from agent.tools.rcon_tool import RconTool
from agent.tools.knowledge_tool import search_knowledge, get_command_reference, get_server_context
from agent.guardrails.command_filter import validate_command, filter_commands, audit_log
from agent.prompts.system_prompts import get_prompt


DEFAULT_CONFIG = {
    'ollama_url': 'http://192.168.0.179:11434',
    'model': 'qwen3-coder:30b',
    'rcon_host': '127.0.0.1',
    'rcon_port': 25577,
    'rcon_password': 'REDACTED_RCON',
    'max_tool_steps': 3,
    'temperature': 0.2,
    'max_tokens': 300,
}


def load_config(path: str = '') -> dict:
    """Load config from file or use defaults."""
    if path and Path(path).exists():
        with open(path) as f:
            cfg = json.load(f)
        return {**DEFAULT_CONFIG, **cfg}
    return dict(DEFAULT_CONFIG)


def ollama_chat(model: str, messages: List[Dict], ollama_url: str,
                temperature: float = 0.2, max_tokens: int = 300,
                fmt: Optional[str] = 'json') -> str:
    """Call Ollama chat API."""
    payload = {
        'model': model,
        'messages': messages,
        'stream': False,
        'options': {
            'temperature': temperature,
            'num_predict': max_tokens,
        }
    }
    if fmt:
        payload['format'] = fmt

    r = requests.post(f"{ollama_url}/api/chat", json=payload, timeout=120)
    r.raise_for_status()
    return r.json()['message']['content']


def parse_response(content: str) -> Dict[str, Any]:
    """Parse LLM JSON response, with fallback for malformed output."""
    try:
        return json.loads(content)
    except json.JSONDecodeError:
        # Try to extract commands from partial JSON
        import re
        cmds = re.findall(r'"([^"]+)"', content)
        return {'commands': cmds, 'message': '', 'reasoning': 'parse fallback'}


class MinecraftAssistant:
    """Baseline Minecraft ops assistant with tools and guardrails."""

    def __init__(self, config: dict):
        self.config = config
        self.rcon = RconTool(
            host=config['rcon_host'],
            port=config['rcon_port'],
            password=config['rcon_password'],
        )
        self.model = config['model']
        self.ollama_url = config['ollama_url']

    def _gather_context(self, player: str, query: str) -> str:
        """Gather world state and knowledge context for the LLM."""
        context_parts = []

        # Player info
        if player:
            info = self.rcon.get_player_info(player)
            if info.get('online'):
                pos = info.get('position', {})
                context_parts.append(
                    f"Player: {player} at ({pos.get('x', 0):.0f}, {pos.get('y', 0):.0f}, {pos.get('z', 0):.0f}) "
                    f"health={info.get('health', '?')} gamemode={info.get('gamemode', '?')}"
                )

        # Server status
        status = self.rcon.get_server_status()
        context_parts.append(f"Online: {', '.join(status['players_online']) or 'none'}")

        # Knowledge search
        kb_results = search_knowledge(query, limit=3)
        if kb_results:
            context_parts.append("Relevant reference:")
            for r in kb_results:
                context_parts.append(f"  [{r['title']}] {r['snippet'][:150]}")

        return '\n'.join(context_parts)

    def ask(self, query: str, player: str = '', mode: str = 'sudo') -> Dict[str, Any]:
        """
        Process a query and return structured response.

        Returns:
            {
                'message': str or None,
                'commands': [str],
                'reasoning': str,
                'tool_trace': [dict],
                'guardrail_results': [dict],
            }
        """
        start = time.time()
        tool_trace = []

        # Gather context
        context = self._gather_context(player, query)
        tool_trace.append({'tool': 'context_gather', 'duration_ms': int((time.time() - start) * 1000)})

        # Build messages
        system_prompt = get_prompt(mode)
        user_message = f"Request from {player or 'admin'}: {query}\n\nContext:\n{context}"

        messages = [
            {'role': 'system', 'content': system_prompt},
            {'role': 'user', 'content': user_message},
        ]

        # LLM call
        llm_start = time.time()
        raw = ollama_chat(
            self.model, messages, self.ollama_url,
            temperature=self.config['temperature'],
            max_tokens=self.config['max_tokens'],
        )
        tool_trace.append({
            'tool': 'llm_call', 'model': self.model,
            'duration_ms': int((time.time() - llm_start) * 1000),
        })

        # Parse response
        parsed = parse_response(raw)
        commands = parsed.get('commands', [])
        message = parsed.get('message')
        reasoning = parsed.get('reasoning', '')

        # Apply guardrails
        safe_commands, guardrail_results = filter_commands(commands)

        # Audit log
        audit_log({
            'mode': mode,
            'player': player,
            'query': query,
            'raw_commands': commands,
            'safe_commands': safe_commands,
            'message': message,
            'reasoning': reasoning,
            'model': self.model,
            'duration_ms': int((time.time() - start) * 1000),
        })

        return {
            'message': message,
            'commands': safe_commands,
            'reasoning': reasoning,
            'raw_commands': commands,
            'tool_trace': tool_trace,
            'guardrail_results': guardrail_results,
            'duration_ms': int((time.time() - start) * 1000),
        }

    def evaluate(self, dataset_path: str) -> Dict[str, Any]:
        """
        Run the assistant against a dataset and score results.

        Returns summary statistics.
        """
        results = []
        with open(dataset_path) as f:
            examples = [json.loads(line) for line in f if line.strip()]

        print(f"Evaluating {len(examples)} examples with {self.model}...")
        print()

        correct = 0
        syntax_ok = 0
        safety_ok = 0
        total = len(examples)

        for i, ex in enumerate(examples):
            query = ex['input']['user_message']
            expected_cmds = ex['output'].get('commands', [])
            expected_safety = ex['output'].get('safety_flags', [])
            category = ex.get('category', '?')

            # Determine mode from query
            mode = 'sudo'
            if query.lower().startswith('pray '):
                mode = 'god'
                query = query[5:]

            # Run assistant
            result = self.ask(query, player='slingshooter08', mode=mode)
            actual_cmds = result.get('commands', [])

            # Score: command match (loose)
            expected_set = set(c.strip() for c in expected_cmds)
            actual_set = set(c.strip() for c in actual_cmds)

            # Check if commands are functionally similar (not exact match)
            cmd_match = False
            if not expected_cmds and not actual_cmds:
                cmd_match = True  # both empty = correct refusal
            elif expected_cmds and actual_cmds:
                # Check if same command types are used
                expected_types = set(c.split()[0] for c in expected_cmds if c)
                actual_types = set(c.split()[0] for c in actual_cmds if c)
                cmd_match = len(expected_types & actual_types) > 0

            # Syntax check: do any actual commands have guardrail warnings?
            has_syntax_issues = any(
                r.get('warnings') for r in result.get('guardrail_results', [])
            )

            # Safety check: if expected is empty commands (refusal), did model also refuse?
            safety_match = True
            if 'destructive' in expected_safety and expected_cmds == []:
                safety_match = len(actual_cmds) == 0

            if cmd_match:
                correct += 1
            if not has_syntax_issues:
                syntax_ok += 1
            if safety_match:
                safety_ok += 1

            status = 'OK' if cmd_match else 'MISS'
            print(f"[{i+1}/{total}] [{status}] ({category}) {query[:60]}")
            if not cmd_match:
                print(f"  Expected: {expected_cmds[:3]}")
                print(f"  Got:      {actual_cmds[:3]}")

            results.append({
                'id': ex.get('id'),
                'category': category,
                'query': query,
                'expected': expected_cmds,
                'actual': actual_cmds,
                'cmd_match': cmd_match,
                'syntax_ok': not has_syntax_issues,
                'safety_ok': safety_match,
                'duration_ms': result.get('duration_ms', 0),
            })

        print()
        summary = {
            'total': total,
            'command_match_rate': round(correct / total * 100, 1) if total else 0,
            'syntax_ok_rate': round(syntax_ok / total * 100, 1) if total else 0,
            'safety_ok_rate': round(safety_ok / total * 100, 1) if total else 0,
            'model': self.model,
            'avg_duration_ms': round(sum(r['duration_ms'] for r in results) / total) if total else 0,
        }
        print(f"=== Baseline Evaluation Results ===")
        print(f"Model: {summary['model']}")
        print(f"Command match rate: {summary['command_match_rate']}%")
        print(f"Syntax OK rate: {summary['syntax_ok_rate']}%")
        print(f"Safety OK rate: {summary['safety_ok_rate']}%")
        print(f"Avg latency: {summary['avg_duration_ms']}ms")

        # Save results
        out_dir = ROOT / 'eval' / 'results'
        out_dir.mkdir(parents=True, exist_ok=True)
        ts = int(time.time())
        out_path = out_dir / f'baseline_{ts}.json'
        with open(out_path, 'w') as f:
            json.dump({'summary': summary, 'results': results}, f, indent=2)
        print(f"Results saved to {out_path}")

        return summary


def main():
    parser = argparse.ArgumentParser(description='Minecraft AI Ops Assistant')
    parser.add_argument('--mode', default='sudo', choices=['sudo', 'god', 'god_system'])
    parser.add_argument('--player', default='slingshooter08')
    parser.add_argument('--query', default='', help='Single query mode')
    parser.add_argument('--eval', default='', help='Evaluate against dataset file')
    parser.add_argument('--config', default='', help='Config JSON file path')
    parser.add_argument('--model', default='', help='Override model name')
    parser.add_argument('--ollama-url', default='', help='Override Ollama URL')
    args = parser.parse_args()

    config = load_config(args.config)
    if args.model:
        config['model'] = args.model
    if args.ollama_url:
        config['ollama_url'] = args.ollama_url

    assistant = MinecraftAssistant(config)

    if args.eval:
        assistant.evaluate(args.eval)
        return

    if args.query:
        result = assistant.ask(args.query, player=args.player, mode=args.mode)
        print(json.dumps(result, indent=2))
        return

    # Interactive mode
    print(f"Minecraft AI Assistant ({config['model']})")
    print(f"Mode: {args.mode} | Player: {args.player}")
    print("Type 'quit' to exit, 'mode <sudo|god>' to switch modes\n")

    while True:
        try:
            query = input(f"[{args.mode}] > ").strip()
        except (EOFError, KeyboardInterrupt):
            break

        if not query:
            continue
        if query.lower() == 'quit':
            break
        if query.lower().startswith('mode '):
            args.mode = query.split()[1]
            print(f"Switched to {args.mode} mode")
            continue

        result = assistant.ask(query, player=args.player, mode=args.mode)
        if result.get('message'):
            print(f"Message: {result['message']}")
        if result.get('commands'):
            print(f"Commands: {result['commands']}")
        if result.get('reasoning'):
            print(f"Reasoning: {result['reasoning']}")
        print(f"({result.get('duration_ms', 0)}ms)")
        print()


if __name__ == '__main__':
    main()