novelty-seeking/experiments/novelty_loop/demo.py

#!/usr/bin/env python3
"""
Novelty-Driven Task Generation Demo

Interactive CLI for exploring the novelty-driven task generation agent.

Examples:
    # Basic usage with default settings
    python demo.py "Improve urban transportation"

    # Custom threshold and iterations
    python demo.py "Design a better bicycle" --threshold 0.35 --max-iter 15

    # Use Chinese language
    python demo.py "改进城市交通" --language zh

    # Use exhaustion strategy (explore until stuck)
    python demo.py "Sustainable energy solutions" --strategy exhaust

    # Use coverage strategy (find N distinct clusters)
    python demo.py "Future of education" --strategy coverage --clusters 5

    # Save results to file
    python demo.py "Smart home innovations" --output results.json

    # Verbose mode with detailed logging
    python demo.py "Healthcare improvements" --verbose
"""

import argparse
import asyncio
import json
import logging
import sys
from datetime import datetime
from pathlib import Path

# Add parent directory to path for imports
sys.path.insert(0, str(Path(__file__).parent.parent.parent))

from experiments.novelty_loop.agent import (
    NoveltyDrivenTaskAgent,
    ExhaustFrontierAgent,
    CoverageTargetAgent,
    GeneratedTask,
    TaskGenerationResult
)

# ANSI color codes for terminal output
class Colors:
    HEADER = '\033[95m'
    BLUE = '\033[94m'
    CYAN = '\033[96m'
    GREEN = '\033[92m'
    YELLOW = '\033[93m'
    RED = '\033[91m'
    BOLD = '\033[1m'
    UNDERLINE = '\033[4m'
    END = '\033[0m'


def print_header(text: str):
    """Print a styled header."""
    print(f"\n{Colors.BOLD}{Colors.HEADER}{'='*60}{Colors.END}")
    print(f"{Colors.BOLD}{Colors.HEADER}{text.center(60)}{Colors.END}")
    print(f"{Colors.BOLD}{Colors.HEADER}{'='*60}{Colors.END}\n")


def print_iteration(task: GeneratedTask):
    """Print iteration result with colors."""
    status_color = Colors.GREEN if task.is_breakthrough else Colors.CYAN

    print(f"\n{Colors.BOLD}Iteration {task.iteration}{Colors.END}")
    print(f"  {Colors.YELLOW}Expert:{Colors.END} {task.expert} ({task.expert_domain})")
    print(f"  {Colors.YELLOW}Task:{Colors.END} {task.task}")

    novelty_bar = "█" * int(task.novelty_score * 20) + "░" * (20 - int(task.novelty_score * 20))
    print(f"  {Colors.YELLOW}Novelty:{Colors.END} [{novelty_bar}] {task.novelty_score:.4f}")

    if task.is_breakthrough:
        print(f"  {Colors.GREEN}{Colors.BOLD}★ BREAKTHROUGH! ★{Colors.END}")


def print_result(result: TaskGenerationResult):
    """Print final result summary."""
    print_header("RESULTS")

    print(f"{Colors.BOLD}Seed Problem:{Colors.END} {result.seed_problem}")
    print(f"{Colors.BOLD}Total Iterations:{Colors.END} {result.total_iterations}")
    print(f"{Colors.BOLD}Terminated By:{Colors.END} {result.terminated_by}")

    if result.novelty_trajectory:
        print(f"\n{Colors.BOLD}Novelty Statistics:{Colors.END}")
        print(f"  Mean Novelty: {result.novelty_trajectory.mean_novelty:.4f}")
        print(f"  Max Novelty:  {result.novelty_trajectory.max_novelty:.4f}")
        print(f"  Jump Ratio:   {result.novelty_trajectory.jump_ratio:.2%}")

    if result.breakthrough_task:
        print(f"\n{Colors.GREEN}{Colors.BOLD}{'='*60}{Colors.END}")
        print(f"{Colors.GREEN}{Colors.BOLD}BREAKTHROUGH TASK{Colors.END}")
        print(f"{Colors.GREEN}{Colors.BOLD}{'='*60}{Colors.END}")
        print(f"\n{Colors.BOLD}Expert:{Colors.END} {result.breakthrough_task.expert}")
        print(f"{Colors.BOLD}Domain:{Colors.END} {result.breakthrough_task.expert_domain}")
        print(f"{Colors.BOLD}Task:{Colors.END}")
        print(f"  {Colors.CYAN}{result.breakthrough_task.task}{Colors.END}")
        print(f"\n{Colors.BOLD}Novelty Score:{Colors.END} {result.breakthrough_task.novelty_score:.4f}")
        print(f"{Colors.BOLD}Found at Iteration:{Colors.END} {result.breakthrough_task.iteration}")

    # Show trajectory summary
    print(f"\n{Colors.BOLD}Exploration Trajectory:{Colors.END}")
    for task in result.trajectory:
        marker = "★" if task.is_breakthrough else "○"
        novelty_indicator = "█" * int(task.novelty_score * 10)
        print(f"  {marker} [{task.iteration:2d}] {task.expert:20s} | {novelty_indicator:10s} {task.novelty_score:.3f}")


def save_result(result: TaskGenerationResult, output_path: str):
    """Save result to JSON file."""
    with open(output_path, "w", encoding="utf-8") as f:
        json.dump(result.to_dict(), f, ensure_ascii=False, indent=2)
    print(f"\n{Colors.GREEN}Results saved to: {output_path}{Colors.END}")


async def run_demo(args):
    """Run the novelty-driven task generation demo."""

    print_header("NOVELTY-DRIVEN TASK GENERATION")

    print(f"{Colors.BOLD}Configuration:{Colors.END}")
    print(f"  Seed Problem: {args.seed_problem}")
    print(f"  Strategy: {args.strategy}")
    print(f"  Novelty Threshold: {args.threshold}")
    print(f"  Max Iterations: {args.max_iter}")
    print(f"  Language: {args.language}")
    print(f"  LLM Model: {args.model}")

    # Create appropriate agent based on strategy
    common_kwargs = {
        "max_iterations": args.max_iter,
        "llm_model": args.model,
        "embedding_model": args.embedding_model,
        "language": args.language,
        "temperature": args.temperature,
        "on_iteration": print_iteration if not args.quiet else None
    }

    if args.strategy == "breakthrough":
        agent = NoveltyDrivenTaskAgent(
            novelty_threshold=args.threshold,
            **common_kwargs
        )
    elif args.strategy == "exhaust":
        agent = ExhaustFrontierAgent(
            exhaustion_threshold=args.exhaust_threshold,
            window_size=args.window_size,
            min_iterations=args.min_iter,
            **common_kwargs
        )
    elif args.strategy == "coverage":
        agent = CoverageTargetAgent(
            target_clusters=args.clusters,
            cluster_threshold=args.cluster_threshold,
            **common_kwargs
        )
    else:
        print(f"{Colors.RED}Unknown strategy: {args.strategy}{Colors.END}")
        return

    print(f"\n{Colors.BOLD}Starting generation loop...{Colors.END}")
    print("-" * 60)

    try:
        result = await agent.run(args.seed_problem)
        print_result(result)

        if args.output:
            save_result(result, args.output)

    except Exception as e:
        print(f"\n{Colors.RED}Error: {e}{Colors.END}")
        if args.verbose:
            import traceback
            traceback.print_exc()
    finally:
        await agent.close()


def main():
    parser = argparse.ArgumentParser(
        description="Novelty-Driven Task Generation Demo",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog=__doc__
    )

    # Required argument
    parser.add_argument(
        "seed_problem",
        help="The seed problem or challenge to explore"
    )

    # Strategy selection
    parser.add_argument(
        "--strategy", "-s",
        choices=["breakthrough", "exhaust", "coverage"],
        default="breakthrough",
        help="Termination strategy (default: breakthrough)"
    )

    # Common options
    parser.add_argument(
        "--threshold", "-t",
        type=float,
        default=0.4,
        help="Novelty threshold for breakthrough (default: 0.4)"
    )
    parser.add_argument(
        "--max-iter", "-m",
        type=int,
        default=20,
        help="Maximum iterations (default: 20)"
    )
    parser.add_argument(
        "--language", "-l",
        choices=["en", "zh"],
        default="en",
        help="Language for prompts and experts (default: en)"
    )

    # Model options
    parser.add_argument(
        "--model",
        default="qwen3:8b",
        help="LLM model for task generation (default: qwen3:8b)"
    )
    parser.add_argument(
        "--embedding-model",
        default="qwen3-embedding:4b",
        help="Embedding model (default: qwen3-embedding:4b)"
    )
    parser.add_argument(
        "--temperature",
        type=float,
        default=0.7,
        help="LLM temperature (default: 0.7)"
    )

    # Exhaust strategy options
    parser.add_argument(
        "--exhaust-threshold",
        type=float,
        default=0.15,
        help="Exhaustion threshold for 'exhaust' strategy (default: 0.15)"
    )
    parser.add_argument(
        "--window-size",
        type=int,
        default=3,
        help="Window size for exhaustion check (default: 3)"
    )
    parser.add_argument(
        "--min-iter",
        type=int,
        default=5,
        help="Minimum iterations before exhaustion check (default: 5)"
    )

    # Coverage strategy options
    parser.add_argument(
        "--clusters",
        type=int,
        default=5,
        help="Target clusters for 'coverage' strategy (default: 5)"
    )
    parser.add_argument(
        "--cluster-threshold",
        type=float,
        default=0.7,
        help="Cluster similarity threshold (default: 0.7)"
    )

    # Output options
    parser.add_argument(
        "--output", "-o",
        help="Save results to JSON file"
    )
    parser.add_argument(
        "--quiet", "-q",
        action="store_true",
        help="Suppress iteration output"
    )
    parser.add_argument(
        "--verbose", "-v",
        action="store_true",
        help="Enable verbose logging"
    )

    args = parser.parse_args()

    # Configure logging
    if args.verbose:
        logging.basicConfig(
            level=logging.DEBUG,
            format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
        )
    else:
        logging.basicConfig(level=logging.WARNING)

    # Run the demo
    asyncio.run(run_demo(args))


if __name__ == "__main__":
    main()