Agent/agent/cli/interactive.py

"""
交互式控制器

提供暂停/继续、交互式菜单、经验总结等功能。
"""

import sys
import asyncio
from typing import Optional, Dict, Any
from pathlib import Path

from agent.core.runner import AgentRunner
from agent.trace import TraceStore


# ===== 非阻塞 stdin 检测 =====

if sys.platform == 'win32':
    import msvcrt


def check_stdin() -> Optional[str]:
    """
    跨平台非阻塞检查 stdin 输入。

    Windows: 使用 msvcrt.kbhit()
    macOS/Linux: 使用 select.select()

    Returns:
        'pause' | 'quit' | None
    """
    if sys.platform == 'win32':
        # Windows: 检查是否有按键按下
        if msvcrt.kbhit():
            ch = msvcrt.getwch().lower()
            if ch == 'p':
                return 'pause'
            if ch == 'q':
                return 'quit'
        return None
    else:
        # Unix/Mac: 使用 select
        import select
        ready, _, _ = select.select([sys.stdin], [], [], 0)
        if ready:
            line = sys.stdin.readline().strip().lower()
            if line in ('p', 'pause'):
                return 'pause'
            if line in ('q', 'quit'):
                return 'quit'
        return None


def read_multiline() -> str:
    """
    读取多行输入，以连续两次回车（空行）结束。

    Returns:
        用户输入的多行文本
    """
    print("\n请输入干预消息（连续输入两次回车结束）：")
    lines = []
    blank_count = 0

    while True:
        line = input()
        if line == "":
            blank_count += 1
            if blank_count >= 2:
                break
            lines.append("")  # 保留单个空行
        else:
            blank_count = 0
            lines.append(line)

    # 去掉尾部多余空行
    while lines and lines[-1] == "":
        lines.pop()

    return "\n".join(lines)


# ===== 交互式控制器 =====

class InteractiveController:
    """
    交互式控制器

    管理暂停/继续、交互式菜单、经验总结等交互功能。
    """

    def __init__(
        self,
        runner: AgentRunner,
        store: TraceStore,
        enable_stdin_check: bool = True
    ):
        """
        初始化交互式控制器

        Args:
            runner: Agent Runner 实例
            store: Trace Store 实例
            enable_stdin_check: 是否启用 stdin 检查
        """
        self.runner = runner
        self.store = store
        self.enable_stdin_check = enable_stdin_check

    def check_stdin(self) -> Optional[str]:
        """
        检查 stdin 输入

        Returns:
            'pause' | 'quit' | None
        """
        if not self.enable_stdin_check:
            return None
        return check_stdin()

    async def show_menu(
        self,
        trace_id: str,
        current_sequence: int
    ) -> Dict[str, Any]:
        """
        显示交互式菜单

        Args:
            trace_id: Trace ID
            current_sequence: 当前消息序号

        Returns:
            用户选择的操作
        """
        print("\n" + "=" * 60)
        print("  执行已暂停")
        print("=" * 60)
        print("请选择操作：")
        print("  1. 插入干预消息并继续")
        print("  2. 触发经验总结（reflect）")
        print("  3. 查看当前 GoalTree")
        print("  4. 手动压缩上下文（compact）")
        print("  5. 继续执行")
        print("  6. 停止执行")
        print("=" * 60)

        while True:
            choice = input("请输入选项 (1-6): ").strip()

            if choice == "1":
                # 插入干预消息
                text = read_multiline()
                if not text:
                    print("未输入任何内容，取消操作")
                    continue

                print(f"\n将插入干预消息并继续执行...")
                # 从 store 读取实际的 last_sequence
                live_trace = await self.store.get_trace(trace_id)
                actual_sequence = live_trace.last_sequence if live_trace and live_trace.last_sequence else current_sequence

                return {
                    "action": "continue",
                    "messages": [{"role": "user", "content": text}],
                    "after_sequence": actual_sequence,
                }

            elif choice == "2":
                # 触发经验总结
                print("\n触发经验总结...")
                focus = input("请输入反思重点（可选，直接回车跳过）: ").strip()
                await self.perform_reflection(trace_id, focus=focus)
                continue

            elif choice == "3":
                # 查看 GoalTree
                goal_tree = await self.store.get_goal_tree(trace_id)
                if goal_tree and goal_tree.goals:
                    print("\n当前 GoalTree:")
                    print(goal_tree.to_prompt())
                else:
                    print("\n当前没有 Goal")
                continue

            elif choice == "4":
                # 手动压缩上下文
                await self.manual_compact(trace_id)
                continue

            elif choice == "5":
                # 继续执行
                print("\n继续执行...")
                return {"action": "continue"}

            elif choice == "6":
                # 停止执行
                print("\n停止执行...")
                return {"action": "stop"}

            else:
                print("无效选项，请重新输入")

    async def perform_reflection(
        self,
        trace_id: str,
        focus: str = ""
    ):
        """
        执行经验总结

        Args:
            trace_id: Trace ID
            focus: 反思重点（可选）
        """
        from agent.core.prompts.knowledge import build_reflect_prompt
        from agent.core.runner import RunConfig

        trace = await self.store.get_trace(trace_id)
        if not trace:
            print("未找到 Trace")
            return

        saved_head = trace.head_sequence

        # 构建反思 prompt
        prompt = build_reflect_prompt()
        if focus:
            prompt += f"\n\n请特别关注：{focus}"

        print("正在生成反思...")
        reflect_cfg = RunConfig(trace_id=trace_id, max_iterations=10, tools=[])

        try:
            result = await self.runner.run_result(
                messages=[{"role": "user", "content": prompt}],
                config=reflect_cfg,
            )
            reflection_text = result.get("summary", "")

            if reflection_text:
                print("\n--- 反思内容 ---")
                print(reflection_text)
                print("--- 结束 ---\n")
            else:
                print("未生成反思内容")

        finally:
            # 恢复 head_sequence（反思消息成为侧枝，不污染主对话）
            await self.store.update_trace(trace_id, head_sequence=saved_head)

    async def manual_compact(self, trace_id: str):
        """
        手动压缩上下文

        Args:
            trace_id: Trace ID
        """
        from agent.core.runner import RunConfig

        print("\n正在执行上下文压缩（compact）...")

        try:
            goal_tree = await self.store.get_goal_tree(trace_id)
            trace = await self.store.get_trace(trace_id)

            if not trace:
                print("未找到 Trace，无法压缩")
                return

            # 重建当前 history
            main_path = await self.store.get_main_path_messages(trace_id, trace.head_sequence)
            history = [msg.to_llm_dict() for msg in main_path]
            head_seq = main_path[-1].sequence if main_path else 0
            next_seq = head_seq + 1

            compact_config = RunConfig(trace_id=trace_id)
            new_history, new_head, new_seq = await self.runner._compress_history(
                trace_id=trace_id,
                history=history,
                goal_tree=goal_tree,
                config=compact_config,
                sequence=next_seq,
                head_seq=head_seq,
            )

            print(f"\n✅ 压缩完成: {len(history)} 条消息 → {len(new_history)} 条")

        except Exception as e:
            print(f"\n❌ 压缩失败: {e}")