mirror of
https://github.com/val1813/kwcode.git
synced 2026-09-03 06:34:30 +08:00
0a386f5fae
CLI restructure: - kaiwu/cli/main.py: only app registration + entry point (173 lines) - kaiwu/cli/commands/task.py: run/chat/vision/multi-task commands - kaiwu/cli/commands/expert.py: expert list/info/export/install/remove - kaiwu/cli/commands/config.py: init/api/model/setup-search/serve - kaiwu/cli/formatters.py: Rich output formatting (spinner, summary, header) Expert-level EventBus emit: - locator.py: emit file reads and method type - event_bus.py: singleton get_instance() for expert access - orchestrator.py: pass bus to experts for fine-grained events 451/451 tests green. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
487 lines
21 KiB
Python
487 lines
21 KiB
Python
"""
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Interactive REPL loop for KwCode CLI.
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"""
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import os
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import time
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from rich.panel import Panel
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from kaiwu.cli.formatters import VERSION, console, escape_html, render_header
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from kaiwu.cli.commands.config import handle_api_command, maybe_show_weekly_stats
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from kaiwu.cli.commands.task import (
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build_pipeline,
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run_task,
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handle_multi_command,
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)
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# ── REPL commands ─────────────────────────────────────────────
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REPL_COMMANDS = {
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"/help": "显示帮助",
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"/memory": "查看项目记忆 (KAIWU.md)",
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"/init": "初始化 KWCODE.md + KAIWU.md",
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"/model": "切换模型 (用法: /model qwen3-8b)",
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"/cd": "切换项目目录 (用法: /cd /path/to/project)",
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"/experts": "列出已注册专家",
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"/plan": "计划模式 (用法: /plan <任务> 或 /plan 后输入任务)",
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"/multi": "多任务模式 (用法: /multi 后按提示输入多个任务)",
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"/api": "API配置 (用法: /api show | /api temp <url> | /api default <url>)",
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"/paste": "从剪贴板粘贴图片",
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"/image": "添加图片文件 (用法: /image <path>)",
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"/stats": "查看任务统计和Gate准确率",
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"/exit": "退出",
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}
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class SessionState:
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"""Tracks session state for multi-turn coherence and System Reminders."""
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def __init__(self):
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self.tasks_this_session: list[dict] = []
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self.files_touched: set = set()
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self.turn_count: int = 0
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def record_task(self, user_input: str, success: bool, files: list[str], elapsed: float):
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"""Record a completed task."""
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self.turn_count += 1
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self.tasks_this_session.append({
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"input": user_input[:100],
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"success": success,
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"files": files[:5],
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"elapsed": elapsed,
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})
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self.files_touched.update(files)
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def to_reminder(self) -> str:
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"""Generate System Reminder text for injection into Gate memory_context."""
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if not self.tasks_this_session:
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return ""
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recent = self.tasks_this_session[-3:]
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lines = ["[本次会话已完成]"]
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for t in recent:
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status = "OK" if t["success"] else "FAIL"
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files_str = ", ".join(t["files"][:2]) if t["files"] else ""
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line = f"- [{status}] {t['input'][:40]}"
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if files_str:
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line += f" → {files_str}"
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lines.append(line)
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if self.files_touched:
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touched = list(self.files_touched)[:5]
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lines.append(f"[已修改文件] {', '.join(touched)}")
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return "\n".join(lines)
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def _resolve_image_path(path: str, project_root: str) -> str:
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"""Resolve /image paths relative to the active project directory."""
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expanded = os.path.expanduser(path)
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if not os.path.isabs(expanded):
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expanded = os.path.join(project_root, expanded)
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return os.path.abspath(expanded)
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def repl(model_path, ollama_url, ollama_model, project_root, verbose, no_search=False):
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"""Interactive REPL loop with prompt_toolkit bottom_toolbar."""
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from kaiwu.memory.kaiwu_md import KaiwuMemory
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from kaiwu.core.sysinfo import get_sysinfo, VRAMWatcher
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from kaiwu.core.context_pruner import ContextPruner
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from kaiwu.cli.status_bar import StatusBar, TokPerSecEstimator
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from prompt_toolkit import PromptSession
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from prompt_toolkit.formatted_text import HTML
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from prompt_toolkit.completion import Completer, Completion
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gate, orchestrator, memory, registry = build_pipeline(
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model_path=model_path,
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ollama_url=ollama_url,
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ollama_model=ollama_model,
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project_root=project_root,
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verbose=verbose,
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)
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# P2: Model capability detection
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from kaiwu.core.model_capability import detect_model_tier, get_strategy, tier_display_name
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model_tier = detect_model_tier(ollama_model, ollama_url)
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model_strategy = get_strategy(model_tier)
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orchestrator._model_name = ollama_model
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# P2: Flywheel notifier
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from kaiwu.notification.flywheel_notifier import FlywheelNotifier
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notifier = FlywheelNotifier()
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# Hardware info (once at startup)
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sysinfo = get_sysinfo()
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# Render header
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render_header(ollama_model, project_root, registry)
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# P2: Show model tier info
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tier_name = tier_display_name(model_tier)
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if model_tier.value == "small":
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console.print(
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f" [yellow][{tier_name}][/yellow] "
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f"已启用计划确认 · 任务范围≤{model_strategy.max_files_per_task}文件 · "
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f"第{model_strategy.search_trigger_after}次失败触发搜索"
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)
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elif model_tier.value == "large":
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console.print(f" [green][{tier_name}][/green] 允许更大范围任务")
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# P2: Weekly stats hint
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maybe_show_weekly_stats(console)
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# Init status bar
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status = StatusBar()
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status.model = ollama_model
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status.ctx_max = 8192
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status.vram_used = sysinfo.vram_used_gb
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status.vram_total = sysinfo.vram_total_gb
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status.ram_used = sysinfo.ram_used_gb
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status.ram_total = sysinfo.ram_total_gb
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tps_estimator = TokPerSecEstimator()
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pruner = ContextPruner(max_tokens=status.ctx_max)
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conversation_history: list[dict] = []
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session_state = SessionState()
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# Background VRAM watcher
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vram_watcher = VRAMWatcher(status)
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vram_watcher.start()
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# prompt_toolkit session with bottom_toolbar
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from prompt_toolkit.styles import Style as PTStyle
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_pt_style = PTStyle.from_dict({
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'bottom-toolbar': 'bg:#1a1a1a #666666 noreverse',
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})
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def _toolbar():
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status.refresh_ram()
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width = console.width
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bar = escape_html(status.render(width))
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return HTML(f'<style bg="#1a1a1a" fg="#666666">{bar}</style>')
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# Slash command completer — 输入/后弹出命令菜单
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class SlashCompleter(Completer):
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def get_completions(self, document, complete_event):
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text = document.text_before_cursor.lstrip()
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if not text.startswith("/"):
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return
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for cmd, desc in REPL_COMMANDS.items():
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if cmd.startswith(text):
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yield Completion(cmd, start_position=-len(text), display_meta=desc)
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session = PromptSession(completer=SlashCompleter(), style=_pt_style)
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plan_next = False
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task_count = 0
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while True:
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# P2-RED-2: Show pending flywheel notifications before next task
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notifier.flush(console)
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try:
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user_input = session.prompt(
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" > ",
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bottom_toolbar=_toolbar,
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).strip()
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except (KeyboardInterrupt, EOFError):
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console.print("\n [dim]bye[/dim]")
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break
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if not user_input:
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continue
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# ── Slash commands ──
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if user_input.startswith("/"):
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parts = user_input.split(maxsplit=1)
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cmd = parts[0].lower()
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arg = parts[1] if len(parts) > 1 else ""
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if cmd in ("/exit", "/quit", "/q"):
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console.print(" [dim]bye[/dim]")
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break
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elif cmd == "/help":
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for k, v in REPL_COMMANDS.items():
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console.print(f" [cyan]{k:10s}[/cyan] {v}")
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elif cmd == "/memory":
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content = memory.show(project_root)
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console.print(Panel(content, title="KAIWU.md", border_style="blue"))
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elif cmd == "/init":
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# Generate KWCODE.md template
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from kaiwu.core.kwcode_md import generate_kwcode_template
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result = generate_kwcode_template(project_root)
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console.print(f" {result}")
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# Also init KAIWU.md if needed
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result2 = memory.init(project_root)
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console.print(f" {result2}")
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elif cmd == "/model":
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if not arg:
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console.print(f" 当前模型: {ollama_model}")
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console.print(" 用法: /model qwen3-8b")
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else:
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ollama_model = arg.strip()
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console.print(f" [green]模型切换为: {ollama_model}[/green]")
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console.print(" [dim]重建流水线...[/dim]")
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gate, orchestrator, memory, registry = build_pipeline(
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model_path=model_path,
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ollama_url=ollama_url,
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ollama_model=ollama_model,
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project_root=project_root,
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verbose=verbose,
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)
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status.model = ollama_model
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# 持久化到config，下次启动自动用新模型
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from kaiwu.cli.onboarding import load_config, _save_config
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cfg = load_config()
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cfg.setdefault("default", {})
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cfg["default"]["model"] = ollama_model
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_save_config(cfg)
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elif cmd == "/cd":
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if not arg:
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console.print(f" 当前项目: {project_root}")
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console.print(" 用法: /cd /path/to/project")
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else:
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new_root = os.path.abspath(arg.strip())
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if os.path.isdir(new_root):
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project_root = new_root
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console.print(f" [green]项目切换为: {project_root}[/green]")
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# 重建 tools（project_root 变了）
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gate, orchestrator, memory, registry = build_pipeline(
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model_path=model_path,
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ollama_url=ollama_url,
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ollama_model=ollama_model,
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project_root=project_root,
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verbose=verbose,
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)
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else:
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console.print(f" [red]❌ 目录不存在[/red]")
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console.print(f" [yellow]路径：[/yellow]{new_root}")
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console.print("\n [cyan]💡 提示：[/cyan]")
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console.print(" • 检查路径拼写是否正确")
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console.print(" • 使用绝对路径或相对路径")
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console.print(f" • 当前目录：[dim]{os.getcwd()}[/dim]")
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console.print(" • 示例：[dim]/cd ~/projects/myapp[/dim]")
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elif cmd == "/experts":
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experts = registry.list_experts()
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console.print(f" [cyan]已注册专家: {len(experts)}[/cyan]")
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for e in experts:
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lc = e.get("lifecycle", "new")
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perf = e.get("performance", {})
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cnt = perf.get("task_count", 0)
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sr = perf.get("success_rate", 0.0)
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kws = ", ".join(e["trigger_keywords"][:4])
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console.print(f" [bold]{e['name']}[/bold] [{lc}] tasks={cnt} sr={sr:.0%} kw=[{kws}]")
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elif cmd == "/stats":
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from kaiwu.stats.value_tracker import ValueTracker
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tracker = ValueTracker()
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summary = tracker.get_summary(days=30)
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gate_acc = tracker.get_gate_accuracy(days=30)
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console.print("\n [bold]任务统计（近30天）[/bold]")
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console.print(f" 总任务: {summary['total_tasks']} | 成功: {summary['succeeded_tasks']} | "
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f"成功率: {summary['succeeded_tasks']/max(summary['total_tasks'],1)*100:.0f}%")
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console.print(f" 节省时间: ~{summary['time_saved_hours']}h | 累计: {summary['total_all_time']} 个任务")
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if gate_acc:
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console.print("\n [bold]Gate路由准确率[/bold]")
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console.print(" [dim]类型 总数 成功率 平均耗时 平均重试[/dim]")
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for g in gate_acc:
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sr = f"{g['success_rate']*100:.0f}%"
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console.print(
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f" {g['expert_type']:14s} {g['total']:4d} {sr:>5s} "
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f"{g['avg_elapsed']:5.1f}s {g['avg_retries']:.1f}次"
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)
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else:
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console.print("\n [dim]暂无Gate路由数据（需要执行几个任务后才有统计）[/dim]")
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console.print()
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elif cmd == "/plan":
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if arg:
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pending_images = list(getattr(session, '_pending_images', []))
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if pending_images:
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console.print(f" [cyan]图片上下文: {len(pending_images)} 张图片[/cyan]")
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# /plan <任务描述> → 直接以plan模式执行
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task_count += 1
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conversation_history.append({"role": "user", "content": arg})
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t0 = time.perf_counter()
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success = run_task(
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task=arg, gate=gate, orchestrator=orchestrator,
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memory=memory, project_root=project_root,
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verbose=verbose, plan=True, no_search=no_search,
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image_paths=pending_images if pending_images else None,
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)
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if pending_images:
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session._pending_images = []
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elapsed = time.perf_counter() - t0
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tps_estimator.record("x" * int(elapsed * 15), elapsed)
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status.tok_per_sec = tps_estimator.value
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conversation_history.append({"role": "assistant", "content": arg[:500]})
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status.ctx_used = pruner.estimate_total(conversation_history)
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else:
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plan_next = True
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console.print(" [dim]下一个任务将先显示计划[/dim]")
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elif cmd == "/multi":
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handle_multi_command(arg, gate, orchestrator, project_root, console)
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elif cmd == "/paste":
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from kaiwu.experts.vision_expert import save_clipboard_image
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image_path = save_clipboard_image()
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if image_path:
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console.print(f" [green]图片已从剪贴板保存: {image_path}[/green]")
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console.print(" [dim]现在可以输入任务描述，图片将作为上下文[/dim]")
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# 存储图片路径供后续任务使用
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if not hasattr(session, '_pending_images'):
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session._pending_images = []
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session._pending_images.append(image_path)
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else:
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console.print(" [yellow]剪贴板中没有图片[/yellow]")
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elif cmd == "/image":
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if not arg:
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console.print(" [yellow]用法: /image <图片路径>[/yellow]")
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else:
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from kaiwu.experts.vision_expert import validate_image_path
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image_path = _resolve_image_path(arg.strip(), project_root)
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if validate_image_path(image_path):
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console.print(f" [green]图片已添加: {image_path}[/green]")
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console.print(" [dim]现在可以输入任务描述，图片将作为上下文[/dim]")
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# 存储图片路径供后续任务使用
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if not hasattr(session, '_pending_images'):
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session._pending_images = []
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session._pending_images.append(image_path)
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else:
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console.print(f" [red]图片文件不存在或格式不支持: {image_path}[/red]")
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elif cmd == "/api":
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api_parts = user_input.split()
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result = handle_api_command(api_parts, ollama_url, ollama_model)
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if result:
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# /api temp or /api default changed the URL, rebuild pipeline
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ollama_url = result.get("url", ollama_url)
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gate, orchestrator, memory, registry = build_pipeline(
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model_path=model_path,
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ollama_url=ollama_url,
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ollama_model=ollama_model,
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project_root=project_root,
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verbose=verbose,
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)
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status.model = ollama_model
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else:
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console.print(f" [yellow]未知命令: {cmd}[/yellow] 输入 /help 查看帮助")
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continue
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# ── Execute task ──
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task_count += 1
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# Track conversation for context pruning
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conversation_history.append({"role": "user", "content": user_input})
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# Check if context needs pruning before task
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if pruner.needs_pruning(conversation_history):
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conversation_history = pruner.prune(conversation_history)
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status.compress_count = pruner.compress_count
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console.print(
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f" [dim]context已压缩（第{pruner.compress_count}次，"
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f"耗时{pruner._last_compress_ms:.1f}ms）[/dim]"
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)
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# 处理待处理的图片
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pending_images = getattr(session, '_pending_images', [])
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if pending_images:
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console.print(f" [cyan]图片上下文: {len(pending_images)} 张图片[/cyan]")
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for img_path in pending_images:
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console.print(f" - {img_path}")
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t0 = time.perf_counter()
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# P2: Small model forces plan mode (问题6修复：用户可通过 no_search 间接控制)
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effective_plan = plan_next or (model_strategy.force_plan_mode and not no_search)
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# Inject session reminder into memory context for Gate
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session_reminder = session_state.to_reminder()
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if session_reminder and session_state.turn_count % 5 == 0:
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# Every 5 turns, also re-inject KWCODE.md core rules (attention decay countermeasure)
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from kaiwu.core.kwcode_md import load_kwcode_md, build_kwcode_system
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kwcode_sections = load_kwcode_md(project_root)
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if kwcode_sections and "all" in kwcode_sections:
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session_reminder += f"\n\n[项目规则提醒]\n{kwcode_sections['all'][:500]}"
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success = run_task(
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task=user_input,
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gate=gate,
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||
orchestrator=orchestrator,
|
||
memory=memory,
|
||
project_root=project_root,
|
||
verbose=verbose,
|
||
plan=effective_plan,
|
||
no_search=no_search,
|
||
image_paths=pending_images if pending_images else None,
|
||
)
|
||
|
||
# 清除已使用的图片
|
||
if pending_images:
|
||
session._pending_images = []
|
||
|
||
elapsed = time.perf_counter() - t0
|
||
plan_next = False # Reset plan flag
|
||
|
||
# Record task in session state
|
||
task_files = []
|
||
if success:
|
||
try:
|
||
last_result = getattr(orchestrator, '_last_result', None)
|
||
if last_result and last_result.get("context"):
|
||
ctx = last_result["context"]
|
||
if ctx.generator_output:
|
||
task_files = [p.get("file", "") for p in ctx.generator_output.get("patches", [])]
|
||
except Exception:
|
||
pass
|
||
session_state.record_task(user_input, success, task_files, elapsed)
|
||
|
||
# Update tok/s estimator (rough: use elapsed as proxy)
|
||
tps_estimator.record("x" * int(elapsed * 15), elapsed) # ~15 tok/s estimate
|
||
status.tok_per_sec = tps_estimator.value
|
||
|
||
# Update ctx usage with actual task result (问题7修复：存真实输出而非user_input)
|
||
assistant_content = ""
|
||
if success:
|
||
# 尝试从orchestrator获取真实输出
|
||
try:
|
||
last_result = getattr(orchestrator, '_last_result', None)
|
||
if last_result and last_result.get("context"):
|
||
ctx = last_result["context"]
|
||
if ctx.generator_output and ctx.generator_output.get("explanation"):
|
||
assistant_content = ctx.generator_output["explanation"][:500]
|
||
except Exception:
|
||
pass
|
||
if not assistant_content:
|
||
assistant_content = f"[任务{'成功' if success else '失败'}] {user_input[:100]}"
|
||
conversation_history.append({"role": "assistant", "content": assistant_content})
|
||
status.ctx_used = pruner.estimate_total(conversation_history)
|
||
status.model = ollama_model
|
||
|
||
# Cleanup
|
||
vram_watcher.stop()
|
||
|
||
# P4: 会话连续性 — 保存SESSION.md
|
||
try:
|
||
from kaiwu.memory.session_md import save_session
|
||
tasks_done = []
|
||
for msg in conversation_history:
|
||
if msg.get("role") == "assistant":
|
||
content = msg.get("content", "")
|
||
success = not content.startswith("[任务失败")
|
||
tasks_done.append({"input": content[:50], "success": success, "files": [], "elapsed": 0})
|
||
if tasks_done:
|
||
save_session(project_root, tasks_done[-10:])
|
||
except Exception:
|
||
pass
|