mirror of
https://github.com/val1813/kwcode.git
synced 2026-09-03 06:34:30 +08:00
7f2f74fbb6
- Add TaskCompiler (kaiwu/core/task_compiler.py): lightweight DAG scheduler with ThreadPoolExecutor + topological sort for serial/parallel multi-task - Add PromptOptimizer (kaiwu/flywheel/prompt_optimizer.py): analyzes trajectories via Opus/Sonnet API, appends learned rules to expert YAML system_prompt - Add Cross-Encoder reranker (kaiwu/search/reranker.py): optional BM25+CE pipeline - Add Reflexion persistence (kaiwu/memory/pattern_md.py): REFLECTION.md structured pattern memory with /plan integration - Remove Python expert system (ExpertBase, BugFixExpert.py, SelfImprovingOptimizer) - Revert registry to YAML-only loading - Remove _get_python_expert/_run_python_expert from orchestrator - Replace run_self_improvement with run_prompt_optimization in ab_tester - 277 tests passing (265 regression + 12 new task_compiler tests) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
346 lines
10 KiB
Python
346 lines
10 KiB
Python
"""
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PATTERN.md memory: high-frequency task patterns (flywheel data source).
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Spec §7.2: stored in .kaiwu/PATTERN.md.
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"""
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import json
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import logging
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import os
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import re
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from datetime import datetime
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from typing import Optional
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from kaiwu.core.context import TaskContext
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logger = logging.getLogger(__name__)
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PATTERN_MD_TEMPLATE = """# 高频任务模式
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> 飞轮数据源，自动维护
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## 模式统计
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| 任务类型 | 次数 | 成功率 | 平均耗时 | 最近触发 |
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|---------|------|--------|---------|---------|
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## 候选专家触发
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"""
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# Internal JSON sidecar for structured stats (markdown is for display)
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_STATS_FILENAME = ".pattern_stats.json"
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def _kaiwu_dir(project_root: str) -> str:
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return os.path.join(project_root, ".kaiwu")
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def _md_path(project_root: str) -> str:
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return os.path.join(_kaiwu_dir(project_root), "PATTERN.md")
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def _stats_path(project_root: str) -> str:
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return os.path.join(_kaiwu_dir(project_root), _STATS_FILENAME)
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def _ensure_dir(project_root: str):
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d = _kaiwu_dir(project_root)
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if not os.path.exists(d):
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os.makedirs(d, exist_ok=True)
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def _load_stats(project_root: str) -> dict:
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"""Load structured stats from JSON sidecar."""
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path = _stats_path(project_root)
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if not os.path.exists(path):
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return {}
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try:
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with open(path, "r", encoding="utf-8") as f:
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return json.load(f)
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except Exception:
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return {}
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def _save_stats(project_root: str, stats: dict):
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"""Save structured stats to JSON sidecar."""
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_ensure_dir(project_root)
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path = _stats_path(project_root)
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try:
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with open(path, "w", encoding="utf-8") as f:
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json.dump(stats, f, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.warning("Failed to write pattern stats: %s", e)
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def _rebuild_markdown(project_root: str, stats: dict):
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"""Rebuild PATTERN.md from structured stats."""
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_ensure_dir(project_root)
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lines = [
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"# 高频任务模式",
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"> 飞轮数据源，自动维护",
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"",
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"## 模式统计",
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"| 任务类型 | 次数 | 成功率 | 平均耗时 | 最近触发 |",
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"|---------|------|--------|---------|---------|",
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]
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# Sort by count descending
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sorted_types = sorted(stats.items(), key=lambda x: x[1].get("count", 0), reverse=True)
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for task_type, data in sorted_types:
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count = data.get("count", 0)
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success = data.get("success", 0)
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rate = f"{success / count * 100:.0f}%" if count > 0 else "0%"
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total_elapsed = data.get("total_elapsed", 0.0)
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avg_elapsed = f"{total_elapsed / count:.1f}s" if count > 0 else "N/A"
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last_trigger = data.get("last_trigger", "N/A")
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lines.append(f"| {task_type} | {count} | {rate} | {avg_elapsed} | {last_trigger} |")
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lines.append("")
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lines.append("## 候选专家触发")
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# Auto-detect candidates: >=5 tasks with 100% success
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for task_type, data in sorted_types:
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count = data.get("count", 0)
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success = data.get("success", 0)
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if count >= 5 and success == count:
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avg_elapsed = data.get("total_elapsed", 0.0) / count
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lines.append(f"- {task_type}: {count}次全部成功，平均{avg_elapsed:.1f}s")
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# Recent failures section
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has_failures = any(d.get("recent_failures") for _, d in sorted_types)
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if has_failures:
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lines.append("")
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lines.append("## 近期失败模式")
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for task_type, data in sorted_types:
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failures = data.get("recent_failures", [])
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if failures:
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for f in failures[-5:]: # Show last 5 per type in markdown
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lines.append(f"- {task_type} {f}")
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lines.append("")
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path = _md_path(project_root)
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try:
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with open(path, "w", encoding="utf-8") as f:
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f.write("\n".join(lines))
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except Exception as e:
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logger.warning("Failed to write PATTERN.md: %s", e)
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# ── Public API ──
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def load(project_root: str) -> str:
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"""Return pattern summary as injectable context."""
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path = _md_path(project_root)
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if not os.path.exists(path):
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return ""
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try:
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with open(path, "r", encoding="utf-8") as f:
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return f.read()
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except Exception as e:
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logger.warning("Failed to read PATTERN.md: %s", e)
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return ""
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def update(project_root: str, ctx: TaskContext, success: bool, elapsed: float = 0.0):
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"""Update pattern stats after every task (success or failure)."""
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expert_type = ctx.gate_result.get("expert_type", "unknown")
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now = datetime.now().strftime("%Y-%m-%d %H:%M")
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stats = _load_stats(project_root)
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if expert_type not in stats:
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stats[expert_type] = {
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"count": 0,
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"success": 0,
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"total_elapsed": 0.0,
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"last_trigger": "",
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"recent_failures": [],
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}
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entry = stats[expert_type]
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entry["count"] += 1
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if success:
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entry["success"] += 1
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else:
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# Record failure mode for future reference
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error_detail = ""
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if ctx.verifier_output:
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error_detail = ctx.verifier_output.get("error_detail", "")
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failure_record = f"[{now}] {error_detail[:100]}"
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failures = entry.get("recent_failures", [])
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failures.append(failure_record)
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# Keep only last 10 failures
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entry["recent_failures"] = failures[-10:]
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entry["total_elapsed"] += elapsed
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entry["last_trigger"] = now
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_save_stats(project_root, stats)
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_rebuild_markdown(project_root, stats)
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logger.info("Updated PATTERN.md for %s (success=%s)", expert_type, success)
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def get_pattern_stats(project_root: str) -> list[dict]:
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"""Return structured stats for flywheel consumption."""
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stats = _load_stats(project_root)
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result = []
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for task_type, data in stats.items():
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count = data.get("count", 0)
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success = data.get("success", 0)
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result.append({
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"task_type": task_type,
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"count": count,
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"success_rate": success / count if count > 0 else 0.0,
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"avg_elapsed": data.get("total_elapsed", 0.0) / count if count > 0 else 0.0,
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"last_trigger": data.get("last_trigger", ""),
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})
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return sorted(result, key=lambda x: x["count"], reverse=True)
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def count_similar_failures(expert_type: str, keywords: list[str],
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project_root: str) -> int:
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"""
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Count historical failures similar to the given task.
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Simple keyword match against recent_failures, no LLM call.
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Used by Planner for risk assessment.
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"""
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stats = _load_stats(project_root)
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entry = stats.get(expert_type, {})
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failures = entry.get("recent_failures", [])
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count = 0
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for line in failures:
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if any(kw in line for kw in keywords if len(kw) > 1):
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count += 1
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return count
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def show(project_root: str) -> str:
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"""Display PATTERN.md content."""
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path = _md_path(project_root)
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if not os.path.exists(path):
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return "PATTERN.md not found. Will be created after first task."
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try:
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with open(path, "r", encoding="utf-8") as f:
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return f.read()
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except Exception as e:
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return f"Failed to read PATTERN.md: {e}"
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# ── Reflexion持久化 (SE-RED-5: 结构化格式) ──
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REFLECTION_TEMPLATE = "- [{date}] {task_summary} → {reflection}\n"
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_REFLECTION_FILE = "REFLECTION.md"
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def _reflection_path(project_root: str) -> str:
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return os.path.join(_kaiwu_dir(project_root), _REFLECTION_FILE)
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def _read_reflection(project_root: str) -> str:
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path = _reflection_path(project_root)
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if not os.path.exists(path):
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return "# KWCode Pattern Memory\n"
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try:
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with open(path, "r", encoding="utf-8") as f:
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return f.read()
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except Exception:
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return "# KWCode Pattern Memory\n"
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def _write_reflection(project_root: str, content: str):
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_ensure_dir(project_root)
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path = _reflection_path(project_root)
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try:
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with open(path, "w", encoding="utf-8") as f:
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f.write(content)
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except Exception as e:
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logger.warning("Failed to write REFLECTION.md: %s", e)
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def save_reflection(
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project_root: str,
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expert_type: str,
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task_summary: str,
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reflection: str,
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success: bool,
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):
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"""
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把Reflection结果持久化到REFLECTION.md。
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SE-RED-5：结构化格式，不是自由文本。
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"""
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from datetime import date as date_mod
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# 根据成败选择section
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if success:
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section = f"## {expert_type} 注意事项"
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prefix = "注意"
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else:
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section = f"## {expert_type} 失败模式"
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prefix = "根因"
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entry = REFLECTION_TEMPLATE.format(
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date=date_mod.today().isoformat(),
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task_summary=task_summary[:30],
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reflection=f"{prefix}：{reflection[:80]}",
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)
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content = _read_reflection(project_root)
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# 找到对应section，追加条目
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if section in content:
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content = content.replace(
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section + "\n",
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section + "\n" + entry,
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)
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else:
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content += f"\n{section}\n{entry}"
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# 限制每个section最多20条（防止无限增长）
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content = _trim_reflection_sections(content, max_entries=20)
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_write_reflection(project_root, content)
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def get_reflections_for_plan(project_root: str, expert_type: str) -> str:
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"""
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/plan时读取相关历史Reflection，作为风险提示。
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返回最近5条相关记录。
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"""
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content = _read_reflection(project_root)
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sections = [
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f"## {expert_type} 失败模式",
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f"## {expert_type} 注意事项",
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f"## {expert_type} 风险点",
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]
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result = []
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for section in sections:
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if section in content:
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after = content.split(section)[1].split("##")[0].strip()
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recent = "\n".join(after.splitlines()[:5])
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if recent:
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result.append(f"{section}\n{recent}")
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return "\n\n".join(result)
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def _trim_reflection_sections(content: str, max_entries: int) -> str:
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"""每个section只保留最新的max_entries条。"""
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lines = content.splitlines()
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result = []
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current_section_entries = 0
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in_section = False
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for line in lines:
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if line.startswith("## "):
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in_section = True
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current_section_entries = 0
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result.append(line)
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elif in_section and line.startswith("- ["):
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current_section_entries += 1
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if current_section_entries <= max_entries:
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result.append(line)
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else:
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result.append(line)
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return "\n".join(result)
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