renjianbo
beff3fac8d
- Fix delete agent 500: clean up FK records (agent_llm_logs, permissions, schedules, executions, team_members) and unbind goals/tasks before delete - Remove hardcoded personality templates in Android, replace with dynamic system prompt generation from name + description - Set promptSectionsEnabled=false to bypass PromptComposer for personality - Add Tencent Cloud Linux deployment guide (Docker Compose) - Accumulated backend service updates, frontend UI fixes, Android app changes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
498 lines
20 KiB
Python
498 lines
20 KiB
Python
"""
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对话自动压缩引擎 — 三级压缩体系
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参考 Claude Code:
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- src/services/compact/microCompact.ts — Tier 1: 工具结果打桩
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- src/services/compact/compact.ts — Tier 2: LLM 摘要替换
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- src/services/compact/reactiveCompact.ts — Tier 3: 错误触发压缩
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- src/services/compact/grouping.ts — 安全分割点识别
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"""
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from __future__ import annotations
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import logging
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import time
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from enum import Enum
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from typing import Any, Dict, List, Optional, Tuple
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from app.core.token_counter import (
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TokenCounter,
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get_model_context_window,
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is_context_length_error,
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)
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from app.core.compaction_config import CompactionConfig
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logger = logging.getLogger(__name__)
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# ──────────────────────────── 数据结构 ────────────────────────────
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class CompactionStrategy(str, Enum):
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NONE = "none"
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MICRO = "micro" # Tier 1: 工具结果打桩
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FULL = "full" # Tier 2: LLM 摘要替换
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REACTIVE = "reactive" # Tier 3: 错误触发
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class CompactionResult:
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"""压缩操作结果。"""
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def __init__(
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self,
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messages: List[Dict[str, Any]],
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strategy: CompactionStrategy,
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tokens_before: int,
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tokens_after: int,
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details: Optional[str] = None,
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):
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self.messages = messages
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self.strategy = strategy
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self.tokens_before = tokens_before
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self.tokens_after = tokens_after
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self.tokens_saved = tokens_before - tokens_after
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self.details = details
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self.timestamp = time.time()
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def __repr__(self) -> str:
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return (
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f"CompactionResult(strategy={self.strategy.value}, "
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f"saved={self.tokens_saved} tokens, "
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f"before={self.tokens_before} after={self.tokens_after})"
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)
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# ──────────────────────────── 压缩摘要提示词 ────────────────────────────
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COMPACT_SUMMARY_SYSTEM = """你是一个对话摘要专家。你需要将一段 AI 助手与用户的对话历史压缩为简洁的摘要。
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规则：
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- 保留关键事实和决策（文件路径、数值、结论、用户偏好）
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- 保留未完成的任务或待处理事项
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- 忽略纯粹的问候、闲聊和中间工具调用细节
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- 用第三人称中文描述
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- 控制在你被要求的字数范围内
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返回格式：直接返回摘要文本，不要加任何前缀或标记。"""
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def _build_compact_user_prompt(older_messages: List[Dict[str, Any]], max_chars: int = 2000) -> str:
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"""从旧消息中构建压缩提示词。"""
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parts = []
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total_chars = 0
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for msg in older_messages:
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role = msg.get("role", "?")
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content = msg.get("content", "") or ""
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# 截断长内容
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if len(content) > 500:
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content = content[:500] + "..."
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line = f"[{role}]: {content}"
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if total_chars + len(line) > max_chars:
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parts.append("...(更早的消息已省略)")
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break
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parts.append(line)
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total_chars += len(line)
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return "\n".join(parts)
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# ──────────────────────────── CompactionEngine ────────────────────────────
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class CompactionEngine:
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"""三级对话压缩引擎。
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与 Claude Code 一样，在每轮 LLM 调用前检查 token 用量：
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- >70% → MicroCompact（旧工具结果打桩）
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- >85% → FullCompact（LLM 摘要替换）
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- >95% → 等 API 报错后 ReactiveCompact
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"""
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def __init__(
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self,
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config: CompactionConfig,
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token_counter: Optional[TokenCounter] = None,
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model: str = "deepseek-v4-flash",
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):
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self.config = config
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self.token_counter = token_counter or TokenCounter(model=model)
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self.model = model
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# 熔断状态
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self._consecutive_failures = 0
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self._last_compact_time: float = 0
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self._compact_count = 0
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# ──────────────────── 入口 ────────────────────
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async def maybe_compact(
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self,
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messages: List[Dict[str, Any]],
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context_window: Optional[int] = None,
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) -> CompactionResult:
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"""根据当前 token 用量决定是否压缩，返回（可能压缩后的）消息列表。
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Args:
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messages: 当前消息列表 (含 system prompt)
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context_window: 模型上下文窗口大小（None=自动检测）
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Returns:
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CompactionResult，包含（可能压缩后的）消息列表
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"""
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if not self.config.enabled:
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return CompactionResult(
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messages, CompactionStrategy.NONE,
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tokens_before=0, tokens_after=0,
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details="压缩已禁用",
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)
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# 确定上下文窗口
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if context_window is None:
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context_window = get_model_context_window(self.model)
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if self.config.context_window_override > 0:
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context_window = self.config.context_window_override
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# 有效窗口 = 模型窗口 - 输出余量
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effective_window = context_window - self.config.output_reserve_tokens
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# 计算当前 token 数
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tokens_before = self.token_counter.count_messages(messages)
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usage_ratio = tokens_before / effective_window if effective_window > 0 else 0
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# ── 决策 ──
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# Tier 1: MicroCompact
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if (
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self.config.micro_compact_enabled
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and usage_ratio >= self.config.micro_compact_threshold
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and usage_ratio < self.config.full_compact_threshold
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):
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return await self._micro_compact(messages, tokens_before)
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# Tier 2: FullCompact
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if (
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self.config.full_compact_enabled
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and usage_ratio >= self.config.full_compact_threshold
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):
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return await self._full_compact(messages, tokens_before)
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# 不需要压缩
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return CompactionResult(
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messages, CompactionStrategy.NONE,
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tokens_before=tokens_before, tokens_after=tokens_before,
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details=f"usage={usage_ratio:.1%} < threshold={self.config.micro_compact_threshold:.0%}",
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)
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async def reactive_compact(
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self,
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messages: List[Dict[str, Any]],
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error: Exception,
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context_window: Optional[int] = None,
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) -> CompactionResult:
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"""响应 API 上下文超限错误的紧急压缩（Tier 3）。
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Args:
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messages: 当前消息列表
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error: 触发的异常
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context_window: 上下文窗口大小
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Returns:
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CompactionResult
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"""
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if not self.config.reactive_compact_enabled:
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raise error
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if context_window is None:
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context_window = get_model_context_window(self.model)
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tokens_before = self.token_counter.count_messages(messages)
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logger.warning(
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"ReactiveCompact 触发: %s, tokens=%d, window=%d",
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str(error)[:100], tokens_before, context_window,
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)
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return await self._full_compact(messages, tokens_before, is_reactive=True)
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# ──────────────────── Tier 1: MicroCompact ────────────────────
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async def _micro_compact(
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self, messages: List[Dict[str, Any]], tokens_before: int
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) -> CompactionResult:
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"""MicroCompact: 将旧工具结果替换为桩标记。
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核心逻辑（参考 Claude Code microCompact.ts）：
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1. 找到所有 "可压缩" 工具类型的结果消息
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2. 保留最近 N 轮的工具结果不动
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3. 更早的工具结果 → 替换 content 为 "[Tool result compacted]"
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4. 保护 assistant(tool_calls) 消息 — 它们包含推理链
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5. 保护破坏性工具结果（write/edit/deploy 等）
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"""
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try:
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compactable = set(self.config.compactable_tools)
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protected = set(self.config.protected_tools)
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# ── 第 1 步: 识别各消息角色 ──
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# 从后往前数 user 消息来确定"轮次"
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user_indices = []
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for i, msg in enumerate(messages):
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if msg.get("role") == "user":
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user_indices.append(i)
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if len(user_indices) <= self.config.min_preserve_messages // 2:
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# 对话轮次太少，不需要压缩
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return CompactionResult(
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messages, CompactionStrategy.MICRO,
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tokens_before=tokens_before, tokens_after=tokens_before,
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details="对话轮次不足，跳过 MicroCompact",
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)
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# 找到"保护线"：倒数第 compact_older_than_rounds 个 user 消息的位置
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preserve_idx = max(0, len(user_indices) - self.config.compact_older_than_rounds)
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compact_before = user_indices[preserve_idx] if preserve_idx < len(user_indices) else 0
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# ── 第 2 步: 识别 tool_call → tool_result 配对 ──
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# 收集 assistant(tool_calls) 的 tool_call_id 集合
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active_tool_ids = set()
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for msg in messages:
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if msg.get("role") == "assistant" and msg.get("tool_calls"):
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for tc in msg["tool_calls"]:
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tc_id = tc.get("id") or tc.get("tool_call_id")
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if tc_id:
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active_tool_ids.add(tc_id)
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# ── 第 3 步: 在保护线之前压缩可压缩工具结果 ──
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stubbed_count = 0
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result = []
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for i, msg in enumerate(messages):
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if i >= compact_before:
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# 在保护线之后，保留原样
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result.append(msg)
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continue
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role = msg.get("role", "")
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tool_name = msg.get("name", "")
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if role == "tool" and tool_name in compactable and tool_name not in protected:
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# 检查是否有对应的 assistant(tool_calls) 也早于保护线
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tc_id = msg.get("tool_call_id", "")
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result.append({
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"role": "tool",
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"tool_call_id": tc_id or "compacted",
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"content": "[Tool result compacted]",
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"name": tool_name,
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})
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stubbed_count += 1
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else:
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result.append(msg)
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if stubbed_count == 0:
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return CompactionResult(
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result, CompactionStrategy.MICRO,
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tokens_before=tokens_before,
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tokens_after=self.token_counter.count_messages(result),
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details="没有可压缩的旧工具结果",
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)
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tokens_after = self.token_counter.count_messages(result)
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logger.info(
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"MicroCompact: %d 条工具结果打桩, %d → %d tokens (节省 %d)",
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stubbed_count, tokens_before, tokens_after,
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tokens_before - tokens_after,
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)
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return CompactionResult(
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result, CompactionStrategy.MICRO,
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tokens_before=tokens_before, tokens_after=tokens_after,
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details=f"{stubbed_count} 条工具结果已压缩",
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)
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except Exception as e:
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self._consecutive_failures += 1
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logger.error("MicroCompact 失败 (%d/%d): %s",
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self._consecutive_failures,
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self.config.max_consecutive_failures, e)
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if self._consecutive_failures >= self.config.max_consecutive_failures:
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logger.warning("MicroCompact 熔断！跳过本次压缩")
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return CompactionResult(
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messages, CompactionStrategy.MICRO,
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tokens_before=tokens_before, tokens_after=tokens_before,
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details=f"熔断 ({self._consecutive_failures}次连续失败)",
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)
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return CompactionResult(
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messages, CompactionStrategy.MICRO,
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tokens_before=tokens_before, tokens_after=tokens_before,
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details=f"失败: {e}",
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)
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# ──────────────────── Tier 2: FullCompact ────────────────────
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async def _full_compact(
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self,
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messages: List[Dict[str, Any]],
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tokens_before: int,
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is_reactive: bool = False,
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llm_client=None, # 可选：外部传入 LLM 客户端
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) -> CompactionResult:
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"""FullCompact: 用 LLM 将旧对话压缩为摘要消息。
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核心逻辑（参考 Claude Code compact.ts）：
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1. 保留 system 消息 + 最近 N 条消息
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2. 中间部分 → 调用轻量 LLM 生成摘要
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3. 将摘要作为 compact_boundary 消息插入
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4. 熔断保护：连续失败 N 次后放弃
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"""
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try:
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preserve_count = self.config.min_preserve_messages
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if len(messages) <= preserve_count + 4:
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return CompactionResult(
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messages, CompactionStrategy.FULL,
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tokens_before=tokens_before, tokens_after=tokens_before,
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details="消息数不足，跳过 FullCompact",
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)
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# ── 分离各段 ──
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# 找到 system 消息
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system_msgs = [m for m in messages if m.get("role") == "system"]
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non_system = [m for m in messages if m.get("role") != "system"]
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middle_start = 0
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# 跳过最前面的几条 system 后的过渡消息（通常是首次问候等）
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# 保留至少 preserve_count 条在末尾
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if len(non_system) > preserve_count + 4:
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middle_end = len(non_system) - preserve_count
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# 只压缩中间部分：跳过前 2 条（通常是 system 后的首次交互）到倒数 preserve_count 条之间
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middle_start = max(2, 0)
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older = non_system[middle_start:middle_end]
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recent = non_system[middle_end:]
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else:
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# 消息太少，只保留最近的
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older = non_system[:-preserve_count] if len(non_system) > preserve_count else []
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recent = non_system[-preserve_count:] if len(non_system) >= preserve_count else non_system
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if len(older) < 3:
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return CompactionResult(
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messages, CompactionStrategy.FULL,
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tokens_before=tokens_before, tokens_after=tokens_before,
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details="旧消息不足以压缩",
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)
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# ── 调用 LLM 生成摘要 ──
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summary_text = await self._generate_summary(older, llm_client)
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# ── 组装结果: system + compact_boundary + recent ──
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compact_boundary = {
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"role": "user",
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"content": (
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f"[对话上下文摘要 — 之前的关键信息]\n\n{summary_text}\n\n"
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f"[以上为自动生成的对话摘要，共压缩 {len(older)} 条消息。"
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f"以下是最近的对话延续]"
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),
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}
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new_messages = system_msgs + [compact_boundary] + recent
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tokens_after = self.token_counter.count_messages(new_messages)
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strategy = CompactionStrategy.REACTIVE if is_reactive else CompactionStrategy.FULL
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logger.info(
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"FullCompact (%s): %d 条消息→摘要 (%d 字), %d → %d tokens (节省 %d)",
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"被动" if is_reactive else "主动",
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len(older), len(summary_text),
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tokens_before, tokens_after, tokens_before - tokens_after,
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)
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self._consecutive_failures = 0 # 成功后重置
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self._last_compact_time = time.time()
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self._compact_count += 1
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return CompactionResult(
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new_messages, strategy,
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tokens_before=tokens_before, tokens_after=tokens_after,
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details=f"压缩 {len(older)} 条→{len(summary_text)} 字摘要",
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)
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except Exception as e:
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self._consecutive_failures += 1
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logger.error("FullCompact 失败 (%d/%d): %s",
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self._consecutive_failures,
|
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self.config.max_consecutive_failures, e)
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|
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if self._consecutive_failures >= self.config.max_consecutive_failures:
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logger.warning("FullCompact 熔断！返回原始消息")
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return CompactionResult(
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messages, CompactionStrategy.FULL,
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tokens_before=tokens_before, tokens_after=tokens_before,
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details=f"熔断 ({self._consecutive_failures}次连续失败)",
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)
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return CompactionResult(
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messages, CompactionStrategy.FULL,
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tokens_before=tokens_before, tokens_after=tokens_before,
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details=f"失败: {e}",
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)
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|
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async def _generate_summary(
|
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self,
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older_messages: List[Dict[str, Any]],
|
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llm_client=None,
|
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) -> str:
|
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"""调用轻量 LLM 生成对话摘要。"""
|
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# 构建提示词
|
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user_content = _build_compact_user_prompt(
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older_messages,
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max_chars=3000,
|
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)
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|
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if llm_client is not None:
|
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# 使用外部传入的 LLM 客户端
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from app.agent_runtime.core import _LLMClient
|
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from app.agent_runtime.schemas import AgentLLMConfig
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|
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if not isinstance(llm_client, _LLMClient):
|
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# 创建临时客户端
|
||
summary_config = AgentLLMConfig(
|
||
provider="deepseek",
|
||
model=self.config.summary_model,
|
||
temperature=self.config.summary_temperature,
|
||
max_tokens=self.config.summary_max_tokens,
|
||
request_timeout=30.0,
|
||
)
|
||
llm_client = _LLMClient(summary_config)
|
||
|
||
messages = [
|
||
{"role": "system", "content": COMPACT_SUMMARY_SYSTEM},
|
||
{"role": "user", "content": f"请将以下对话历史压缩为不超过{self.config.summary_max_tokens // 2}字的摘要：\n\n{user_content}"},
|
||
]
|
||
|
||
response = await llm_client.chat(messages=messages, tools=None, iteration=-1)
|
||
content = getattr(response, 'content', '') or (
|
||
response.get('content', '') if isinstance(response, dict) else ""
|
||
)
|
||
return content.strip() or self._fallback_summary(older_messages)
|
||
else:
|
||
# 无 LLM 客户端，使用 fallback
|
||
return self._fallback_summary(older_messages)
|
||
|
||
@staticmethod
|
||
def _fallback_summary(older_messages: List[Dict[str, Any]]) -> str:
|
||
"""无 LLM 时的降级摘要（提取关键信息）。"""
|
||
topics = set()
|
||
for msg in older_messages:
|
||
if msg.get("role") == "user":
|
||
content = msg.get("content", "")
|
||
if len(content) > 60:
|
||
content = content[:60] + "..."
|
||
if content:
|
||
topics.add(content)
|
||
|
||
if not topics:
|
||
return "此段对话为助手与用户的交互。"
|
||
|
||
topic_list = "；".join(list(topics)[:10])
|
||
return f"对话涉及以下话题: {topic_list}"
|
||
|
||
|
||
# ──────────────────────────── 工厂函数 ────────────────────────────
|
||
|
||
def create_compaction_engine(
|
||
config: Optional[CompactionConfig] = None,
|
||
model: str = "deepseek-v4-flash",
|
||
) -> CompactionEngine:
|
||
"""创建 CompactionEngine 实例的便捷工厂。"""
|
||
if config is None:
|
||
config = CompactionConfig()
|
||
return CompactionEngine(config=config, model=model)
|