mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
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54e81aba11
- Add knowledge/ directory structure and knowledge-wiki skill for structured knowledge accumulation - Auto-inject MEMORY.md into system prompt with truncation (last 200 lines) - Light Dream: extend flush_memory to extract long-term memories into MEMORY.md with date stamps - Add mandatory knowledge auto-write rules in system prompt (no user confirmation needed) - Expand MemoryManager.sync() to index knowledge/ files for vector search - Update RULE.md template with workspace conventions and knowledge guidelines
481 lines
18 KiB
Python
481 lines
18 KiB
Python
"""
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Memory flush manager (with Light Dream)
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Handles memory persistence when conversation context is trimmed or overflows:
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- Uses LLM to summarize discarded messages into concise key-information entries
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- Writes to daily memory files (lazy creation)
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- Light Dream: extracts long-term memories to MEMORY.md in the same LLM call
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- Deduplicates trim flushes to avoid repeated writes
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- Runs summarization asynchronously to avoid blocking normal replies
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- Provides daily summary interface for scheduler
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"""
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import threading
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from typing import Optional, Callable, Any, List, Dict
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from pathlib import Path
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from datetime import datetime
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from common.log import logger
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SUMMARIZE_SYSTEM_PROMPT = """你是一个记忆提取助手。你的任务是从对话记录中提炼出两种记忆：
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## 第一部分：日常记录（[DAILY]）
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按「事件」维度归纳当天发生的事，不要按对话轮次逐条记录：
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- 每条一行，用 "- " 开头
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- 合并同一件事的多轮对话
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- 只记录有意义的事件，忽略闲聊和问候
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## 第二部分：长期记忆（[MEMORY]）
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提取值得**永久记住**的关键信息，这些信息在未来的对话中仍然有价值：
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- 用户的偏好、习惯、风格（如"用户偏好中文回复"、"用户喜欢简洁风格"）
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- 重要的决策或约定（如"项目决定使用 PostgreSQL"）
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- 关键人物信息（如"张总是用户的上级"）
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- 用户明确要求记住的内容
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- 重要的教训或经验总结
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**如果没有值得永久记住的信息，[MEMORY] 部分留空即可。**
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## 输出格式（严格遵守）
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```
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[DAILY]
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- 事件1的摘要
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- 事件2的摘要
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[MEMORY]
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- 值得永久记住的信息1
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- 值得永久记住的信息2
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```
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当对话没有任何记录价值（仅含问候或无意义内容），直接回复"无"。"""
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SUMMARIZE_USER_PROMPT = """请从以下对话记录中提取记忆（按 [DAILY] 和 [MEMORY] 两部分输出）：
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{conversation}"""
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class MemoryFlushManager:
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"""
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Manages memory flush operations.
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Flush is triggered by agent_stream in two scenarios:
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1. Context trim: _trim_messages discards old turns → flush discarded content
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2. Context overflow: API rejects request → emergency flush before clearing
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Additionally, create_daily_summary() can be called by scheduler for end-of-day summaries.
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"""
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def __init__(
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self,
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workspace_dir: Path,
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llm_model: Optional[Any] = None,
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):
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self.workspace_dir = workspace_dir
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self.llm_model = llm_model
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self.memory_dir = workspace_dir / "memory"
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self.memory_dir.mkdir(parents=True, exist_ok=True)
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self.last_flush_timestamp: Optional[datetime] = None
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self._trim_flushed_hashes: set = set() # Content hashes of already-flushed messages
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self._last_flushed_content_hash: str = "" # Content hash at last flush, for daily dedup
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def get_today_memory_file(self, user_id: Optional[str] = None, ensure_exists: bool = False) -> Path:
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"""Get today's memory file path: memory/YYYY-MM-DD.md"""
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today = datetime.now().strftime("%Y-%m-%d")
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if user_id:
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user_dir = self.memory_dir / "users" / user_id
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if ensure_exists:
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user_dir.mkdir(parents=True, exist_ok=True)
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today_file = user_dir / f"{today}.md"
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else:
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today_file = self.memory_dir / f"{today}.md"
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if ensure_exists and not today_file.exists():
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today_file.parent.mkdir(parents=True, exist_ok=True)
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today_file.write_text(f"# Daily Memory: {today}\n\n")
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return today_file
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def get_main_memory_file(self, user_id: Optional[str] = None) -> Path:
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"""Get main memory file path: MEMORY.md (workspace root)"""
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if user_id:
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user_dir = self.memory_dir / "users" / user_id
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user_dir.mkdir(parents=True, exist_ok=True)
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return user_dir / "MEMORY.md"
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else:
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return Path(self.workspace_dir) / "MEMORY.md"
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def get_status(self) -> dict:
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return {
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'last_flush_time': self.last_flush_timestamp.isoformat() if self.last_flush_timestamp else None,
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'today_file': str(self.get_today_memory_file()),
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'main_file': str(self.get_main_memory_file())
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}
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# ---- Flush execution (called by agent_stream or scheduler) ----
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def flush_from_messages(
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self,
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messages: List[Dict],
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user_id: Optional[str] = None,
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reason: str = "trim",
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max_messages: int = 0,
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) -> bool:
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"""
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Asynchronously summarize and flush messages to daily memory.
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Deduplication runs synchronously, then LLM summarization + file write
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run in a background thread so the main reply flow is never blocked.
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Args:
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messages: Conversation message list (OpenAI/Claude format)
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user_id: Optional user ID for user-scoped memory
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reason: Why flush was triggered ("trim" | "overflow" | "daily_summary")
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max_messages: Max recent messages to summarize (0 = all)
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Returns:
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True if flush was dispatched
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"""
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try:
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import hashlib
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deduped = []
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for m in messages:
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text = self._extract_text_from_content(m.get("content", ""))
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if not text or not text.strip():
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continue
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h = hashlib.md5(text.encode("utf-8")).hexdigest()
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if h not in self._trim_flushed_hashes:
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self._trim_flushed_hashes.add(h)
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deduped.append(m)
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if not deduped:
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return False
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import copy
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snapshot = copy.deepcopy(deduped)
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thread = threading.Thread(
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target=self._flush_worker,
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args=(snapshot, user_id, reason, max_messages),
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daemon=True,
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)
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thread.start()
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logger.info(f"[MemoryFlush] Async flush dispatched (reason={reason}, msgs={len(snapshot)})")
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return True
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except Exception as e:
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logger.warning(f"[MemoryFlush] Failed to dispatch flush (reason={reason}): {e}")
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return False
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def _flush_worker(
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self,
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messages: List[Dict],
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user_id: Optional[str],
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reason: str,
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max_messages: int,
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):
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"""Background worker: summarize with LLM, write daily file + MEMORY.md (Light Dream)."""
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try:
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raw_summary = self._summarize_messages(messages, max_messages)
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if not raw_summary or not raw_summary.strip() or raw_summary.strip() == "无":
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logger.info(f"[MemoryFlush] No valuable content to flush (reason={reason})")
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return
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daily_part, memory_part = self._parse_dual_output(raw_summary)
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# --- Write daily memory ---
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if daily_part:
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daily_file = ensure_daily_memory_file(self.workspace_dir, user_id)
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if reason == "overflow":
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header = f"## Context Overflow Recovery ({datetime.now().strftime('%H:%M')})"
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note = "The following conversation was trimmed due to context overflow:\n"
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elif reason == "trim":
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header = f"## Trimmed Context ({datetime.now().strftime('%H:%M')})"
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note = ""
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elif reason == "daily_summary":
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header = f"## Daily Summary ({datetime.now().strftime('%H:%M')})"
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note = ""
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else:
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header = f"## Session Notes ({datetime.now().strftime('%H:%M')})"
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note = ""
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flush_entry = f"\n{header}\n\n{note}{daily_part}\n"
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with open(daily_file, "a", encoding="utf-8") as f:
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f.write(flush_entry)
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logger.info(f"[MemoryFlush] Wrote daily memory to {daily_file.name} (reason={reason}, chars={len(daily_part)})")
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# --- Light Dream: write long-term memory to MEMORY.md ---
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if memory_part:
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self._append_to_main_memory(memory_part, user_id)
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self.last_flush_timestamp = datetime.now()
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except Exception as e:
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logger.warning(f"[MemoryFlush] Async flush failed (reason={reason}): {e}")
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@staticmethod
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def _parse_dual_output(raw: str) -> tuple:
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"""
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Parse LLM output into (daily_part, memory_part).
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Handles both new [DAILY]/[MEMORY] format and legacy single-section format.
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"""
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raw = raw.strip()
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if "[DAILY]" in raw or "[MEMORY]" in raw:
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daily_part = ""
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memory_part = ""
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# Extract [DAILY] section
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if "[DAILY]" in raw:
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start = raw.index("[DAILY]") + len("[DAILY]")
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end = raw.index("[MEMORY]") if "[MEMORY]" in raw else len(raw)
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daily_part = raw[start:end].strip()
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# Extract [MEMORY] section
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if "[MEMORY]" in raw:
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start = raw.index("[MEMORY]") + len("[MEMORY]")
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memory_part = raw[start:].strip()
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# Filter out empty markers
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if memory_part and all(
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not line.strip() or line.strip() == "-"
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for line in memory_part.split("\n")
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):
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memory_part = ""
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return daily_part, memory_part
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# Legacy format: treat entire output as daily, no memory extraction
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return raw, ""
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def _append_to_main_memory(self, memory_entries: str, user_id: Optional[str] = None):
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"""Append extracted long-term memories to MEMORY.md with date stamp."""
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try:
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main_file = self.get_main_memory_file(user_id)
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today = datetime.now().strftime("%Y-%m-%d")
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# Add date prefix to each entry line
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stamped_lines = []
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for line in memory_entries.strip().split("\n"):
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line = line.strip()
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if line.startswith("- "):
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stamped_lines.append(f"- ({today}) {line[2:]}")
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elif line:
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stamped_lines.append(f"- ({today}) {line}")
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if not stamped_lines:
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return
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stamped_text = "\n".join(stamped_lines)
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with open(main_file, "a", encoding="utf-8") as f:
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f.write(f"\n{stamped_text}\n")
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logger.info(f"[LightDream] Appended {len(stamped_lines)} entries to MEMORY.md")
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except Exception as e:
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logger.warning(f"[LightDream] Failed to append to MEMORY.md: {e}")
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def create_daily_summary(
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self,
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messages: List[Dict],
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user_id: Optional[str] = None
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) -> bool:
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"""
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Generate end-of-day summary. Called by daily timer.
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Skips if messages haven't changed since last flush.
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"""
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import hashlib
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content = "".join(
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self._extract_text_from_content(m.get("content", ""))
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for m in messages
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)
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content_hash = hashlib.md5(content.encode("utf-8")).hexdigest()
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if content_hash == self._last_flushed_content_hash:
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logger.debug("[MemoryFlush] Daily summary skipped: no new content since last flush")
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return False
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self._last_flushed_content_hash = content_hash
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return self.flush_from_messages(
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messages=messages,
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user_id=user_id,
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reason="daily_summary",
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max_messages=0,
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)
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# ---- Internal helpers ----
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def _summarize_messages(self, messages: List[Dict], max_messages: int = 0) -> str:
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"""
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Summarize conversation messages using LLM, with rule-based fallback.
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"""
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conversation_text = self._format_conversation_for_summary(messages, max_messages)
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if not conversation_text.strip():
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return ""
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if self.llm_model:
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try:
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summary = self._call_llm_for_summary(conversation_text)
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if summary and summary.strip() and summary.strip() != "无":
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return summary.strip()
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logger.info(f"[MemoryFlush] LLM returned empty or '无', using fallback")
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except Exception as e:
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logger.warning(f"[MemoryFlush] LLM summarization failed, using fallback: {e}")
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else:
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logger.info("[MemoryFlush] No LLM model available, using rule-based fallback")
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return self._extract_summary_fallback(messages, max_messages)
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def _format_conversation_for_summary(self, messages: List[Dict], max_messages: int = 0) -> str:
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"""Format messages into readable conversation text for LLM summarization."""
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msgs = messages if max_messages == 0 else messages[-max_messages * 2:]
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lines = []
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for msg in msgs:
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role = msg.get("role", "")
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text = self._extract_text_from_content(msg.get("content", ""))
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if not text or not text.strip():
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continue
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text = text.strip()
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if role == "user":
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lines.append(f"用户: {text[:500]}")
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elif role == "assistant":
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lines.append(f"助手: {text[:500]}")
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return "\n".join(lines)
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def _call_llm_for_summary(self, conversation_text: str) -> str:
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"""Call LLM to generate a concise summary of the conversation."""
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from agent.protocol.models import LLMRequest
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request = LLMRequest(
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messages=[{"role": "user", "content": SUMMARIZE_USER_PROMPT.format(conversation=conversation_text)}],
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temperature=0,
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max_tokens=500,
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stream=False,
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system=SUMMARIZE_SYSTEM_PROMPT,
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)
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response = self.llm_model.call(request)
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if isinstance(response, dict):
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if response.get("error"):
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raise RuntimeError(response.get("message", "LLM call failed"))
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# OpenAI format
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choices = response.get("choices", [])
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if choices:
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return choices[0].get("message", {}).get("content", "")
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# Handle response object with attribute access (e.g. OpenAI SDK response)
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if hasattr(response, "choices") and response.choices:
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return response.choices[0].message.content or ""
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return ""
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@staticmethod
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def _extract_summary_fallback(messages: List[Dict], max_messages: int = 0) -> str:
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"""
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Rule-based fallback when LLM is unavailable.
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Groups consecutive user+assistant messages into events instead of
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listing each message individually.
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"""
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msgs = messages if max_messages == 0 else messages[-max_messages * 2:]
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events: List[str] = []
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current_user_text = ""
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for msg in msgs:
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role = msg.get("role", "")
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text = MemoryFlushManager._extract_text_from_content(msg.get("content", ""))
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if not text or not text.strip():
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continue
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text = text.strip()
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if role == "user":
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if len(text) <= 5:
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continue
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current_user_text = text[:150]
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elif role == "assistant" and current_user_text:
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first_line = text.split("\n")[0].strip()
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if len(first_line) > 10:
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events.append(f"- {current_user_text} → {first_line[:150]}")
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else:
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events.append(f"- {current_user_text}")
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current_user_text = ""
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if current_user_text:
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events.append(f"- {current_user_text}")
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return "\n".join(events[:10])
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@staticmethod
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def _extract_text_from_content(content) -> str:
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"""Extract plain text from message content (string or content blocks)."""
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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parts = []
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for block in content:
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if isinstance(block, dict) and block.get("type") == "text":
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parts.append(block.get("text", ""))
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elif isinstance(block, str):
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parts.append(block)
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return "\n".join(parts)
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return ""
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def create_memory_files_if_needed(workspace_dir: Path, user_id: Optional[str] = None):
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"""
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Create essential memory files if they don't exist.
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Only creates MEMORY.md; daily files are created lazily on first write.
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Args:
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workspace_dir: Workspace directory
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user_id: Optional user ID for user-specific files
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"""
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memory_dir = workspace_dir / "memory"
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memory_dir.mkdir(parents=True, exist_ok=True)
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# Create main MEMORY.md in workspace root (always needed for bootstrap)
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if user_id:
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user_dir = memory_dir / "users" / user_id
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user_dir.mkdir(parents=True, exist_ok=True)
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main_memory = user_dir / "MEMORY.md"
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else:
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main_memory = Path(workspace_dir) / "MEMORY.md"
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if not main_memory.exists():
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main_memory.write_text("")
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def ensure_daily_memory_file(workspace_dir: Path, user_id: Optional[str] = None) -> Path:
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"""
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Ensure today's daily memory file exists, creating it only when actually needed.
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Called lazily before first write to daily memory.
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Args:
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workspace_dir: Workspace directory
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user_id: Optional user ID for user-specific files
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Returns:
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Path to today's memory file
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"""
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memory_dir = workspace_dir / "memory"
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memory_dir.mkdir(parents=True, exist_ok=True)
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today = datetime.now().strftime("%Y-%m-%d")
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if user_id:
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user_dir = memory_dir / "users" / user_id
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user_dir.mkdir(parents=True, exist_ok=True)
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today_memory = user_dir / f"{today}.md"
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else:
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today_memory = memory_dir / f"{today}.md"
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if not today_memory.exists():
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today_memory.write_text(
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f"# Daily Memory: {today}\n\n"
|
||
)
|
||
|
||
return today_memory
|