mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-06-25 09:34:19 +08:00
590 lines
22 KiB
Python
590 lines
22 KiB
Python
"""
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活动日志中间件 - 自动记录 Agent 每次交互的操作摘要。
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按日期存储在 CONFIG_PATH/agent/activity/YYYY-MM-DD.md 中，
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每次 Agent 执行完毕后自动调用 LLM 对本轮对话生成简洁的活动摘要，
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并在每次 Agent 启动时注入轻量索引，完整日志由工具按需查询。
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"""
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import re
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from collections.abc import Awaitable, Callable
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import Annotated, Any, NotRequired, Optional, TypedDict
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from anyio import Path as AsyncPath
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from langchain.agents.middleware.types import (
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AgentMiddleware,
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AgentState,
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ContextT,
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ModelRequest,
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ModelResponse,
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PrivateStateAttr, # noqa
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ResponseT,
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)
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from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
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from langgraph.runtime import Runtime
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from app.agent.middleware.utils import append_to_system_message
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from app.log import logger
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# 活动日志保留天数
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DEFAULT_RETENTION_DAYS = 7
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# 注入系统提示词时索引的天数
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PROMPT_LOAD_DAYS = 3
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# 工具默认查询的天数
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DEFAULT_QUERY_DAYS = 7
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# 工具单次返回的最大条数
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DEFAULT_QUERY_LIMIT = 20
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MAX_QUERY_LIMIT = 50
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# 每日日志文件最大大小 (256KB)
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MAX_LOG_FILE_SIZE = 256 * 1024
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# 提取本轮对话上下文的最大字符数（避免过长的对话消耗太多 token）
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MAX_CONTEXT_FOR_SUMMARY = 4000
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SUMMARY_SKIP_MARKER = "SKIP"
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# LLM 总结的提示词
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SUMMARY_PROMPT = """请判断以下 AI 助手与用户的对话是否值得写入 MoviePilot 活动日志。
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如果本轮只是问候、寒暄、感谢、确认、闲聊、没有实际任务、没有工具动作、任务没有推进、纯粹的格式纠正或无意义空转，请只输出：SKIP
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如果值得记录，请输出一条中文单行活动摘要，要求：
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- 40 到 160 个汉字左右，信息密度高，不要写成泛泛一句话。
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- 只输出摘要正文，不要标题、编号、Markdown、JSON 或解释。
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- 尽量包含：用户目标、关键对象（影片/剧集/站点/路径/任务/设置）、助手采取的关键动作或工具、结果状态、失败原因或下一步。
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- 如果有明确 ID、路径、站点名、任务状态、成功/失败数量，请保留关键值。
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- 不要记录 API Key、Cookie、Token、密码等敏感信息；如出现请写成“敏感信息已省略”。
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推荐格式示例：
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用户要求整理 `/downloads/Show`，助手识别为《示例剧》TMDB 12345，并提交 transfer_file 整理，结果成功。
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用户排查下载失败，助手查询 qBittorrent 任务和站点状态，发现 tracker 超时，建议更换站点或重试。
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对话记录：
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{conversation}"""
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ACTIVITY_ENTRY_PATTERN = re.compile(r"^-\s+\*\*(?P<time>\d{2}:\d{2})\*\*\s+(?P<summary>.+)$")
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def _coerce_query_limit(limit: Optional[int]) -> int:
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"""规范化活动日志查询条数。"""
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if limit is None:
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return DEFAULT_QUERY_LIMIT
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try:
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value = int(limit)
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except (TypeError, ValueError):
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return DEFAULT_QUERY_LIMIT
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return min(max(value, 1), MAX_QUERY_LIMIT)
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def _build_log_path(activity_dir: str, date_str: str) -> Path:
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"""构建指定日期的活动日志路径。"""
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return Path(activity_dir) / f"{date_str}.md"
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def _iter_recent_dates(days: int) -> list[str]:
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"""返回从今天开始向前的日期字符串列表。"""
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normalized_days = max(1, int(days or 1))
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today = datetime.now().date()
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return [
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(today - timedelta(days=index)).strftime("%Y-%m-%d")
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for index in range(normalized_days)
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]
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def _parse_activity_entries(date_str: str, content: str) -> list[dict[str, str]]:
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"""从单日活动日志 Markdown 中解析活动条目。"""
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entries: list[dict[str, str]] = []
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for line in content.splitlines():
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match = ACTIVITY_ENTRY_PATTERN.match(line.strip())
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if not match:
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continue
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entries.append(
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{
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"date": date_str,
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"time": match.group("time"),
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"summary": match.group("summary").strip(),
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}
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)
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return entries
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def _activity_summary_matches_keyword(
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summary: str,
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keyword: str,
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regex_pattern: Optional[re.Pattern[str]],
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) -> bool:
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"""判断活动摘要是否命中普通关键词或正则表达式。"""
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if regex_pattern:
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return bool(regex_pattern.search(summary))
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return keyword.lower() in summary.lower()
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def load_activity_log_index(activity_dir: str, days: int = PROMPT_LOAD_DAYS) -> dict[str, str]:
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"""加载近期活动日志索引，不返回完整日志正文。"""
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index: dict[str, str] = {}
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for date_str in _iter_recent_dates(days):
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log_path = _build_log_path(activity_dir, date_str)
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if not log_path.is_file():
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continue
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try:
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content = log_path.read_text(encoding="utf-8")
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except Exception as e:
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logger.warning(f"读取活动日志索引失败 {log_path}: {e}")
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continue
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entry_count = len(_parse_activity_entries(date_str, content))
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if entry_count:
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index[date_str] = f"{entry_count} 条活动记录"
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return index
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def query_activity_logs(
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activity_dir: str,
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*,
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keyword: Optional[str] = None,
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use_regex: bool = False,
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date: Optional[str] = None,
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days: int = DEFAULT_QUERY_DAYS,
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limit: Optional[int] = DEFAULT_QUERY_LIMIT,
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) -> dict[str, Any]:
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"""
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查询活动日志条目。
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:param activity_dir: 活动日志目录
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:param keyword: 可选关键词，按摘要文本过滤
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:param use_regex: 是否将关键词按正则表达式匹配
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:param date: 可选日期，格式为 ``YYYY-MM-DD``
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:param days: 未指定日期时向前查询的天数
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:param limit: 返回条数上限
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:return: 查询结果载荷
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"""
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normalized_limit = _coerce_query_limit(limit)
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normalized_keyword = str(keyword or "").strip()
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normalized_use_regex = bool(use_regex)
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regex_pattern: Optional[re.Pattern[str]] = None
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if normalized_keyword and normalized_use_regex:
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try:
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regex_pattern = re.compile(normalized_keyword, re.IGNORECASE)
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except re.error as err:
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return {
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"success": False,
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"message": f"无效的活动日志正则表达式: {err}",
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"activity_dir": activity_dir,
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"keyword": normalized_keyword,
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"use_regex": normalized_use_regex,
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"date": date,
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"days": days if not date else None,
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"searched_dates": [],
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"total_count": 0,
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"returned_count": 0,
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"truncated": False,
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"entries": [],
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}
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date_candidates = [date] if date else _iter_recent_dates(days)
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entries: list[dict[str, str]] = []
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searched_dates: list[str] = []
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for date_str in date_candidates:
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if not date_str:
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continue
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searched_dates.append(date_str)
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log_path = _build_log_path(activity_dir, date_str)
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if not log_path.is_file():
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continue
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try:
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content = log_path.read_text(encoding="utf-8")
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except Exception as e:
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logger.warning(f"读取活动日志失败 {log_path}: {e}")
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continue
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for entry in _parse_activity_entries(date_str, content):
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if normalized_keyword and not _activity_summary_matches_keyword(
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entry["summary"], normalized_keyword, regex_pattern
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):
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continue
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entries.append(entry)
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entries.sort(key=lambda item: (item["date"], item["time"]), reverse=True)
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total_count = len(entries)
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return {
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"success": True,
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"activity_dir": activity_dir,
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"keyword": normalized_keyword or None,
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"use_regex": normalized_use_regex,
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"date": date,
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"days": days if not date else None,
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"searched_dates": searched_dates,
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"total_count": total_count,
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"returned_count": min(total_count, normalized_limit),
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"truncated": total_count > normalized_limit,
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"entries": entries[:normalized_limit],
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}
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class ActivityLogState(AgentState):
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"""ActivityLogMiddleware 的状态模型。"""
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activity_log_contents: NotRequired[Annotated[dict[str, str], PrivateStateAttr]]
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"""将日期字符串映射到日志索引摘要的字典。标记为私有，不包含在最终代理状态中。"""
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class ActivityLogStateUpdate(TypedDict):
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"""ActivityLogMiddleware 的状态更新。"""
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activity_log_contents: dict[str, str]
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def _extract_last_round(messages: list) -> Optional[list]:
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"""从完整消息列表中提取最后一轮交互。
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从最后一条 HumanMessage 到消息末尾即为本轮交互。
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参数：
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messages: Agent 执行后的完整消息列表。
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返回：
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本轮交互的消息子列表，如果无有效交互则返回 None。
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"""
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if not messages:
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return None
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# 找到最后一条用户消息的索引
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last_human_idx = None
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for i in range(len(messages) - 1, -1, -1):
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if isinstance(messages[i], HumanMessage) and messages[i].content:
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last_human_idx = i
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break
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if last_human_idx is None:
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return None
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round_messages = messages[last_human_idx:]
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# 检查是否为系统心跳消息
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user_msg = round_messages[0]
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user_content = (
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user_msg.content if isinstance(user_msg.content, str) else str(user_msg.content)
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)
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if user_content.strip().startswith("[System Heartbeat]"):
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return None
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return round_messages
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def _format_conversation_for_summary(round_messages: list) -> str:
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"""将本轮对话消息格式化为文本，供 LLM 总结。
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参数：
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round_messages: 本轮交互的消息列表。
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返回：
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格式化后的对话文本。
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"""
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lines = []
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total_len = 0
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for msg in round_messages:
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if isinstance(msg, HumanMessage):
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content = msg.content if isinstance(msg.content, str) else str(msg.content)
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line = f"用户: {content}"
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elif isinstance(msg, AIMessage):
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if hasattr(msg, "tool_calls") and msg.tool_calls:
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tool_names = [
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tc["name"]
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for tc in msg.tool_calls
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if isinstance(tc, dict) and "name" in tc
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]
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line = f"助手调用工具: {', '.join(tool_names)}"
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elif msg.content:
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content = (
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msg.content if isinstance(msg.content, str) else str(msg.content)
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)
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line = f"助手: {content}"
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else:
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continue
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elif isinstance(msg, ToolMessage):
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content = msg.content if isinstance(msg.content, str) else str(msg.content)
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# 工具返回可能很长，截断
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if len(content) > 200:
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content = content[:200] + "..."
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line = f"工具返回: {content}"
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else:
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continue
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# 控制总长度
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if total_len + len(line) > MAX_CONTEXT_FOR_SUMMARY:
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lines.append("...(后续对话省略)")
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break
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lines.append(line)
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total_len += len(line)
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return "\n".join(lines)
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def _should_skip_activity_summary(round_messages: list) -> bool:
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"""判断本轮交互是否无需生成活动日志。"""
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if not round_messages:
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return True
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has_tool_activity = any(
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isinstance(msg, ToolMessage)
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or (isinstance(msg, AIMessage) and bool(getattr(msg, "tool_calls", None)))
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for msg in round_messages
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)
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if has_tool_activity:
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return False
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return True
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async def _summarize_with_llm(conversation_text: str) -> Optional[str]:
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"""调用 LLM 对对话文本生成活动摘要。
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参数：
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conversation_text: 格式化后的对话文本。
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返回：
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LLM 生成的摘要字符串，失败时返回 None。
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"""
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try:
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from app.agent.llm import LLMHelper
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llm = await LLMHelper.get_llm(streaming=False)
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prompt = SUMMARY_PROMPT.format(conversation=conversation_text)
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response = await llm.ainvoke(prompt)
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summary = response.content.strip()
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# 清理模型可能输出的前缀（如 "摘要：" "总结："）
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summary = re.sub(r"^(摘要|总结|活动记录)[：:]\s*", "", summary)
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if summary.strip().upper() == SUMMARY_SKIP_MARKER:
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return None
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return summary if summary else None
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except Exception as e:
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logger.debug(f"LLM 活动摘要生成失败: {e}")
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return None
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ACTIVITY_LOG_SYSTEM_PROMPT = """<activity_log>
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<activity_log_index>
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{activity_log_index}
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</activity_log_index>
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<activity_log_guidelines>
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Activity logs are automatically maintained by the system and are available for continuity, but full log contents are not injected into context by default.
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**How to use this information:**
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- The <activity_log_index> above only lists which recent dates have activity records and how many entries exist.
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- Use `query_activity_log` when the user asks about previous work, asks to continue a prior task, or when recent activity is clearly relevant to the current request.
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- To find related logs, start with a broad search: use the exact date if known; otherwise query recent days with a short keyword, or omit `keyword` and inspect the latest entries. If there are no matches, retry with a larger `days` value or a shorter object/path fragment.
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- `query_activity_log.keyword` is a plain substring by default. Set `use_regex=true` only when matching alternatives or patterns such as multiple titles, paths, or task IDs.
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- Do not query activity logs for routine standalone tasks such as file organization, media recognition, downloads, subscriptions, or diagnostics unless the user explicitly references prior activity.
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- Activity logs are read-only from your perspective. Do not attempt to edit or write to activity log files.
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- For long-term preferences and knowledge, continue to use MEMORY.md.
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- Activity logs are retained for {retention_days} days and then automatically cleaned up.
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</activity_log_guidelines>
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</activity_log>
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"""
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class ActivityLogMiddleware(AgentMiddleware[ActivityLogState, ContextT, ResponseT]): # noqa
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"""自动记录 Agent 活动日志并注入轻量索引的中间件。
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- abefore_agent: 加载近几天的活动日志索引
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- awrap_model_call: 将活动日志索引和检索规则注入系统提示词
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- aafter_agent: 从本次对话中提取摘要并追加到当日日志文件
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参数：
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activity_dir: 活动日志存储目录路径。
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retention_days: 日志保留天数（默认 7 天）。
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prompt_load_days: 注入系统提示词时索引的天数（默认 3 天）。
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"""
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state_schema = ActivityLogState
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def __init__(
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self,
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*,
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activity_dir: str,
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retention_days: int = DEFAULT_RETENTION_DAYS,
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prompt_load_days: int = PROMPT_LOAD_DAYS,
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) -> None:
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self.activity_dir = activity_dir
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self.retention_days = retention_days
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self.prompt_load_days = prompt_load_days
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def _get_log_path(self, date_str: str) -> AsyncPath:
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"""获取指定日期的日志文件路径。"""
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return AsyncPath(self.activity_dir) / f"{date_str}.md"
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def _format_activity_log(self, contents: dict[str, str]) -> str:
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"""格式化活动日志索引用于系统提示词注入。"""
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if not contents:
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return ACTIVITY_LOG_SYSTEM_PROMPT.format(
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activity_log_index="(近期暂无活动日志索引。需要历史上下文时可调用 query_activity_log。)",
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retention_days=self.retention_days,
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)
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# 按日期排序（最近的在前）
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sorted_dates = sorted(contents.keys(), reverse=True)
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sections = []
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for date_str in sorted_dates:
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content = contents[date_str].strip()
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if content:
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sections.append(f"### {date_str}\n{content}")
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if not sections:
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return ACTIVITY_LOG_SYSTEM_PROMPT.format(
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activity_log_index="(近期暂无活动日志索引。需要历史上下文时可调用 query_activity_log。)",
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retention_days=self.retention_days,
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)
|
||
|
||
log_body = "\n".join(sections)
|
||
return ACTIVITY_LOG_SYSTEM_PROMPT.format(
|
||
activity_log_index=log_body,
|
||
retention_days=self.retention_days,
|
||
)
|
||
|
||
async def _load_recent_logs(self) -> dict[str, str]:
|
||
"""加载近几天的活动日志索引。"""
|
||
return load_activity_log_index(
|
||
activity_dir=self.activity_dir,
|
||
days=self.prompt_load_days,
|
||
)
|
||
|
||
async def _append_activity(self, summary: str) -> None:
|
||
"""将一条活动记录追加到当日日志文件。"""
|
||
today_str = datetime.now().strftime("%Y-%m-%d")
|
||
now_str = datetime.now().strftime("%H:%M")
|
||
log_path = self._get_log_path(today_str)
|
||
|
||
# 确保目录存在
|
||
dir_path = AsyncPath(self.activity_dir)
|
||
if not await dir_path.exists():
|
||
await dir_path.mkdir(parents=True, exist_ok=True)
|
||
|
||
# 检查文件大小
|
||
if await log_path.exists():
|
||
stat = await log_path.stat()
|
||
if stat.st_size >= MAX_LOG_FILE_SIZE:
|
||
logger.warning(
|
||
"Activity log %s exceeds size limit (%d bytes), skipping append",
|
||
today_str,
|
||
stat.st_size,
|
||
)
|
||
return
|
||
|
||
# 追加记录
|
||
entry = f"- **{now_str}** {summary}\n"
|
||
try:
|
||
if await log_path.exists():
|
||
existing = await log_path.read_text(encoding="utf-8")
|
||
await log_path.write_text(existing + entry, encoding="utf-8")
|
||
else:
|
||
header = f"# {today_str} 活动日志\n\n"
|
||
await log_path.write_text(header + entry, encoding="utf-8")
|
||
logger.debug("Activity logged: %s", summary[:80])
|
||
except Exception as e:
|
||
logger.warning("Failed to append activity log: %s", e)
|
||
|
||
async def _cleanup_old_logs(self) -> None:
|
||
"""清理超过保留天数的旧日志文件。"""
|
||
dir_path = AsyncPath(self.activity_dir)
|
||
if not await dir_path.exists():
|
||
return
|
||
|
||
cutoff_date = datetime.now().date() - timedelta(days=self.retention_days)
|
||
date_pattern = re.compile(r"^(\d{4}-\d{2}-\d{2})\.md$")
|
||
|
||
try:
|
||
async for path in dir_path.iterdir():
|
||
if not await path.is_file():
|
||
continue
|
||
match = date_pattern.match(path.name)
|
||
if not match:
|
||
continue
|
||
try:
|
||
file_date = datetime.strptime(match.group(1), "%Y-%m-%d").date()
|
||
if file_date < cutoff_date:
|
||
await path.unlink()
|
||
logger.debug("Cleaned up old activity log: %s", path.name)
|
||
except ValueError:
|
||
continue
|
||
except Exception as e:
|
||
logger.warning("Failed to cleanup old activity logs: %s", e)
|
||
|
||
async def abefore_agent(
|
||
self, state: ActivityLogState, runtime: Runtime
|
||
) -> Optional[ActivityLogStateUpdate]:
|
||
"""在 Agent 执行前加载近期活动日志。"""
|
||
# 如果已经加载则跳过
|
||
if "activity_log_contents" in state:
|
||
return None
|
||
|
||
contents = await self._load_recent_logs()
|
||
|
||
# 趁机清理旧日志（低频操作，不影响性能）
|
||
await self._cleanup_old_logs()
|
||
|
||
return ActivityLogStateUpdate(activity_log_contents=contents)
|
||
|
||
def modify_request(self, request: ModelRequest[ContextT]) -> ModelRequest[ContextT]:
|
||
"""将活动日志注入系统消息。"""
|
||
contents = request.state.get("activity_log_contents", {}) # noqa
|
||
activity_log_prompt = self._format_activity_log(contents)
|
||
|
||
new_system_message = append_to_system_message(
|
||
request.system_message, activity_log_prompt
|
||
)
|
||
return request.override(system_message=new_system_message)
|
||
|
||
async def awrap_model_call(
|
||
self,
|
||
request: ModelRequest[ContextT],
|
||
handler: Callable[
|
||
[ModelRequest[ContextT]], Awaitable[ModelResponse[ResponseT]]
|
||
],
|
||
) -> ModelResponse[ResponseT]:
|
||
"""异步包装模型调用，注入活动日志到系统提示词。"""
|
||
modified_request = self.modify_request(request)
|
||
return await handler(modified_request)
|
||
|
||
async def aafter_agent(
|
||
self, state: ActivityLogState, runtime: Runtime
|
||
) -> Optional[dict[str, Any]]:
|
||
"""Agent 执行完毕后，调用 LLM 对本轮对话生成摘要并追加到当日活动日志。"""
|
||
try:
|
||
messages = state.get("messages", [])
|
||
if not messages:
|
||
return None
|
||
|
||
# 提取本轮交互
|
||
round_messages = _extract_last_round(messages)
|
||
if not round_messages:
|
||
return None
|
||
if _should_skip_activity_summary(round_messages):
|
||
return None
|
||
|
||
# 格式化对话文本
|
||
conversation_text = _format_conversation_for_summary(round_messages)
|
||
if not conversation_text:
|
||
return None
|
||
|
||
# 调用 LLM 生成摘要
|
||
summary = await _summarize_with_llm(conversation_text)
|
||
if summary:
|
||
await self._append_activity(summary)
|
||
except Exception as e:
|
||
logger.warning("Failed to record activity: %s", e)
|
||
|
||
return None
|
||
|
||
|
||
__all__ = [
|
||
"ActivityLogMiddleware",
|
||
"load_activity_log_index",
|
||
"query_activity_logs",
|
||
]
|