8b84378fe1
Elevate MiroFish/CrowdSight from single-container dev to a SaaS foundation: - Local memory backend (Zep-compatible): memory services/models, local graph builder + updater, AgentActivity seam, import-boundary isolation; Zep stays default, local is opt-in behind MEMORY_BACKEND. Semantic parity not yet proven. - Durable product persistence: projects/simulations/reports schema (migration 0007) + tenant/owner-scoped ProductRepository + dual-write + scoped_project read-first + ArtifactStore abstraction; durable JobQueue + worker.py. - SaaS hardening: durable RateLimiter (wired to login), UsageService (LLM accounting), redacted AuditService, idempotency, CORS allowlist, safe API errors, single-use PasswordResetService + endpoints (covers invite-pending). - Exactly 3 roles (super_admin/admin/user) with tenant authz policy. - Admin UI: GET/POST/PATCH /api/admin/users + GET/PUT /api/admin/settings (super-admin only, encrypted/masked); AdminView.vue + SettingsView.vue with admin/super-admin route guards, th/en i18n. - Production deploy topology: multi-stage Dockerfile (frontend build + gunicorn wsgi + nginx SPA-proxy + supervisord worker), backend/wsgi.py, gunicorn dep. Backend 197 passed; frontend 10 tests + build green. ruff unavailable (gap). No commit of credentials; secrets handled via env/.env.example. Deferred: Zep semantic A/B parity, object storage cutover, mobile QA, EasyPanel container build of deploy topology.
380 lines
14 KiB
Python
380 lines
14 KiB
Python
"""
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Zep图谱记忆更新服务
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将模拟中的Agent活动动态更新到Zep图谱中
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"""
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import os
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import time
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import threading
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import json
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from typing import Dict, Any, List, Optional, Callable
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from dataclasses import dataclass
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from datetime import datetime
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from queue import Queue, Empty
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from zep_cloud.client import Zep
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from ..config import Config
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from ..utils.logger import get_logger
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from ..utils.locale import get_locale, set_locale
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logger = get_logger('crowdsight.zep_graph_memory_updater')
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from .memory_activity import AgentActivity
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class ZepGraphMemoryUpdater:
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"""
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Zep图谱记忆更新器
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监控模拟的actions日志文件，将新的agent活动实时更新到Zep图谱中。
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按平台分组，每累积BATCH_SIZE条活动后批量发送到Zep。
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所有有意义的行为都会被更新到Zep，action_args中会包含完整的上下文信息：
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- 点赞/踩的帖子原文
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- 转发/引用的帖子原文
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- 关注/屏蔽的用户名
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- 点赞/踩的评论原文
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"""
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# 批量发送大小（每个平台累积多少条后发送）
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BATCH_SIZE = 5
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# 平台名称映射（用于控制台显示）
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PLATFORM_DISPLAY_NAMES = {
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'twitter': '世界1',
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'reddit': '世界2',
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}
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# 发送间隔（秒），避免请求过快
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SEND_INTERVAL = 0.5
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# 重试配置
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MAX_RETRIES = 3
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RETRY_DELAY = 2 # 秒
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def __init__(self, graph_id: str, api_key: Optional[str] = None):
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"""
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初始化更新器
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Args:
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graph_id: Zep图谱ID
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api_key: Zep API Key（可选，默认从配置读取）
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"""
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self.graph_id = graph_id
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self.api_key = api_key or Config.ZEP_API_KEY
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if not self.api_key:
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raise ValueError("ZEP_API_KEY未配置")
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self.client = Zep(api_key=self.api_key)
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# 活动队列
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self._activity_queue: Queue = Queue()
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# 按平台分组的活动缓冲区（每个平台各自累积到BATCH_SIZE后批量发送）
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self._platform_buffers: Dict[str, List[AgentActivity]] = {
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'twitter': [],
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'reddit': [],
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}
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self._buffer_lock = threading.Lock()
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# 控制标志
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self._running = False
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self._worker_thread: Optional[threading.Thread] = None
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# 统计
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self._total_activities = 0 # 实际添加到队列的活动数
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self._total_sent = 0 # 成功发送到Zep的批次数
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self._total_items_sent = 0 # 成功发送到Zep的活动条数
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self._failed_count = 0 # 发送失败的批次数
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self._skipped_count = 0 # 被过滤跳过的活动数（DO_NOTHING）
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logger.info(f"ZepGraphMemoryUpdater 初始化完成: graph_id={graph_id}, batch_size={self.BATCH_SIZE}")
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def _get_platform_display_name(self, platform: str) -> str:
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"""获取平台的显示名称"""
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return self.PLATFORM_DISPLAY_NAMES.get(platform.lower(), platform)
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def start(self):
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"""启动后台工作线程"""
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if self._running:
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return
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# Capture locale before spawning background thread
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current_locale = get_locale()
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self._running = True
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self._worker_thread = threading.Thread(
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target=self._worker_loop,
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args=(current_locale,),
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daemon=True,
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name=f"ZepMemoryUpdater-{self.graph_id[:8]}"
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)
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self._worker_thread.start()
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logger.info(f"ZepGraphMemoryUpdater 已启动: graph_id={self.graph_id}")
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def stop(self):
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"""停止后台工作线程"""
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self._running = False
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# 发送剩余的活动
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self._flush_remaining()
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if self._worker_thread and self._worker_thread.is_alive():
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self._worker_thread.join(timeout=10)
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logger.info(f"ZepGraphMemoryUpdater 已停止: graph_id={self.graph_id}, "
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f"total_activities={self._total_activities}, "
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f"batches_sent={self._total_sent}, "
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f"items_sent={self._total_items_sent}, "
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f"failed={self._failed_count}, "
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f"skipped={self._skipped_count}")
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def add_activity(self, activity: AgentActivity):
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"""
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添加一个agent活动到队列
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所有有意义的行为都会被添加到队列，包括：
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- CREATE_POST（发帖）
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- CREATE_COMMENT（评论）
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- QUOTE_POST（引用帖子）
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- SEARCH_POSTS（搜索帖子）
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- SEARCH_USER（搜索用户）
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- LIKE_POST/DISLIKE_POST（点赞/踩帖子）
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- REPOST（转发）
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- FOLLOW（关注）
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- MUTE（屏蔽）
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- LIKE_COMMENT/DISLIKE_COMMENT（点赞/踩评论）
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action_args中会包含完整的上下文信息（如帖子原文、用户名等）。
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Args:
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activity: Agent活动记录
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"""
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# 跳过DO_NOTHING类型的活动
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if activity.action_type == "DO_NOTHING":
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self._skipped_count += 1
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return
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self._activity_queue.put(activity)
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self._total_activities += 1
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logger.debug(f"添加活动到Zep队列: {activity.agent_name} - {activity.action_type}")
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def add_activity_from_dict(self, data: Dict[str, Any], platform: str):
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"""
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从字典数据添加活动
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Args:
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data: 从actions.jsonl解析的字典数据
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platform: 平台名称 (twitter/reddit)
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"""
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# 跳过事件类型的条目
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if "event_type" in data:
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return
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activity = AgentActivity(
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platform=platform,
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agent_id=data.get("agent_id", 0),
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agent_name=data.get("agent_name", ""),
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action_type=data.get("action_type", ""),
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action_args=data.get("action_args", {}),
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round_num=data.get("round", 0),
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timestamp=data.get("timestamp", datetime.now().isoformat()),
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)
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self.add_activity(activity)
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def _worker_loop(self, locale: str = 'zh'):
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"""后台工作循环 - 按平台批量发送活动到Zep"""
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set_locale(locale)
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while self._running or not self._activity_queue.empty():
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try:
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# 尝试从队列获取活动（超时1秒）
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try:
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activity = self._activity_queue.get(timeout=1)
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# 将活动添加到对应平台的缓冲区
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platform = activity.platform.lower()
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with self._buffer_lock:
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if platform not in self._platform_buffers:
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self._platform_buffers[platform] = []
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self._platform_buffers[platform].append(activity)
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# 检查该平台是否达到批量大小
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if len(self._platform_buffers[platform]) >= self.BATCH_SIZE:
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batch = self._platform_buffers[platform][:self.BATCH_SIZE]
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self._platform_buffers[platform] = self._platform_buffers[platform][self.BATCH_SIZE:]
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# 释放锁后再发送
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self._send_batch_activities(batch, platform)
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# 发送间隔，避免请求过快
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time.sleep(self.SEND_INTERVAL)
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except Empty:
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pass
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except Exception as e:
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logger.error(f"工作循环异常: {e}")
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time.sleep(1)
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def _send_batch_activities(self, activities: List[AgentActivity], platform: str):
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"""
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批量发送活动到Zep图谱（合并为一条文本）
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Args:
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activities: Agent活动列表
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platform: 平台名称
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"""
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if not activities:
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return
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# 将多条活动合并为一条文本，用换行分隔
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episode_texts = [activity.to_episode_text() for activity in activities]
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combined_text = "\n".join(episode_texts)
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# 带重试的发送
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for attempt in range(self.MAX_RETRIES):
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try:
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self.client.graph.add(
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graph_id=self.graph_id,
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type="text",
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data=combined_text
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)
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self._total_sent += 1
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self._total_items_sent += len(activities)
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display_name = self._get_platform_display_name(platform)
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logger.info(f"成功批量发送 {len(activities)} 条{display_name}活动到图谱 {self.graph_id}")
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logger.debug(f"批量内容预览: {combined_text[:200]}...")
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return
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except Exception as e:
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if attempt < self.MAX_RETRIES - 1:
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logger.warning(f"批量发送到Zep失败 (尝试 {attempt + 1}/{self.MAX_RETRIES}): {e}")
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time.sleep(self.RETRY_DELAY * (attempt + 1))
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else:
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logger.error(f"批量发送到Zep失败，已重试{self.MAX_RETRIES}次: {e}")
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self._failed_count += 1
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def _flush_remaining(self):
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"""发送队列和缓冲区中剩余的活动"""
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# 首先处理队列中剩余的活动，添加到缓冲区
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while not self._activity_queue.empty():
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try:
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activity = self._activity_queue.get_nowait()
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platform = activity.platform.lower()
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with self._buffer_lock:
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if platform not in self._platform_buffers:
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self._platform_buffers[platform] = []
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self._platform_buffers[platform].append(activity)
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except Empty:
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break
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# 然后发送各平台缓冲区中剩余的活动（即使不足BATCH_SIZE条）
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with self._buffer_lock:
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for platform, buffer in self._platform_buffers.items():
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if buffer:
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display_name = self._get_platform_display_name(platform)
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logger.info(f"发送{display_name}平台剩余的 {len(buffer)} 条活动")
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self._send_batch_activities(buffer, platform)
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# 清空所有缓冲区
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for platform in self._platform_buffers:
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self._platform_buffers[platform] = []
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def get_stats(self) -> Dict[str, Any]:
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"""获取统计信息"""
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with self._buffer_lock:
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buffer_sizes = {p: len(b) for p, b in self._platform_buffers.items()}
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return {
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"graph_id": self.graph_id,
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"batch_size": self.BATCH_SIZE,
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"total_activities": self._total_activities, # 添加到队列的活动总数
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"batches_sent": self._total_sent, # 成功发送的批次数
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"items_sent": self._total_items_sent, # 成功发送的活动条数
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"failed_count": self._failed_count, # 发送失败的批次数
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"skipped_count": self._skipped_count, # 被过滤跳过的活动数（DO_NOTHING）
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"queue_size": self._activity_queue.qsize(),
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"buffer_sizes": buffer_sizes, # 各平台缓冲区大小
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"running": self._running,
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}
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class ZepGraphMemoryManager:
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"""
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管理多个模拟的Zep图谱记忆更新器
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每个模拟可以有自己的更新器实例
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"""
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_updaters: Dict[str, ZepGraphMemoryUpdater] = {}
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_lock = threading.Lock()
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@classmethod
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def create_updater(cls, simulation_id: str, graph_id: str) -> ZepGraphMemoryUpdater:
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"""
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为模拟创建图谱记忆更新器
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Args:
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simulation_id: 模拟ID
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graph_id: Zep图谱ID
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Returns:
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ZepGraphMemoryUpdater实例
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"""
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with cls._lock:
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# 如果已存在，先停止旧的
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if simulation_id in cls._updaters:
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cls._updaters[simulation_id].stop()
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updater = ZepGraphMemoryUpdater(graph_id)
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updater.start()
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cls._updaters[simulation_id] = updater
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logger.info(f"创建图谱记忆更新器: simulation_id={simulation_id}, graph_id={graph_id}")
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return updater
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@classmethod
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def get_updater(cls, simulation_id: str) -> Optional[ZepGraphMemoryUpdater]:
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"""获取模拟的更新器"""
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return cls._updaters.get(simulation_id)
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@classmethod
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def stop_updater(cls, simulation_id: str):
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"""停止并移除模拟的更新器"""
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with cls._lock:
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if simulation_id in cls._updaters:
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cls._updaters[simulation_id].stop()
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del cls._updaters[simulation_id]
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logger.info(f"已停止图谱记忆更新器: simulation_id={simulation_id}")
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# 防止 stop_all 重复调用的标志
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_stop_all_done = False
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@classmethod
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def stop_all(cls):
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"""停止所有更新器"""
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# 防止重复调用
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if cls._stop_all_done:
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return
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cls._stop_all_done = True
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with cls._lock:
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if cls._updaters:
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for simulation_id, updater in list(cls._updaters.items()):
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try:
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updater.stop()
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except Exception as e:
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logger.error(f"停止更新器失败: simulation_id={simulation_id}, error={e}")
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cls._updaters.clear()
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logger.info("已停止所有图谱记忆更新器")
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@classmethod
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def get_all_stats(cls) -> Dict[str, Dict[str, Any]]:
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"""获取所有更新器的统计信息"""
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return {
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sim_id: updater.get_stats()
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for sim_id, updater in cls._updaters.items()
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}
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