Fix/memory celery fix (#168)
* refactor(celery): optimize task routing and worker configuration - Simplify Celery queue configuration with single default 'io_tasks' queue - Implement task routing strategy separating IO-bound and CPU-bound tasks - Add Flower monitoring support with task event tracking enabled - Add summary node search optimization to only retrieve summary nodes - Clean up unused imports and reorganize import statements for consistency - Update docker-compose configuration to support multi-queue worker setup * chore(celery): simplify flower configuration and add gevent dependency * chore(dependencies): add gevent dependency to requirements - Add gevent==24.11.1 to api/requirements.txt - Gevent is required for async worker support in Celery - Complements existing flower and celery configuration * refactor(celery): simplify async event loop handling and reorganize task queues - Replace complex nest_asyncio and manual event loop management with asyncio.run() in read_message_task, write_message_task, regenerate_memory_cache, and workspace_reflection_task - Rename task queues from io_tasks/cpu_tasks to memory_tasks/document_tasks for better semantic clarity - Update task routing configuration to reflect new queue names for memory agent tasks and document processing tasks - Remove redundant exception handling comments and simplify error handling logic - Update README with improved community support section including GitHub Issues, Pull Requests, Discussions, and WeChat community links - Simplifies event loop management by leveraging asyncio.run() which handles loop creation and cleanup automatically, reducing code complexity and potential race conditions
This commit is contained in:
Ke Sun
@@ -1,4 +1,5 @@
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import os
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import platform
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from datetime import timedelta
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from urllib.parse import quote
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@@ -14,27 +15,12 @@ celery_app = Celery(
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backend=f"redis://:{quote(settings.REDIS_PASSWORD)}@{settings.REDIS_HOST}:{settings.REDIS_PORT}/{settings.CELERY_BACKEND}",
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)
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# 配置使用本地队列，避免与远程 worker 冲突
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celery_app.conf.task_default_queue = 'localhost_test_wyl'
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celery_app.conf.task_default_exchange = 'localhost_test_wyl'
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celery_app.conf.task_default_routing_key = 'localhost_test_wyl'
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# Default queue for unrouted tasks
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celery_app.conf.task_default_queue = 'memory_tasks'
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# macOS 兼容性配置
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import platform
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if platform.system() == 'Darwin': # macOS
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# 设置环境变量解决 fork 问题
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if platform.system() == 'Darwin':
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os.environ.setdefault('OBJC_DISABLE_INITIALIZE_FORK_SAFETY', 'YES')
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# 使用 solo 池避免多进程问题
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celery_app.conf.worker_pool = 'solo'
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# 设置唯一的节点名称
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import socket
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import time
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hostname = socket.gethostname()
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timestamp = int(time.time())
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celery_app.conf.worker_name = f"celery@{hostname}-{timestamp}"
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# Celery 配置
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celery_app.conf.update(
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@@ -52,36 +38,47 @@ celery_app.conf.update(
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task_ignore_result=False,
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# 超时设置
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task_time_limit=30 * 60, # 30 分钟硬超时
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task_soft_time_limit=25 * 60, # 25 分钟软超时
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task_time_limit=1800, # 30分钟硬超时
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task_soft_time_limit=1500, # 25分钟软超时
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# Worker 设置 - 针对 macOS 优化
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worker_prefetch_multiplier=1, # 减少预取任务数，避免内存堆积
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worker_max_tasks_per_child=10, # 大幅减少每个 worker 执行的任务数，频繁重启防止内存泄漏
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worker_max_memory_per_child=200000, # 200MB 内存限制，超过后重启 worker
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# Worker 设置 (per-worker settings are in docker-compose command line)
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worker_prefetch_multiplier=1, # Don't hoard tasks, fairer distribution
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# 结果过期时间
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result_expires=3600, # 结果保存 1 小时
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result_expires=3600, # 结果保存1小时
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# 任务确认设置
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task_acks_late=True, # 任务完成后才确认，避免任务丢失
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worker_disable_rate_limits=True, # 禁用速率限制
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task_acks_late=True,
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task_reject_on_worker_lost=True,
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worker_disable_rate_limits=True,
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# 任务路由（可选，用于不同队列）
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# task_routes={
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# 'app.core.rag.tasks.parse_document': {'queue': 'document_processing'},
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# 'app.core.memory.agent.read_message': {'queue': 'memory_processing'},
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# 'app.core.memory.agent.write_message': {'queue': 'memory_processing'},
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# 'tasks.process_item': {'queue': 'default'},
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# },
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# FLower setting
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worker_send_task_events=True,
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task_send_sent_event=True,
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# task routing
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task_routes={
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# Memory tasks → memory_tasks queue (threads worker)
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'app.core.memory.agent.read_message_priority': {'queue': 'memory_tasks'},
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'app.core.memory.agent.read_message': {'queue': 'memory_tasks'},
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'app.core.memory.agent.write_message': {'queue': 'memory_tasks'},
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# Document tasks → document_tasks queue (prefork worker)
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'app.core.rag.tasks.parse_document': {'queue': 'document_tasks'},
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'app.core.rag.tasks.build_graphrag_for_kb': {'queue': 'document_tasks'},
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# Beat/periodic tasks → document_tasks queue (prefork worker)
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'app.tasks.workspace_reflection_task': {'queue': 'document_tasks'},
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'app.tasks.regenerate_memory_cache': {'queue': 'document_tasks'},
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'app.tasks.run_forgetting_cycle_task': {'queue': 'document_tasks'},
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'app.controllers.memory_storage_controller.search_all': {'queue': 'document_tasks'},
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},
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)
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# 自动发现任务模块
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celery_app.autodiscover_tasks(['app'])
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# Celery Beat schedule for periodic tasks
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reflection_schedule = timedelta(seconds=settings.REFLECTION_INTERVAL_SECONDS)
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health_schedule = timedelta(seconds=settings.HEALTH_CHECK_SECONDS)
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memory_increment_schedule = timedelta(hours=settings.MEMORY_INCREMENT_INTERVAL_HOURS)
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memory_cache_regeneration_schedule = timedelta(hours=settings.MEMORY_CACHE_REGENERATION_HOURS)
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workspace_reflection_schedule = timedelta(seconds=30) # 每30秒运行一次settings.REFLECTION_INTERVAL_TIME
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@@ -89,12 +86,6 @@ forgetting_cycle_schedule = timedelta(hours=24) # 每24小时运行一次遗忘
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# 构建定时任务配置
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beat_schedule_config = {
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# "check-read-service": {
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# "task": "app.core.memory.agent.health.check_read_service",
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# "schedule": health_schedule,
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# "args": (),
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# },
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"run-workspace-reflection": {
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"task": "app.tasks.workspace_reflection_task",
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"schedule": workspace_reflection_schedule,
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