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
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@@ -10,15 +10,17 @@ import re
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import time
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import uuid
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from typing import Any, AsyncGenerator, Dict, List, Optional
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import redis
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from langchain_core.messages import HumanMessage
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import redis
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from app.core.config import settings
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from app.core.logging_config import get_config_logger, get_logger
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from app.core.memory.agent.langgraph_graph.read_graph import make_read_graph
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from app.core.memory.agent.langgraph_graph.write_graph import make_write_graph
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from app.core.memory.agent.logger_file.log_streamer import LogStreamer
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from app.core.memory.agent.utils.messages_tools import merge_multiple_search_results, reorder_output_results
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from app.core.memory.agent.utils.messages_tools import (
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merge_multiple_search_results,
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reorder_output_results,
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)
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from app.core.memory.agent.utils.type_classifier import status_typle
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from app.core.memory.agent.utils.write_tools import write # 新增:直接导入 write 函数
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from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags
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@@ -33,6 +35,7 @@ from app.services.memory_config_service import MemoryConfigService
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from app.services.memory_konwledges_server import (
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write_rag,
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)
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from langchain_core.messages import HumanMessage
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from pydantic import BaseModel, Field
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from sqlalchemy import func
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from sqlalchemy.orm import Session
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@@ -404,6 +407,7 @@ class MemoryAgentService:
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import time
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start_time = time.time()
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logger.info(f"[PERF] read_memory started for group_id={group_id}, search_switch={search_switch}")
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# Resolve config_id if None using end_user's connected config
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if config_id is None:
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@@ -427,13 +431,15 @@ class MemoryAgentService:
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audit_logger = None
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config_load_start = time.time()
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try:
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config_service = MemoryConfigService(db)
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memory_config = config_service.load_memory_config(
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config_id=config_id,
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service_name="MemoryAgentService"
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)
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logger.info(f"Configuration loaded successfully: {memory_config.config_name}")
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config_load_time = time.time() - config_load_start
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logger.info(f"[PERF] Configuration loaded in {config_load_time:.4f}s: {memory_config.config_name}")
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except ConfigurationError as e:
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error_msg = f"Failed to load configuration for config_id: {config_id}: {e}"
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logger.error(error_msg)
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@@ -457,6 +463,7 @@ class MemoryAgentService:
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logger.debug(f"Group ID:{group_id}, Message:{message}, History:{history}, Config ID:{config_id}")
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# Step 3: Initialize MCP client and execute read workflow
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graph_exec_start = time.time()
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try:
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async with make_read_graph() as graph:
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config = {"configurable": {"thread_id": group_id}}
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@@ -513,6 +520,9 @@ class MemoryAgentService:
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if summary_n and summary_n != [] and summary_n != {}:
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_intermediate_outputs.append(summary_n)
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graph_exec_time = time.time() - graph_exec_start
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logger.info(f"[PERF] Graph execution completed in {graph_exec_time:.4f}s")
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_intermediate_outputs = [item for item in _intermediate_outputs if item and item != [] and item != {}]
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optimized_outputs = merge_multiple_search_results(_intermediate_outputs)
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@@ -570,6 +580,8 @@ class MemoryAgentService:
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logger.error(f"保存短期记忆失败: {str(save_error)}", exc_info=True)
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# Log successful operation
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total_time = time.time() - start_time
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logger.info(f"[PERF] read_memory completed successfully in {total_time:.4f}s (config: {config_load_time:.4f}s, graph: {graph_exec_time:.4f}s)")
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if audit_logger:
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duration = time.time() - start_time
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audit_logger.log_operation(
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@@ -587,7 +599,8 @@ class MemoryAgentService:
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except Exception as e:
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# Ensure proper error handling and logging
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error_msg = f"Read operation failed: {str(e)}"
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logger.error(error_msg)
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total_time = time.time() - start_time
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logger.error(f"[PERF] read_memory failed after {total_time:.4f}s: {error_msg}")
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if audit_logger:
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duration = time.time() - start_time
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audit_logger.log_operation(
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