Merge pull request #990 from SuanmoSuanyangTechnology/feature/celery-task-scheduler

Feature/celery task scheduler

api/app/celery_app.py

@@ -17,6 +17,7 @@ def _mask_url(url: str) -> str:
     """隐藏 URL 中的密码部分，适用于 redis:// 和 amqp:// 等协议"""
     return re.sub(r'(://[^:]*:)[^@]+(@)', r'\1***\2', url)
 
+
 # macOS fork() safety - must be set before any Celery initialization
 if platform.system() == 'Darwin':
     os.environ.setdefault('OBJC_DISABLE_INITIALIZE_FORK_SAFETY', 'YES')
@@ -29,7 +30,7 @@ if platform.system() == 'Darwin':
 #       这些名称会被 Celery CLI 的 Click 框架劫持，详见 docs/celery-env-bug-report.md
 
 _broker_url = os.getenv("CELERY_BROKER_URL") or \
-    f"redis://:{quote(settings.REDIS_PASSWORD)}@{settings.REDIS_HOST}:{settings.REDIS_PORT}/{settings.REDIS_DB_CELERY_BROKER}"
+              f"redis://:{quote(settings.REDIS_PASSWORD)}@{settings.REDIS_HOST}:{settings.REDIS_PORT}/{settings.REDIS_DB_CELERY_BROKER}"
 _backend_url = f"redis://:{quote(settings.REDIS_PASSWORD)}@{settings.REDIS_HOST}:{settings.REDIS_PORT}/{settings.REDIS_DB_CELERY_BACKEND}"
 os.environ["CELERY_BROKER_URL"] = _broker_url
 os.environ["CELERY_RESULT_BACKEND"] = _backend_url
@@ -66,11 +67,11 @@ celery_app.conf.update(
     task_serializer='json',
     accept_content=['json'],
     result_serializer='json',
-    
+
     # # 时区
     # timezone='Asia/Shanghai',
     # enable_utc=False,
-    
+
     # 任务追踪
     task_track_started=True,
     task_ignore_result=False,

api/app/celery_task_scheduler.py

@@ -0,0 +1,500 @@
+import hashlib
+import json
+import os
+import socket
+import threading
+import time
+import uuid
+
+import redis
+
+from app.core.config import settings
+from app.core.logging_config import get_named_logger
+from app.celery_app import celery_app
+
+logger = get_named_logger("task_scheduler")
+
+# per-user queue scheduler:uq:{user_id}
+USER_QUEUE_PREFIX = "scheduler:uq:"
+# User Collection of Pending Messages
+ACTIVE_USERS = "scheduler:active_users"
+# Set of users that can dispatch (ready signal)
+READY_SET = "scheduler:ready_users"
+# Metadata of tasks that have been dispatched and are pending completion
+PENDING_HASH = "scheduler:pending_tasks"
+# Dynamic Sharding: Instance Registry
+REGISTRY_KEY = "scheduler:instances"
+
+TASK_TIMEOUT = 7800  # Task timeout (seconds), considered lost if exceeded
+HEARTBEAT_INTERVAL = 10  # Heartbeat interval (seconds)
+INSTANCE_TTL = 30  # Instance timeout (seconds)
+
+LUA_ATOMIC_LOCK = """
+local dispatch_lock = KEYS[1]
+local lock_key = KEYS[2]
+local instance_id = ARGV[1]
+local dispatch_ttl = tonumber(ARGV[2])
+local lock_ttl = tonumber(ARGV[3])
+
+if redis.call('SET', dispatch_lock, instance_id, 'NX', 'EX', dispatch_ttl) == false then
+    return 0
+end
+
+if redis.call('EXISTS', lock_key) == 1 then
+    redis.call('DEL', dispatch_lock)
+    return -1
+end
+
+redis.call('SET', lock_key, 'dispatching', 'EX', lock_ttl)
+return 1
+"""
+
+LUA_SAFE_DELETE = """
+if redis.call('GET', KEYS[1]) == ARGV[1] then
+    return redis.call('DEL', KEYS[1])
+end
+return 0
+"""
+
+
+def stable_hash(value: str) -> int:
+    return int.from_bytes(
+        hashlib.md5(value.encode("utf-8")).digest(),
+        "big"
+    )
+
+
+def health_check_server(scheduler_ref):
+    import uvicorn
+    from fastapi import FastAPI
+
+    health_app = FastAPI()
+
+    @health_app.get("/")
+    def health():
+        return scheduler_ref.health()
+
+    port = int(os.environ.get("SCHEDULER_HEALTH_PORT", "8001"))
+    threading.Thread(
+        target=uvicorn.run,
+        kwargs={
+            "app": health_app,
+            "host": "0.0.0.0",
+            "port": port,
+            "log_config": None,
+        },
+        daemon=True,
+    ).start()
+    logger.info("[Health] Server started at http://0.0.0.0:%s", port)
+
+
+class RedisTaskScheduler:
+    def __init__(self):
+        self.redis = redis.Redis(
+            host=settings.REDIS_HOST,
+            port=settings.REDIS_PORT,
+            db=settings.REDIS_DB_CELERY_BACKEND,
+            password=settings.REDIS_PASSWORD,
+            decode_responses=True,
+        )
+        self.running = False
+        self.dispatched = 0
+        self.errors = 0
+
+        self.instance_id = f"{socket.gethostname()}-{os.getpid()}"
+        self._shard_index = 0
+        self._shard_count = 1
+        self._last_heartbeat = 0.0
+
+    def push_task(self, task_name, user_id, params):
+        try:
+            msg_id = str(uuid.uuid4())
+            msg = json.dumps({
+                "msg_id": msg_id,
+                "task_name": task_name,
+                "user_id": user_id,
+                "params": json.dumps(params),
+            })
+
+            lock_key = f"{task_name}:{user_id}"
+            queue_key = f"{USER_QUEUE_PREFIX}{user_id}"
+
+            pipe = self.redis.pipeline()
+            pipe.rpush(queue_key, msg)
+            pipe.sadd(ACTIVE_USERS, user_id)
+            pipe.set(
+                f"task_tracker:{msg_id}",
+                json.dumps({"status": "QUEUED", "task_id": None}),
+                ex=86400,
+            )
+            pipe.execute()
+
+            if not self.redis.exists(lock_key):
+                self.redis.sadd(READY_SET, user_id)
+
+            logger.info("Task pushed: msg_id=%s task=%s user=%s", msg_id, task_name, user_id)
+            return msg_id
+        except Exception as e:
+            logger.error("Push task exception %s", e, exc_info=True)
+            raise
+
+    def get_task_status(self, msg_id: str) -> dict:
+        raw = self.redis.get(f"task_tracker:{msg_id}")
+        if raw is None:
+            return {"status": "NOT_FOUND"}
+
+        tracker = json.loads(raw)
+        status = tracker["status"]
+        task_id = tracker.get("task_id")
+        result_content = tracker.get("result") or {}
+
+        if status == "DISPATCHED" and task_id:
+            result_raw = self.redis.get(f"celery-task-meta-{task_id}")
+            if result_raw:
+                result_data = json.loads(result_raw)
+                status = result_data.get("status", status)
+                result_content = result_data.get("result")
+
+        return {"status": status, "task_id": task_id, "result": result_content}
+
+    def _cleanup_finished(self):
+        pending = self.redis.hgetall(PENDING_HASH)
+        if not pending:
+            return
+
+        now = time.time()
+        task_ids = list(pending.keys())
+
+        pipe = self.redis.pipeline()
+        for task_id in task_ids:
+            pipe.get(f"celery-task-meta-{task_id}")
+        results = pipe.execute()
+
+        cleanup_pipe = self.redis.pipeline()
+        has_cleanup = False
+        ready_user_ids = set()
+
+        for task_id, raw_result in zip(task_ids, results):
+            try:
+                meta = json.loads(pending[task_id])
+                lock_key = meta["lock_key"]
+                dispatched_at = meta.get("dispatched_at", 0)
+                age = now - dispatched_at
+
+                should_cleanup = False
+                result_data = {}
+
+                if raw_result is not None:
+                    result_data = json.loads(raw_result)
+                    if result_data.get("status") in ("SUCCESS", "FAILURE", "REVOKED"):
+                        should_cleanup = True
+                        logger.info(
+                            "Task finished: %s state=%s", task_id,
+                            result_data.get("status"),
+                        )
+                elif age > TASK_TIMEOUT:
+                    should_cleanup = True
+                    logger.warning(
+                        "Task expired or lost: %s age=%.0fs, force cleanup",
+                        task_id, age,
+                    )
+
+                if should_cleanup:
+                    final_status = (
+                        result_data.get("status", "UNKNOWN") if result_data else "EXPIRED"
+                    )
+
+                    self.redis.eval(LUA_SAFE_DELETE, 1, lock_key, task_id)
+
+                    cleanup_pipe.hdel(PENDING_HASH, task_id)
+
+                    tracker_msg_id = meta.get("msg_id")
+                    if tracker_msg_id:
+                        cleanup_pipe.set(
+                            f"task_tracker:{tracker_msg_id}",
+                            json.dumps({
+                                "status": final_status,
+                                "task_id": task_id,
+                                "result": result_data.get("result") or {},
+                            }),
+                            ex=86400,
+                        )
+                    has_cleanup = True
+
+                    parts = lock_key.split(":", 1)
+                    if len(parts) == 2:
+                        ready_user_ids.add(parts[1])
+
+            except Exception as e:
+                logger.error("Cleanup error for %s: %s", task_id, e, exc_info=True)
+                self.errors += 1
+
+        if has_cleanup:
+            cleanup_pipe.execute()
+
+        if ready_user_ids:
+            self.redis.sadd(READY_SET, *ready_user_ids)
+
+    def _heartbeat(self):
+        now = time.time()
+        if now - self._last_heartbeat < HEARTBEAT_INTERVAL:
+            return
+        self._last_heartbeat = now
+
+        self.redis.hset(REGISTRY_KEY, self.instance_id, str(now))
+
+        all_instances = self.redis.hgetall(REGISTRY_KEY)
+
+        alive = []
+        dead = []
+        for iid, ts in all_instances.items():
+            if now - float(ts) < INSTANCE_TTL:
+                alive.append(iid)
+            else:
+                dead.append(iid)
+
+        if dead:
+            pipe = self.redis.pipeline()
+            for iid in dead:
+                pipe.hdel(REGISTRY_KEY, iid)
+            pipe.execute()
+            logger.info("Cleaned dead instances: %s", dead)
+
+        alive.sort()
+        self._shard_count = max(len(alive), 1)
+        self._shard_index = (
+            alive.index(self.instance_id) if self.instance_id in alive else 0
+        )
+        logger.debug(
+            "Shard: %s/%s (instance=%s, alive=%d)",
+            self._shard_index, self._shard_count,
+            self.instance_id, len(alive),
+        )
+
+    def _is_mine(self, user_id: str) -> bool:
+        if self._shard_count <= 1:
+            return True
+        return stable_hash(user_id) % self._shard_count == self._shard_index
+
+    def _dispatch(self, msg_id, msg_data) -> bool:
+        user_id = msg_data["user_id"]
+        task_name = msg_data["task_name"]
+        params = json.loads(msg_data.get("params", "{}"))
+
+        lock_key = f"{task_name}:{user_id}"
+        dispatch_lock = f"dispatch:{msg_id}"
+
+        result = self.redis.eval(
+            LUA_ATOMIC_LOCK, 2,
+            dispatch_lock, lock_key,
+            self.instance_id, str(300), str(3600),
+        )
+
+        if result == 0:
+            return False
+        if result == -1:
+            return False
+
+        try:
+            task = celery_app.send_task(task_name, kwargs=params)
+        except Exception as e:
+            pipe = self.redis.pipeline()
+            pipe.delete(dispatch_lock)
+            pipe.delete(lock_key)
+            pipe.execute()
+            self.errors += 1
+            logger.error(
+                "send_task failed for %s:%s msg=%s: %s",
+                task_name, user_id, msg_id, e, exc_info=True,
+            )
+            return False
+
+        try:
+            pipe = self.redis.pipeline()
+            pipe.set(lock_key, task.id, ex=3600)
+            pipe.hset(PENDING_HASH, task.id, json.dumps({
+                "lock_key": lock_key,
+                "dispatched_at": time.time(),
+                "msg_id": msg_id,
+            }))
+            pipe.delete(dispatch_lock)
+            pipe.set(
+                f"task_tracker:{msg_id}",
+                json.dumps({"status": "DISPATCHED", "task_id": task.id}),
+                ex=86400,
+            )
+            pipe.execute()
+        except Exception as e:
+            logger.error(
+                "Post-dispatch state update failed for %s: %s",
+                task.id, e, exc_info=True,
+            )
+            self.errors += 1
+
+        self.dispatched += 1
+        logger.info("Task dispatched: %s (msg=%s)", task.id, msg_id)
+        return True
+
+    def _process_batch(self, user_ids):
+        if not user_ids:
+            return
+
+        pipe = self.redis.pipeline()
+        for uid in user_ids:
+            pipe.lindex(f"{USER_QUEUE_PREFIX}{uid}", 0)
+        heads = pipe.execute()
+
+        candidates = []  # (user_id, msg_dict)
+        empty_users = []
+
+        for uid, head in zip(user_ids, heads):
+            if head is None:
+                empty_users.append(uid)
+            else:
+                try:
+                    candidates.append((uid, json.loads(head)))
+                except (json.JSONDecodeError, TypeError) as e:
+                    logger.error("Bad message in queue for user %s: %s", uid, e)
+                    self.redis.lpop(f"{USER_QUEUE_PREFIX}{uid}")
+
+        if empty_users:
+            pipe = self.redis.pipeline()
+            for uid in empty_users:
+                pipe.srem(ACTIVE_USERS, uid)
+            pipe.execute()
+
+        if not candidates:
+            return
+
+        for uid, msg in candidates:
+            if self._dispatch(msg["msg_id"], msg):
+                self.redis.lpop(f"{USER_QUEUE_PREFIX}{uid}")
+
+    def schedule_loop(self):
+        self._heartbeat()
+        self._cleanup_finished()
+
+        pipe = self.redis.pipeline()
+        pipe.smembers(READY_SET)
+        pipe.delete(READY_SET)
+        results = pipe.execute()
+        ready_users = results[0] or set()
+
+        my_users = [uid for uid in ready_users if self._is_mine(uid)]
+
+        if not my_users:
+            time.sleep(0.5)
+            return
+
+        self._process_batch(my_users)
+        time.sleep(0.1)
+
+    def _full_scan(self):
+        cursor = 0
+        ready_batch = []
+        while True:
+            cursor, user_ids = self.redis.sscan(
+                ACTIVE_USERS, cursor=cursor, count=1000,
+            )
+            if user_ids:
+                my_users = [uid for uid in user_ids if self._is_mine(uid)]
+                if my_users:
+                    pipe = self.redis.pipeline()
+                    for uid in my_users:
+                        pipe.lindex(f"{USER_QUEUE_PREFIX}{uid}", 0)
+                    heads = pipe.execute()
+
+                    for uid, head in zip(my_users, heads):
+                        if head is None:
+                            continue
+                        try:
+                            msg = json.loads(head)
+                            lock_key = f"{msg['task_name']}:{uid}"
+                            ready_batch.append((uid, lock_key))
+                        except (json.JSONDecodeError, TypeError):
+                            continue
+
+            if cursor == 0:
+                break
+
+        if not ready_batch:
+            return
+
+        pipe = self.redis.pipeline()
+        for _, lock_key in ready_batch:
+            pipe.exists(lock_key)
+        lock_exists = pipe.execute()
+
+        ready_uids = [
+            uid for (uid, _), locked in zip(ready_batch, lock_exists)
+            if not locked
+        ]
+
+        if ready_uids:
+            self.redis.sadd(READY_SET, *ready_uids)
+            logger.info("Full scan found %d ready users", len(ready_uids))
+
+    def run_server(self):
+        health_check_server(self)
+        self.running = True
+
+        last_full_scan = 0.0
+        full_scan_interval = 30.0
+
+        logger.info(
+            "Scheduler started: instance=%s", self.instance_id,
+        )
+
+        while True:
+            try:
+                self.schedule_loop()
+
+                now = time.time()
+                if now - last_full_scan > full_scan_interval:
+                    self._full_scan()
+                    last_full_scan = now
+
+            except Exception as e:
+                logger.error("Scheduler exception %s", e, exc_info=True)
+                self.errors += 1
+                time.sleep(5)
+
+    def health(self) -> dict:
+        return {
+            "running": self.running,
+            "active_users": self.redis.scard(ACTIVE_USERS),
+            "ready_users": self.redis.scard(READY_SET),
+            "pending_tasks": self.redis.hlen(PENDING_HASH),
+            "dispatched": self.dispatched,
+            "errors": self.errors,
+            "shard": f"{self._shard_index}/{self._shard_count}",
+            "instance": self.instance_id,
+        }
+
+    def shutdown(self):
+        logger.info("Scheduler shutting down: instance=%s", self.instance_id)
+        self.running = False
+        try:
+            self.redis.hdel(REGISTRY_KEY, self.instance_id)
+        except Exception as e:
+            logger.error("Shutdown cleanup error: %s", e)
+
+
+scheduler: RedisTaskScheduler | None = None
+if scheduler is None:
+    scheduler = RedisTaskScheduler()
+
+if __name__ == "__main__":
+    import signal
+    import sys
+
+
+    def _signal_handler(signum, frame):
+        scheduler.shutdown()
+        sys.exit(0)
+
+
+    signal.signal(signal.SIGTERM, _signal_handler)
+    signal.signal(signal.SIGINT, _signal_handler)
+
+    scheduler.run_server()

api/app/controllers/service/memory_api_controller.py

@@ -18,6 +18,7 @@ from app.schemas.memory_api_schema import (
     MemoryWriteSyncResponse,
 )
 from app.services.memory_api_service import MemoryAPIService
+from celery_task_scheduler import scheduler
 
 router = APIRouter(prefix="/memory", tags=["V1 - Memory API"])
 logger = get_business_logger()
@@ -86,7 +87,7 @@ async def write_memory(
         user_rag_memory_id=payload.user_rag_memory_id,
     )
 
-    logger.info(f"Memory write task submitted: task_id={result['task_id']}, end_user_id: {payload.end_user_id}")
+    logger.info(f"Memory write task submitted: task_id: {result['task_id']} end_user_id: {payload.end_user_id}")
     return success(data=MemoryWriteResponse(**result).model_dump(), msg="Memory write task submitted")
 
 
@@ -105,8 +106,7 @@ async def get_write_task_status(
     """
     logger.info(f"Write task status check - task_id: {task_id}")
 
-    from app.services.task_service import get_task_memory_write_result
-    result = get_task_memory_write_result(task_id)
+    result = scheduler.get_task_status(task_id)
 
     return success(data=_sanitize_task_result(result), msg="Task status retrieved")
 

api/app/core/memory/agent/langgraph_graph/routing/write_router.py

@@ -12,9 +12,8 @@ from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 from app.db import get_db_context
 from app.repositories.memory_short_repository import LongTermMemoryRepository
 from app.schemas.memory_agent_schema import AgentMemory_Long_Term
-from app.services.task_service import get_task_memory_write_result
-from app.tasks import write_message_task
 from app.utils.config_utils import resolve_config_id
+from celery_task_scheduler import scheduler
 
 logger = get_agent_logger(__name__)
 template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
@@ -86,16 +85,28 @@ async def write(
 
         logger.info(
             f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}")
-        write_id = write_message_task.delay(
-            actual_end_user_id,  # end_user_id: User ID
-            structured_messages,  # message: JSON string format message list
-            str(actual_config_id),  # config_id: Configuration ID string
-            storage_type,  # storage_type: "neo4j"
-            user_rag_memory_id or ""  # user_rag_memory_id: RAG memory ID (not used in Neo4j mode)
+        # write_id = write_message_task.delay(
+        #     actual_end_user_id,  # end_user_id: User ID
+        #     structured_messages,  # message: JSON string format message list
+        #     str(actual_config_id),  # config_id: Configuration ID string
+        #     storage_type,  # storage_type: "neo4j"
+        #     user_rag_memory_id or ""  # user_rag_memory_id: RAG memory ID (not used in Neo4j mode)
+        # )
+        scheduler.push_task(
+            "app.core.memory.agent.write_message",
+            actual_end_user_id,
+            {
+                "end_user_id": actual_end_user_id,
+                "message": structured_messages,
+                "config_id": str(actual_config_id),
+                "storage_type": storage_type,
+                "user_rag_memory_id": user_rag_memory_id or ""
+            }
         )
-        logger.info(f"[WRITE] Celery task submitted - task_id={write_id}")
-        write_status = get_task_memory_write_result(str(write_id))
-        logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}')
+
+        # logger.info(f"[WRITE] Celery task submitted - task_id={write_id}")
+        # write_status = get_task_memory_write_result(str(write_id))
+        # logger.info(f'[WRITE] Task result - user={actual_end_user_id}')
 
 
 async def term_memory_save(end_user_id, strategy_type, scope):
@@ -164,13 +175,24 @@ async def window_dialogue(end_user_id, langchain_messages, memory_config, scope)
         else:
             config_id = memory_config
 
-        write_message_task.delay(
-            end_user_id,  # end_user_id: User ID
-            redis_messages,  # message: JSON string format message list
-            config_id,  # config_id: Configuration ID string
-            AgentMemory_Long_Term.STORAGE_NEO4J,  # storage_type: "neo4j"
-            ""  # user_rag_memory_id: RAG memory ID (not used in Neo4j mode)
+        scheduler.push_task(
+            "app.core.memory.agent.write_message",
+            end_user_id,
+            {
+                "end_user_id": end_user_id,
+                "message": redis_messages,
+                "config_id": config_id,
+                "storage_type": AgentMemory_Long_Term.STORAGE_NEO4J,
+                "user_rag_memory_id": ""
+            }
         )
+        # write_message_task.delay(
+        #     end_user_id,  # end_user_id: User ID
+        #     redis_messages,  # message: JSON string format message list
+        #     config_id,  # config_id: Configuration ID string
+        #     AgentMemory_Long_Term.STORAGE_NEO4J,  # storage_type: "neo4j"
+        #     ""  # user_rag_memory_id: RAG memory ID (not used in Neo4j mode)
+        # )
         count_store.update_sessions_count(end_user_id, 0, [])
 
 

api/app/core/memory/pipelines/memory_read.py

@@ -1,8 +1,8 @@
 from app.core.memory.enums import SearchStrategy, StorageType
 from app.core.memory.models.service_models import MemorySearchResult
 from app.core.memory.pipelines.base_pipeline import ModelClientMixin, DBRequiredPipeline
-from app.core.memory.read_services.content_search import Neo4jSearchService, RAGSearchService
-from app.core.memory.read_services.query_preprocessor import QueryPreprocessor
+from core.memory.read_services.search_engine.content_search import Neo4jSearchService, RAGSearchService
+from core.memory.read_services.generate_engine.query_preprocessor import QueryPreprocessor
 
 
 class ReadPipeLine(ModelClientMixin, DBRequiredPipeline):

api/app/core/memory/read_services/generate_engine/retrieval_summary.py

@@ -8,4 +8,4 @@ class RetrievalSummaryProcessor:
 
     @staticmethod
     def verify(content: str, llm_client: RedBearLLM):
-        return
+        return

api/app/core/memory/read_services/search_engine/content_search.py

@@ -8,7 +8,7 @@ from neo4j import Session
 from app.core.memory.enums import Neo4jNodeType
 from app.core.memory.memory_service import MemoryContext
 from app.core.memory.models.service_models import Memory, MemorySearchResult
-from app.core.memory.read_services.result_builder import data_builder_factory
+from core.memory.read_services.search_engine.result_builder import data_builder_factory
 from app.core.models import RedBearEmbeddings
 from app.core.rag.nlp.search import knowledge_retrieval
 from app.repositories import knowledge_repository

api/app/core/workflow/nodes/memory/node.py

@@ -11,7 +11,7 @@ from app.core.workflow.variable.base_variable import VariableType
 from app.core.workflow.variable.variable_objects import FileVariable, ArrayVariable
 from app.db import get_db_read
 from app.schemas import FileInput
-from app.tasks import write_message_task
+from celery_task_scheduler import scheduler
 
 
 class MemoryReadNode(BaseNode):
@@ -126,12 +126,23 @@ class MemoryWriteNode(BaseNode):
                 "files": file_info
             })
 
-        write_message_task.delay(
-            end_user_id=end_user_id,
-            message=messages,
-            config_id=str(self.typed_config.config_id),
-            storage_type=state["memory_storage_type"],
-            user_rag_memory_id=state["user_rag_memory_id"]
+        scheduler.push_task(
+            "app.core.memory.agent.write_message",
+            end_user_id,
+            {
+                "end_user_id": end_user_id,
+                "message": messages,
+                "config_id": str(self.typed_config.config_id),
+                "storage_type": state["memory_storage_type"],
+                "user_rag_memory_id": state["user_rag_memory_id"]
+            }
         )
+        # write_message_task.delay(
+        #     end_user_id=end_user_id,
+        #     message=messages,
+        #     config_id=str(self.typed_config.config_id),
+        #     storage_type=state["memory_storage_type"],
+        #     user_rag_memory_id=state["user_rag_memory_id"]
+        # )
 
         return "success"

api/app/schemas/memory_api_schema.py

@@ -112,12 +112,12 @@ class MemoryWriteResponse(BaseModel):
     """Response schema for memory write operation.
     
     Attributes:
-        task_id: Celery task ID for status polling
-        status: Initial task status (PENDING)
+        task_id: task ID for status polling
+        status: Initial task status (QUEUED)
         end_user_id: End user ID the write was submitted for
     """
-    task_id: str = Field(..., description="Celery task ID for polling")
-    status: str = Field(..., description="Task status: PENDING")
+    task_id: str = Field(..., description="task ID for polling")
+    status: str = Field(..., description="Task status: QUEUED")
     end_user_id: str = Field(..., description="End user ID")
 
 

api/app/services/memory_api_service.py

@@ -10,6 +10,7 @@ from typing import Any, Dict, Optional
 
 from sqlalchemy.orm import Session
 
+from app.celery_task_scheduler import scheduler
 from app.core.error_codes import BizCode
 from app.core.exceptions import BusinessException, ResourceNotFoundException
 from app.core.logging_config import get_logger
@@ -166,20 +167,31 @@ class MemoryAPIService:
         # Convert to message list format expected by write_message_task
         messages = message if isinstance(message, list) else [{"role": "user", "content": message}]
 
-        from app.tasks import write_message_task
-        task = write_message_task.delay(
+        # from app.tasks import write_message_task
+        # task = write_message_task.delay(
+        #     end_user_id,
+        #     messages,
+        #     config_id,
+        #     storage_type,
+        #     user_rag_memory_id or "",
+        # )
+        task_id = scheduler.push_task(
+            "app.core.memory.agent.write_message",
             end_user_id,
-            messages,
-            config_id,
-            storage_type,
-            user_rag_memory_id or "",
+            {
+                "end_user_id": end_user_id,
+                "message": messages,
+                "config_id": config_id,
+                "storage_type": storage_type,
+                "user_rag_memory_id": user_rag_memory_id or ""
+            }
         )
 
-        logger.info(f"Memory write task submitted: task_id={task.id}, end_user_id={end_user_id}")
+        logger.info(f"Memory write task submitted, task_id={task_id} end_user_id={end_user_id}")
 
         return {
-            "task_id": task.id,
-            "status": "PENDING",
+            "task_id": task_id,
+            "status": "QUEUED",
             "end_user_id": end_user_id,
         }
 

api/app/tasks.py

@@ -34,7 +34,7 @@ from app.core.rag.prompts.generator import question_proposal
 from app.core.rag.vdb.elasticsearch.elasticsearch_vector import (
     ElasticSearchVectorFactory,
 )
-from app.db import get_db, get_db_context
+from app.db import get_db_context
 from app.models import Document, File, Knowledge
 from app.models.end_user_model import EndUser
 from app.schemas import document_schema, file_schema
@@ -2025,7 +2025,7 @@ def run_forgetting_cycle_task(self, config_id: Optional[uuid.UUID] = None) -> Di
             end_users = db.query(EndUser).all()
             if not end_users:
                 logger.info("没有终端用户，跳过遗忘周期")
-                return {"status": "SUCCESS", "message": "没有终端用户", 
+                return {"status": "SUCCESS", "message": "没有终端用户",
                         "report": {"merged_count": 0, "failed_count": 0, "processed_users": 0},
                         "duration_seconds": time.time() - start_time}
 
@@ -2039,7 +2039,7 @@ def run_forgetting_cycle_task(self, config_id: Optional[uuid.UUID] = None) -> Di
                     # 获取用户配置（自动回退到工作空间默认配置）
                     connected_config = get_end_user_connected_config(str(end_user.id), db)
                     user_config_id = resolve_config_id(connected_config.get("memory_config_id"), db)
-                    
+
                     if not user_config_id:
                         failed_users.append({"end_user_id": str(end_user.id), "error": "无法获取配置"})
                         continue
@@ -2048,13 +2048,13 @@ def run_forgetting_cycle_task(self, config_id: Optional[uuid.UUID] = None) -> Di
                     report = await forget_service.trigger_forgetting_cycle(
                         db=db, end_user_id=str(end_user.id), config_id=user_config_id
                     )
-                    
+
                     total_merged += report.get('merged_count', 0)
                     total_failed += report.get('failed_count', 0)
                     processed_users += 1
-                    
+
                     logger.info(f"用户 {end_user.id}: 融合 {report.get('merged_count', 0)} 对节点")
-                    
+
                 except Exception as e:
                     logger.error(f"处理用户 {end_user.id} 失败: {e}", exc_info=True)
                     failed_users.append({"end_user_id": str(end_user.id), "error": str(e)})
@@ -2801,18 +2801,18 @@ def run_incremental_clustering(
         包含任务执行结果的字典
     """
     start_time = time.time()
-    
+
     async def _run() -> Dict[str, Any]:
         from app.core.logging_config import get_logger
         from app.repositories.neo4j.neo4j_connector import Neo4jConnector
         from app.core.memory.storage_services.clustering_engine.label_propagation import LabelPropagationEngine
-        
+
         logger = get_logger(__name__)
         logger.info(
             f"[IncrementalClustering] 开始增量聚类任务 - end_user_id={end_user_id}, "
             f"实体数={len(new_entity_ids)}, llm_model_id={llm_model_id}"
         )
-        
+
         connector = Neo4jConnector()
         try:
             engine = LabelPropagationEngine(
@@ -2820,12 +2820,12 @@ def run_incremental_clustering(
                 llm_model_id=llm_model_id,
                 embedding_model_id=embedding_model_id,
             )
-            
+
             # 执行增量聚类
             await engine.run(end_user_id=end_user_id, new_entity_ids=new_entity_ids)
-            
+
             logger.info(f"[IncrementalClustering] 增量聚类完成 - end_user_id={end_user_id}")
-            
+
             return {
                 "status": "SUCCESS",
                 "end_user_id": end_user_id,
@@ -2836,18 +2836,18 @@ def run_incremental_clustering(
             raise
         finally:
             await connector.close()
-    
+
     try:
         loop = set_asyncio_event_loop()
         result = loop.run_until_complete(_run())
         result["elapsed_time"] = time.time() - start_time
         result["task_id"] = self.request.id
-        
+
         logger.info(
             f"[IncrementalClustering] 任务完成 - task_id={self.request.id}, "
             f"elapsed_time={result['elapsed_time']:.2f}s"
         )
-        
+
         return result
     except Exception as e:
         elapsed_time = time.time() - start_time

api/docker-compose.yml

@@ -63,6 +63,23 @@ services:
     networks:
       - celery
 
+  celery-task-scheduler:
+    image: redbear-mem-open:latest
+    container_name: celery-task-scheduler
+    env_file:
+      - .env
+    volumes:
+      - /etc/localtime:/etc/localtime:ro
+    command: python app/celery_task_scheduler.py
+    restart: unless-stopped
+    healthcheck:
+      test:  CMD curl -f 127.0.0.1:8001 || exit 1
+      interval: 30s
+      timeout: 5s
+      retries: 3
+    networks:
+      - celery
+
   # Celery Beat - scheduler
   beat:
     image: redbear-mem-open:latest