Merge branch 'refs/heads/develop' into feature/agent-tool_xjn

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/app_log_controller.py

@@ -9,7 +9,7 @@ from app.core.logging_config import get_business_logger
 from app.core.response_utils import success
 from app.db import get_db
 from app.dependencies import get_current_user, cur_workspace_access_guard
-from app.schemas.app_log_schema import AppLogConversation, AppLogConversationDetail
+from app.schemas.app_log_schema import AppLogConversation, AppLogConversationDetail, AppLogMessage
 from app.schemas.response_schema import PageData, PageMeta
 from app.services.app_service import AppService
 from app.services.app_log_service import AppLogService
@@ -78,17 +78,32 @@ def get_app_log_detail(
 
     # 验证应用访问权限
     app_service = AppService(db)
-    app_service.get_app(app_id, workspace_id)
+    app = app_service.get_app(app_id, workspace_id)
 
     # 使用 Service 层查询
     log_service = AppLogService(db)
-    conversation, node_executions_map = log_service.get_conversation_detail(
+    conversation, messages, node_executions_map = log_service.get_conversation_detail(
         app_id=app_id,
         conversation_id=conversation_id,
-        workspace_id=workspace_id
+        workspace_id=workspace_id,
+        app_type=app.type
     )
 
-    detail = AppLogConversationDetail.model_validate(conversation)
-    detail.node_executions_map = node_executions_map
+    # 构建基础会话信息（不经过 ORM relationship）
+    base = AppLogConversation.model_validate(conversation)
+
+    # 单独处理 messages，避免触发 SQLAlchemy relationship 校验
+    if messages and isinstance(messages[0], AppLogMessage):
+        # 工作流：已经是 AppLogMessage 实例
+        msg_list = messages
+    else:
+        # Agent：ORM Message 对象逐个转换
+        msg_list = [AppLogMessage.model_validate(m) for m in messages]
+
+    detail = AppLogConversationDetail(
+        **base.model_dump(),
+        messages=msg_list,
+        node_executions_map=node_executions_map,
+    )
 
     return success(data=detail)

api/app/controllers/memory_explicit_controller.py

@@ -4,7 +4,9 @@
 处理显性记忆相关的API接口，包括情景记忆和语义记忆的查询。
 """
 
-from fastapi import APIRouter, Depends
+from typing import Optional
+
+from fastapi import APIRouter, Depends, Query
 
 from app.core.logging_config import get_api_logger
 from app.core.response_utils import success, fail
@@ -69,6 +71,140 @@ async def get_explicit_memory_overview_api(
         return fail(BizCode.INTERNAL_ERROR, "显性记忆总览查询失败", str(e))
 
 
+@router.get("/episodics", response_model=ApiResponse)
+async def get_episodic_memory_list_api(
+    end_user_id: str = Query(..., description="end user ID"),
+    page: int = Query(1, gt=0, description="page number, starting from 1"),
+    pagesize: int = Query(10, gt=0, le=100, description="number of items per page, max 100"),
+    start_date: Optional[int] = Query(None, description="start timestamp (ms)"),
+    end_date: Optional[int] = Query(None, description="end timestamp (ms)"),
+    episodic_type: str = Query("all", description="episodic type ：all/conversation/project_work/learning/decision/important_event"),
+    current_user: User = Depends(get_current_user),
+) -> dict:
+    """
+    获取情景记忆分页列表
+
+    返回指定用户的情景记忆列表，支持分页、时间范围筛选和情景类型筛选。
+
+    Args:
+        end_user_id: 终端用户ID（必填）
+        page: 页码（从1开始，默认1）
+        pagesize: 每页数量（默认10，最大100）
+        start_date: 开始时间戳（可选，毫秒），自动扩展到当天 00:00:00
+        end_date: 结束时间戳（可选，毫秒），自动扩展到当天 23:59:59
+        episodic_type: 情景类型筛选（可选，默认all）
+        current_user: 当前用户
+
+    Returns:
+        ApiResponse: 包含情景记忆分页列表
+
+    Examples:
+        - 基础分页查询：GET /episodics?end_user_id=xxx&page=1&pagesize=5
+          返回第1页，每页5条数据
+        - 按时间范围筛选：GET /episodics?end_user_id=xxx&page=1&pagesize=5&start_date=1738684800000&end_date=1738771199000
+          返回指定时间范围内的数据
+        - 按情景类型筛选：GET /episodics?end_user_id=xxx&page=1&pagesize=5&episodic_type=important_event
+          返回类型为"重要事件"的数据
+
+    Notes:
+        - start_date 和 end_date 必须同时提供或同时不提供
+        - start_date 不能大于 end_date
+        - episodic_type 可选值：all, conversation, project_work, learning, decision, important_event
+        - total 为该用户情景记忆总数（不受筛选条件影响）
+        - page.total 为筛选后的总条数
+    """
+    workspace_id = current_user.current_workspace_id
+
+    # 检查用户是否已选择工作空间
+    if workspace_id is None:
+        api_logger.warning(f"用户 {current_user.username} 尝试查询情景记忆列表但未选择工作空间")
+        return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
+
+    api_logger.info(
+        f"情景记忆分页查询: end_user_id={end_user_id}, "
+        f"start_date={start_date}, end_date={end_date}, episodic_type={episodic_type}, "
+        f"page={page}, pagesize={pagesize}, username={current_user.username}"
+    )
+
+    # 1. 参数校验
+    if page < 1 or pagesize < 1:
+        api_logger.warning(f"分页参数错误: page={page}, pagesize={pagesize}")
+        return fail(BizCode.INVALID_PARAMETER, "分页参数必须大于0")
+
+    valid_episodic_types = ["all", "conversation", "project_work", "learning", "decision", "important_event"]
+    if episodic_type not in valid_episodic_types:
+        api_logger.warning(f"无效的情景类型参数: {episodic_type}")
+        return fail(BizCode.INVALID_PARAMETER, f"无效的情景类型参数，可选值：{', '.join(valid_episodic_types)}")
+
+    # 时间戳参数校验
+    if (start_date is not None and end_date is None) or (end_date is not None and start_date is None):
+        return fail(BizCode.INVALID_PARAMETER, "start_date和end_date必须同时提供")
+
+    if start_date is not None and end_date is not None and start_date > end_date:
+        return fail(BizCode.INVALID_PARAMETER, "start_date不能大于end_date")
+
+    # 2. 执行查询
+    try:
+        result = await memory_explicit_service.get_episodic_memory_list(
+            end_user_id=end_user_id,
+            page=page,
+            pagesize=pagesize,
+            start_date=start_date,
+            end_date=end_date,
+            episodic_type=episodic_type,
+        )
+        api_logger.info(
+            f"情景记忆分页查询成功: end_user_id={end_user_id}, "
+            f"total={result['total']}, 返回={len(result['items'])}条"
+        )
+    except Exception as e:
+        api_logger.error(f"情景记忆分页查询失败: end_user_id={end_user_id}, error={str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "情景记忆分页查询失败", str(e))
+
+    # 3. 返回结构化响应
+    return success(data=result, msg="查询成功")
+
+@router.get("/semantics", response_model=ApiResponse)
+async def get_semantic_memory_list_api(
+    end_user_id: str = Query(..., description="终端用户ID"),
+    current_user: User = Depends(get_current_user),
+) -> dict:
+    """
+    获取语义记忆列表
+
+    返回指定用户的全量语义记忆列表。
+
+    Args:
+        end_user_id: 终端用户ID（必填）
+        current_user: 当前用户
+
+    Returns:
+        ApiResponse: 包含语义记忆全量列表
+    """
+    workspace_id = current_user.current_workspace_id
+
+    if workspace_id is None:
+        api_logger.warning(f"用户 {current_user.username} 尝试查询语义记忆列表但未选择工作空间")
+        return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
+
+    api_logger.info(
+        f"语义记忆列表查询: end_user_id={end_user_id}, username={current_user.username}"
+    )
+
+    try:
+        result = await memory_explicit_service.get_semantic_memory_list(
+            end_user_id=end_user_id
+        )
+        api_logger.info(
+            f"语义记忆列表查询成功: end_user_id={end_user_id}, total={len(result)}"
+        )
+    except Exception as e:
+        api_logger.error(f"语义记忆列表查询失败: end_user_id={end_user_id}, error={str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "语义记忆列表查询失败", str(e))
+
+    return success(data=result, msg="查询成功")
+
+
 @router.post("/details", response_model=ApiResponse)
 async def get_explicit_memory_details_api(
     request: ExplicitMemoryDetailsRequest,

api/app/controllers/service/__init__.py

@@ -14,6 +14,7 @@ from . import (
     rag_api_document_controller,
     rag_api_file_controller,
     rag_api_knowledge_controller,
+    user_memory_api_controller,
 )
 
 # 创建 V1 API 路由器
@@ -28,5 +29,6 @@ service_router.include_router(rag_api_chunk_controller.router)
 service_router.include_router(memory_api_controller.router)
 service_router.include_router(end_user_api_controller.router)
 service_router.include_router(memory_config_api_controller.router)
+service_router.include_router(user_memory_api_controller.router)
 
 __all__ = ["service_router"]

api/app/controllers/service/memory_api_controller.py

@@ -3,6 +3,7 @@
 from fastapi import APIRouter, Body, Depends, Query, Request
 from sqlalchemy.orm import Session
 
+from app.celery_task_scheduler import scheduler
 from app.core.api_key_auth import require_api_key
 from app.core.logging_config import get_business_logger
 from app.core.quota_stub import check_end_user_quota
@@ -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/controllers/service/user_memory_api_controller.py

@@ -0,0 +1,230 @@
+"""User Memory 服务接口 — 基于 API Key 认证
+
+包装 user_memory_controllers.py 和 memory_agent_controller.py 中的内部接口，
+提供基于 API Key 认证的对外服务:
+1./analytics/graph_data - 知识图谱数据接口
+2./analytics/community_graph - 社区图谱接口
+3./analytics/node_statistics - 记忆节点统计接口
+4./analytics/user_summary - 用户摘要接口
+5./analytics/memory_insight - 记忆洞察接口
+6./analytics/interest_distribution - 兴趣分布接口
+7./analytics/end_user_info - 终端用户信息接口
+8./analytics/generate_cache - 缓存生成接口
+
+
+路由前缀: /memory
+子路径: /analytics/...
+最终路径: /v1/memory/analytics/...
+认证方式: API Key (@require_api_key)
+"""
+
+from typing import Optional
+
+from fastapi import APIRouter, Depends, Header, Query, Request, Body
+from sqlalchemy.orm import Session
+
+from app.core.api_key_auth import require_api_key
+from app.core.api_key_utils import get_current_user_from_api_key, validate_end_user_in_workspace
+from app.core.logging_config import get_business_logger
+from app.db import get_db
+from app.schemas.api_key_schema import ApiKeyAuth
+from app.schemas.memory_storage_schema import GenerateCacheRequest
+
+# 包装内部服务 controller
+from app.controllers import user_memory_controllers, memory_agent_controller
+
+router = APIRouter(prefix="/memory", tags=["V1 - User Memory API"])
+logger = get_business_logger()
+
+
+# ==================== 知识图谱 ====================
+
+
+@router.get("/analytics/graph_data")
+@require_api_key(scopes=["memory"])
+async def get_graph_data(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    node_types: Optional[str] = Query(None, description="Comma-separated node types filter"),
+    limit: int = Query(100, description="Max nodes to return (auto-capped at 1000 in service layer)"),
+    depth: int = Query(1, description="Graph traversal depth (auto-capped at 3 in service layer)"),
+    center_node_id: Optional[str] = Query(None, description="Center node for subgraph"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get knowledge graph data (nodes + edges) for an end user."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.get_graph_data_api(
+        end_user_id=end_user_id,
+        node_types=node_types,
+        limit=limit,
+        depth=depth,
+        center_node_id=center_node_id,
+        current_user=current_user,
+        db=db,
+    )
+
+
+@router.get("/analytics/community_graph")
+@require_api_key(scopes=["memory"])
+async def get_community_graph(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get community clustering graph for an end user."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.get_community_graph_data_api(
+        end_user_id=end_user_id,
+        current_user=current_user,
+        db=db,
+    )
+
+
+# ==================== 节点统计 ====================
+
+
+@router.get("/analytics/node_statistics")
+@require_api_key(scopes=["memory"])
+async def get_node_statistics(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get memory node type statistics for an end user."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.get_node_statistics_api(
+        end_user_id=end_user_id,
+        current_user=current_user,
+        db=db,
+    )
+
+
+# ==================== 用户摘要 & 洞察 ====================
+
+
+@router.get("/analytics/user_summary")
+@require_api_key(scopes=["memory"])
+async def get_user_summary(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    language_type: str = Header(default=None, alias="X-Language-Type"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get cached user summary for an end user."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.get_user_summary_api(
+        end_user_id=end_user_id,
+        language_type=language_type,
+        current_user=current_user,
+        db=db,
+    )
+
+
+@router.get("/analytics/memory_insight")
+@require_api_key(scopes=["memory"])
+async def get_memory_insight(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get cached memory insight report for an end user."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.get_memory_insight_report_api(
+        end_user_id=end_user_id,
+        current_user=current_user,
+        db=db,
+    )
+
+
+# ==================== 兴趣分布 ====================
+
+
+@router.get("/analytics/interest_distribution")
+@require_api_key(scopes=["memory"])
+async def get_interest_distribution(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    limit: int = Query(5, le=5, description="Max interest tags to return"),
+    language_type: str = Header(default=None, alias="X-Language-Type"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get interest distribution tags for an end user."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await memory_agent_controller.get_interest_distribution_by_user_api(
+        end_user_id=end_user_id,
+        limit=limit,
+        language_type=language_type,
+        current_user=current_user,
+        db=db,
+    )
+
+
+# ==================== 终端用户信息 ====================
+
+
+@router.get("/analytics/end_user_info")
+@require_api_key(scopes=["memory"])
+async def get_end_user_info(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get end user basic information (name, aliases, metadata)."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.get_end_user_info(
+        end_user_id=end_user_id,
+        current_user=current_user,
+        db=db,
+    )
+
+
+# ==================== 缓存生成 ====================
+
+
+@router.post("/analytics/generate_cache")
+@require_api_key(scopes=["memory"])
+async def generate_cache(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(None, description="Request body"),
+    language_type: str = Header(default=None, alias="X-Language-Type"),
+):
+    """Trigger cache generation (user summary + memory insight) for an end user or all workspace users."""
+    body = await request.json()
+    cache_request = GenerateCacheRequest(**body)
+
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+
+    if cache_request.end_user_id:
+        validate_end_user_in_workspace(db, cache_request.end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.generate_cache_api(
+        request=cache_request,
+        language_type=language_type,
+        current_user=current_user,
+        db=db,
+    )
+
+

api/app/core/api_key_utils.py

@@ -1,8 +1,15 @@
 """API Key 工具函数"""
 import secrets
+import uuid as _uuid
 from typing import Optional, Union
 from datetime import datetime
 
+from sqlalchemy.orm import Session as _Session
+from app.core.error_codes import BizCode as _BizCode
+from app.core.exceptions import BusinessException as _BusinessException
+from app.models.end_user_model import EndUser as _EndUser
+from app.repositories.end_user_repository import EndUserRepository as _EndUserRepository
+
 from app.models.api_key_model import ApiKeyType
 from fastapi import Response
 from fastapi.responses import JSONResponse
@@ -65,3 +72,72 @@ def datetime_to_timestamp(dt: Optional[datetime]) -> Optional[int]:
         return None
 
     return int(dt.timestamp() * 1000)
+
+
+def get_current_user_from_api_key(db: _Session, api_key_auth):
+    """通过 API Key 构造 current_user 对象。
+
+    从 API Key 反查创建者（管理员用户），并设置其 workspace 上下文。
+    与内部接口的 Depends(get_current_user) (JWT) 等价。
+
+    Args:
+        db: 数据库会话
+        api_key_auth: API Key 认证信息（ApiKeyAuth）
+
+    Returns:
+        User ORM 对象，已设置 current_workspace_id
+    """
+    from app.services import api_key_service
+
+    api_key = api_key_service.ApiKeyService.get_api_key(
+        db, api_key_auth.api_key_id, api_key_auth.workspace_id
+    )
+    current_user = api_key.creator
+    current_user.current_workspace_id = api_key_auth.workspace_id
+    return current_user
+
+
+def validate_end_user_in_workspace(
+    db: _Session,
+    end_user_id: str,
+    workspace_id,
+) -> _EndUser:
+    """校验 end_user 是否存在且属于指定 workspace。
+
+    Args:
+        db: 数据库会话
+        end_user_id: 终端用户 ID
+        workspace_id: 工作空间 ID（UUID 或字符串均可）
+
+    Returns:
+        EndUser ORM 对象（校验通过时）
+
+    Raises:
+        BusinessException(INVALID_PARAMETER): end_user_id 格式无效
+        BusinessException(USER_NOT_FOUND): end_user 不存在
+        BusinessException(PERMISSION_DENIED): end_user 不属于该 workspace
+    """
+    try:
+        _uuid.UUID(end_user_id)
+    except (ValueError, AttributeError):
+        raise _BusinessException(
+            f"Invalid end_user_id format: {end_user_id}",
+            _BizCode.INVALID_PARAMETER,
+        )
+
+    end_user_repo = _EndUserRepository(db)
+    end_user = end_user_repo.get_end_user_by_id(end_user_id)
+
+    if end_user is None:
+        raise _BusinessException(
+            "End user not found",
+            _BizCode.USER_NOT_FOUND,
+        )
+
+    if str(end_user.workspace_id) != str(workspace_id):
+        raise _BusinessException(
+            "End user does not belong to this workspace",
+            _BizCode.PERMISSION_DENIED,
+        )
+
+    return end_user

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

@@ -1,6 +1,7 @@
 import json
 import os
 
+from app.celery_task_scheduler import scheduler
 from app.core.logging_config import get_agent_logger
 from app.core.memory.agent.langgraph_graph.tools.write_tool import format_parsing, messages_parse
 from app.core.memory.agent.models.write_aggregate_model import WriteAggregateModel
@@ -12,8 +13,6 @@ 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
 
 logger = get_agent_logger(__name__)
@@ -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 app.core.memory.read_services.search_engine.content_search import Neo4jSearchService, RAGSearchService
+from app.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 app.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/tools/custom/base.py

@@ -73,6 +73,7 @@ class CustomTool(BaseTool):
         # 添加通用参数（基于第一个操作的参数）
         if self._parsed_operations:
             first_operation = next(iter(self._parsed_operations.values()))
+            # path/query 参数
             for param_name, param_info in first_operation.get("parameters", {}).items():
                 params.append(ToolParameter(
                     name=param_name,
@@ -85,6 +86,23 @@ class CustomTool(BaseTool):
                     maximum=param_info.get("maximum"),
                     pattern=param_info.get("pattern")
                 ))
+            # requestBody 参数 — 将 body 字段平铺为独立参数暴露给模型
+            request_body = first_operation.get("request_body")
+            if request_body:
+                body_schema = request_body.get("properties", {})
+                required_fields = request_body.get("required", [])
+                for prop_name, prop_schema in body_schema.items():
+                    params.append(ToolParameter(
+                        name=prop_name,
+                        type=self._convert_openapi_type(prop_schema.get("type", "string")),
+                        description=prop_schema.get("description", ""),
+                        required=prop_name in required_fields,
+                        default=prop_schema.get("default"),
+                        enum=prop_schema.get("enum"),
+                        minimum=prop_schema.get("minimum"),
+                        maximum=prop_schema.get("maximum"),
+                        pattern=prop_schema.get("pattern")
+                    ))
         
         return params
     

api/app/core/workflow/nodes/cycle_graph/iteration.py

@@ -180,6 +180,9 @@ class IterationRuntime:
                                 "cycle_id": self.node_id,
                                 "cycle_idx": idx,
                                 "node_id": node_name,
+                                "node_type": node_type,
+                                "node_name": node_cfg.get("data", {}).get("label") if node_cfg else node_name,
+                                "status": result.get("node_outputs", {}).get(node_name, {}).get("status", "completed"),
                                 "input": result.get("node_outputs", {}).get(node_name, {}).get("input")
                                 if not cycle_variable else cycle_variable,
                                 "output": result.get("node_outputs", {}).get(node_name, {}).get("output")

api/app/core/workflow/nodes/cycle_graph/loop.py

@@ -210,6 +210,9 @@ class LoopRuntime:
                                 "cycle_id": self.node_id,
                                 "cycle_idx": idx,
                                 "node_id": node_name,
+                                "node_type": node_type,
+                                "node_name": node_name,
+                                "status": result.get("node_outputs", {}).get(node_name, {}).get("status", "completed"),
                                 "input": result.get("node_outputs", {}).get(node_name, {}).get("input")
                                 if not cycle_variable else cycle_variable,
                                 "output": result.get("node_outputs", {}).get(node_name, {}).get("output")

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

@@ -1,6 +1,7 @@
 import re
 from typing import Any
 
+from app.celery_task_scheduler import scheduler
 from app.core.memory.enums import SearchStrategy
 from app.core.memory.memory_service import MemoryService
 from app.core.workflow.engine.state_manager import WorkflowState
@@ -11,7 +12,6 @@ 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
 
 
 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/app_log_schema.py

@@ -14,6 +14,7 @@ class AppLogMessage(BaseModel):
     conversation_id: uuid.UUID
     role: str = Field(description="角色: user / assistant / system")
     content: str
+    status: Optional[str] = Field(default=None, description="执行状态（工作流专用）: completed / failed")
     meta_data: Optional[Dict[str, Any]] = None
     created_at: datetime.datetime
 
@@ -58,6 +59,7 @@ class AppLogNodeExecution(BaseModel):
     input: Optional[Any] = None
     process: Optional[Any] = None
     output: Optional[Any] = None
+    cycle_items: Optional[List[Any]] = None
     elapsed_time: Optional[float] = None
     token_usage: Optional[Dict[str, Any]] = None
 

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/api_key_service.py

@@ -65,6 +65,11 @@ class ApiKeyService:
                         BizCode.BAD_REQUEST
                     )
 
+            if data.resource_id:
+                app = db.get(App, data.resource_id)
+                if not app or not app.current_release_id:
+                    raise BusinessException("该应用未发布", BizCode.APP_NOT_PUBLISHED)
+
             # 生成 API Key
             api_key = generate_api_key(data.type)
 

api/app/services/app_log_service.py

@@ -1,16 +1,17 @@
 """应用日志服务层"""
 import uuid
+import datetime as dt
 from typing import Optional, Tuple
-from datetime import datetime
 
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
 from app.core.logging_config import get_business_logger
+from app.models.app_model import AppType
 from app.models.conversation_model import Conversation, Message
 from app.models.workflow_model import WorkflowExecution
 from app.repositories.conversation_repository import ConversationRepository, MessageRepository
-from app.schemas.app_log_schema import AppLogNodeExecution
+from app.schemas.app_log_schema import AppLogMessage, AppLogNodeExecution
 
 logger = get_business_logger()
 
@@ -83,51 +84,40 @@ class AppLogService:
         self,
         app_id: uuid.UUID,
         conversation_id: uuid.UUID,
-        workspace_id: uuid.UUID
-    ) -> Tuple[Conversation, dict[str, list[AppLogNodeExecution]]]:
+        workspace_id: uuid.UUID,
+        app_type: str = AppType.AGENT
+    ) -> Tuple[Conversation, list, dict[str, list[AppLogNodeExecution]]]:
         """
-        查询会话详情（包含消息和工作流节点执行记录）
-
-        Args:
-            app_id: 应用 ID
-            conversation_id: 会话 ID
-            workspace_id: 工作空间 ID
+        查询会话详情
 
         Returns:
-            Tuple[Conversation, dict[str, list[AppLogNodeExecution]]]:
-                (包含消息的会话对象, 按消息ID分组的节点执行记录)
-
-        Raises:
-            ResourceNotFoundException: 当会话不存在时
+            Tuple[Conversation, list[AppLogMessage|Message], dict[str, list[AppLogNodeExecution]]]
         """
         logger.info(
             "查询应用日志会话详情",
             extra={
                 "app_id": str(app_id),
                 "conversation_id": str(conversation_id),
-                "workspace_id": str(workspace_id)
+                "workspace_id": str(workspace_id),
+                "app_type": app_type
             }
         )
 
-        # 查询会话
         conversation = self.conversation_repository.get_conversation_for_app_log(
             conversation_id=conversation_id,
             app_id=app_id,
             workspace_id=workspace_id
         )
 
-        # 查询消息（按时间正序）
-        messages = self.message_repository.get_messages_by_conversation(
-            conversation_id=conversation_id
-        )
-
-        # 将消息附加到会话对象
-        conversation.messages = messages
-
-        # 查询工作流节点执行记录（按消息分组）
-        _, node_executions_map = self._get_workflow_node_executions_with_map(
-            conversation_id, messages
-        )
+        if app_type == AppType.WORKFLOW:
+            messages, node_executions_map = self._get_workflow_messages_and_nodes(conversation_id)
+        else:
+            messages = self.message_repository.get_messages_by_conversation(
+                conversation_id=conversation_id
+            )
+            node_executions_map = self._get_workflow_node_executions_with_map(
+                conversation_id, messages
+            )
 
         logger.info(
             "查询应用日志会话详情成功",
@@ -139,13 +129,129 @@ class AppLogService:
             }
         )
 
-        return conversation, node_executions_map
+        return conversation, messages, node_executions_map
+
+    def _get_workflow_messages_and_nodes(
+        self,
+        conversation_id: uuid.UUID,
+    ) -> Tuple[list[AppLogMessage], dict[str, list[AppLogNodeExecution]]]:
+        """
+        工作流应用专用：从 workflow_executions 构建 messages 和节点日志。
+
+        每条 WorkflowExecution 对应一轮对话：
+          - user message：来自 execution.input_data（content 取 message 字段，files 放 meta_data）
+          - assistant message：来自 execution.output_data（失败时内容为错误信息）
+        开场白的 suggested_questions 合并到第一条 assistant message 的 meta_data 里。
+
+        Returns:
+            (messages 列表, node_executions_map)
+        """
+        stmt = (
+            select(WorkflowExecution)
+            .where(
+                WorkflowExecution.conversation_id == conversation_id,
+                WorkflowExecution.status.in_(["completed", "failed"])
+            )
+            .order_by(WorkflowExecution.started_at.asc())
+        )
+        executions = list(self.db.scalars(stmt).all())
+
+        # 查开场白：Message 表里 meta_data 含 suggested_questions 的第一条 assistant 消息
+        opening_stmt = (
+            select(Message)
+            .where(
+                Message.conversation_id == conversation_id,
+                Message.role == "assistant",
+            )
+            .order_by(Message.created_at.asc())
+            .limit(10)
+        )
+        early_messages = list(self.db.scalars(opening_stmt).all())
+        suggested_questions: list = []
+        for m in early_messages:
+            if isinstance(m.meta_data, dict) and "suggested_questions" in m.meta_data:
+                suggested_questions = m.meta_data.get("suggested_questions") or []
+                break
+
+        messages: list[AppLogMessage] = []
+        node_executions_map: dict[str, list[AppLogNodeExecution]] = {}
+
+        # 如果有开场白，作为第一条 assistant 消息插入
+        if suggested_questions or early_messages:
+            opening_msg = next(
+                (m for m in early_messages
+                 if isinstance(m.meta_data, dict) and "suggested_questions" in m.meta_data),
+                None
+            )
+            if opening_msg:
+                messages.append(AppLogMessage(
+                    id=opening_msg.id,
+                    conversation_id=conversation_id,
+                    role="assistant",
+                    content=opening_msg.content,
+                    status=None,
+                    meta_data={"suggested_questions": suggested_questions},
+                    created_at=opening_msg.created_at,
+                ))
+
+        for execution in executions:
+            started_at = execution.started_at or dt.datetime.now()
+            completed_at = execution.completed_at or started_at
+
+            # assistant message 的 id，同时作为 node_executions_map 的 key
+            assistant_msg_id = uuid.uuid5(execution.id, "assistant")
+
+            # --- user message（输入）---
+            input_data = execution.input_data or {}
+            input_content = input_data.get("message") or _extract_text(input_data)
+
+            # 跳过没有用户输入的 execution（如开场白触发的记录）
+            if not input_content or not input_content.strip():
+                continue
+
+            files = input_data.get("files") or []
+            user_msg = AppLogMessage(
+                id=uuid.uuid5(execution.id, "user"),
+                conversation_id=conversation_id,
+                role="user",
+                content=input_content,
+                meta_data={"files": files} if files else None,
+                created_at=started_at,
+            )
+            messages.append(user_msg)
+
+            # --- assistant message（输出）---
+            if execution.status == "completed":
+                output_content = _extract_text(execution.output_data)
+                meta = {"usage": execution.token_usage or {}, "elapsed_time": execution.elapsed_time}
+            else:
+                output_content = _extract_text(execution.output_data) or ""
+                meta = {"error": execution.error_message, "error_node_id": execution.error_node_id}
+
+            assistant_msg = AppLogMessage(
+                id=assistant_msg_id,
+                conversation_id=conversation_id,
+                role="assistant",
+                content=output_content,
+                status=execution.status,
+                meta_data=meta,
+                created_at=completed_at,
+            )
+            messages.append(assistant_msg)
+
+            # --- 节点执行记录，从 workflow_executions.output_data["node_outputs"] 读取 ---
+            execution_nodes = _build_nodes_from_output_data(execution.output_data)
+
+            if execution_nodes:
+                node_executions_map[str(assistant_msg_id)] = execution_nodes
+
+        return messages, node_executions_map
 
     def _get_workflow_node_executions_with_map(
         self,
         conversation_id: uuid.UUID,
         messages: list[Message]
-    ) -> Tuple[list[AppLogNodeExecution], dict[str, list[AppLogNodeExecution]]]:
+    ) -> dict[str, list[AppLogNodeExecution]]:
         """
         从 workflow_executions 表中提取节点执行记录，并按 assistant message 分组
 
@@ -157,13 +263,12 @@ class AppLogService:
             Tuple[list[AppLogNodeExecution], dict[str, list[AppLogNodeExecution]]]:
                 (所有节点执行记录列表, 按 message_id 分组的节点执行记录字典)
         """
-        node_executions = []
         node_executions_map: dict[str, list[AppLogNodeExecution]] = {}
 
         # 查询该会话关联的所有工作流执行记录（按时间正序）
         stmt = select(WorkflowExecution).where(
             WorkflowExecution.conversation_id == conversation_id,
-            WorkflowExecution.status == "completed"
+            WorkflowExecution.status.in_(["completed", "failed"])
         ).order_by(WorkflowExecution.started_at.asc())
 
         executions = self.db.scalars(stmt).all()
@@ -188,10 +293,18 @@ class AppLogService:
         used_message_ids: set[str] = set()
 
         for execution in executions:
-            if not execution.output_data:
+            # 构建节点执行记录列表，从 workflow_executions.output_data["node_outputs"] 读取
+            execution_nodes = _build_nodes_from_output_data(execution.output_data)
+
+            if not execution_nodes:
                 continue
 
-            # 找到该 execution 对应的 assistant message
+            # 失败的执行没有 assistant message，直接用 execution id 作为 key
+            if execution.status == "failed":
+                node_executions_map[f"execution_{str(execution.id)}"] = execution_nodes
+                continue
+
+            # completed：通过时序匹配关联到对应的 assistant message
             # 逻辑：找 execution.started_at 之后最近的、未使用的 assistant message
             best_msg = None
             best_dt = None
@@ -200,9 +313,9 @@ class AppLogService:
                 if msg_id_str in used_message_ids:
                     continue
                 if msg.created_at and msg.created_at >= execution.started_at:
-                    dt = (msg.created_at - execution.started_at).total_seconds()
-                    if best_dt is None or dt < best_dt:
-                        best_dt = dt
+                    delta = (msg.created_at - execution.started_at).total_seconds()
+                    if best_dt is None or delta < best_dt:
+                        best_dt = delta
                         best_msg = msg
 
             if not best_msg:
@@ -210,31 +323,76 @@ class AppLogService:
 
             msg_id_str = str(best_msg.id)
             used_message_ids.add(msg_id_str)
+            node_executions_map[msg_id_str] = execution_nodes
 
-            # 提取节点输出
-            output_data = execution.output_data
-            if isinstance(output_data, dict):
-                node_outputs = output_data.get("node_outputs", {})
-                execution_nodes = []
-                for node_id, node_data in node_outputs.items():
-                    if not isinstance(node_data, dict):
-                        continue
-                    node_execution = AppLogNodeExecution(
-                        node_id=node_data.get("node_id", node_id),
-                        node_type=node_data.get("node_type", "unknown"),
-                        node_name=node_data.get("node_name"),
-                        status=node_data.get("status", "unknown"),
-                        error=node_data.get("error"),
-                        input=node_data.get("input"),
-                        process=node_data.get("process"),
-                        output=node_data.get("output"),
-                        elapsed_time=node_data.get("elapsed_time"),
-                        token_usage=node_data.get("token_usage"),
-                    )
-                    node_executions.append(node_execution)
-                    execution_nodes.append(node_execution)
+        return node_executions_map
 
-                # 将节点记录关联到 message_id
-                node_executions_map[msg_id_str] = execution_nodes
 
-        return node_executions, node_executions_map
+def _extract_text(data: Optional[dict]) -> str:
+    """从 workflow execution 的 input_data / output_data 中提取可读文本。
+
+    优先取 'text'、'content'、'output' 字段；若都没有则 JSON 序列化整个 dict。
+    """
+    if not data:
+        return ""
+    for key in ("message", "text", "content", "output", "result", "answer"):
+        if key in data and isinstance(data[key], str):
+            return data[key]
+    import json
+    return json.dumps(data, ensure_ascii=False)
+
+
+def _build_nodes_from_output_data(output_data: Optional[dict]) -> list[AppLogNodeExecution]:
+    """从 workflow_executions.output_data["node_outputs"] 构建节点执行记录列表。
+
+    output_data 结构：
+    {
+        "node_outputs": {
+            "<node_id>": {
+                "node_type": ...,
+                "node_name": ...,
+                "status": ...,
+                "input": ...,
+                "output": ...,
+                "elapsed_time": ...,
+                "token_usage": ...,
+                "error": ...,
+                "cycle_items": [...],
+                ...
+            }
+        },
+        "error": ...,
+        ...
+    }
+    """
+    if not output_data:
+        return []
+    node_outputs: dict = output_data.get("node_outputs") or {}
+    result = []
+    for node_id, node_data in node_outputs.items():
+        if not isinstance(node_data, dict):
+            continue
+        output = dict(node_data)
+        cycle_items = output.pop("cycle_items", None)
+        # 把已知的顶层字段剥离，剩余的作为 output
+        node_type = output.pop("node_type", "unknown")
+        node_name = output.pop("node_name", None)
+        status = output.pop("status", "completed")
+        error = output.pop("error", None)
+        inp = output.pop("input", None)
+        elapsed_time = output.pop("elapsed_time", None)
+        token_usage = output.pop("token_usage", None)
+        result.append(AppLogNodeExecution(
+            node_id=node_id,
+            node_type=node_type,
+            node_name=node_name,
+            status=status,
+            error=error,
+            input=inp,
+            process=None,
+            output=output if output else None,
+            cycle_items=cycle_items,
+            elapsed_time=elapsed_time,
+            token_usage=token_usage,
+        ))
+    return result

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/services/memory_explicit_service.py

@@ -4,7 +4,7 @@
 处理显性记忆相关的业务逻辑，包括情景记忆和语义记忆的查询。
 """
 
-from typing import Any, Dict
+from typing import Any, Dict, Optional
 
 from app.core.logging_config import get_logger
 from app.services.memory_base_service import MemoryBaseService
@@ -104,7 +104,7 @@ class MemoryExplicitService(MemoryBaseService):
                    e.description AS core_definition
             ORDER BY e.name ASC
             """
-            
+
             semantic_result = await self.neo4j_connector.execute_query(
                 semantic_query, 
                 end_user_id=end_user_id
@@ -146,6 +146,209 @@ class MemoryExplicitService(MemoryBaseService):
             logger.error(f"获取显性记忆总览时出错: {str(e)}", exc_info=True)
             raise
 
+
+    async def get_episodic_memory_list(
+        self,
+        end_user_id: str,
+        page: int,
+        pagesize: int,
+        start_date: Optional[int] = None,
+        end_date: Optional[int] = None,
+        episodic_type: str = "all",
+    ) -> Dict[str, Any]:
+        """
+        获取情景记忆分页列表
+
+        Args:
+            end_user_id: 终端用户ID
+            page: 页码
+            pagesize: 每页数量
+            start_date: 开始时间戳（毫秒），可选
+            end_date: 结束时间戳（毫秒），可选
+            episodic_type: 情景类型筛选
+
+        Returns:
+            {
+                "total": int,          # 该用户情景记忆总数（不受筛选影响）
+                "items": [...],        # 当前页数据
+                "page": {
+                    "page": int,
+                    "pagesize": int,
+                    "total": int,      # 筛选后总数
+                    "hasnext": bool
+                }
+            }
+        """
+        try:
+            logger.info(
+                f"情景记忆分页查询: end_user_id={end_user_id}, "
+                f"start_date={start_date}, end_date={end_date}, "
+                f"episodic_type={episodic_type}, page={page}, pagesize={pagesize}"
+            )
+
+            # 1. 查询情景记忆总数（不受筛选条件限制）
+            total_all_query = """
+            MATCH (s:MemorySummary)
+            WHERE s.end_user_id = $end_user_id
+            RETURN count(s) AS total
+            """
+            total_all_result = await self.neo4j_connector.execute_query(
+                total_all_query, end_user_id=end_user_id
+            )
+            total_all = total_all_result[0]["total"] if total_all_result else 0
+
+            # 2. 构建筛选条件
+            where_clauses = ["s.end_user_id = $end_user_id"]
+            params = {"end_user_id": end_user_id}
+
+            # 时间戳筛选（毫秒时间戳转为 UTC ISO 字符串，使用 Neo4j datetime() 精确比较）
+            if start_date is not None and end_date is not None:
+                from datetime import datetime, timezone
+                start_dt = datetime.fromtimestamp(start_date / 1000, tz=timezone.utc)
+                end_dt = datetime.fromtimestamp(end_date / 1000, tz=timezone.utc)
+                # 开始时间取当天 UTC 00:00:00，结束时间取当天 UTC 23:59:59.999999
+                start_iso = start_dt.strftime("%Y-%m-%dT") + "00:00:00.000000"
+                end_iso = end_dt.strftime("%Y-%m-%dT") + "23:59:59.999999"
+            
+                where_clauses.append("datetime(s.created_at) >= datetime($start_iso) AND datetime(s.created_at) <= datetime($end_iso)")
+                params["start_iso"] = start_iso
+                params["end_iso"] = end_iso
+
+            # 类型筛选下推到 Cypher（兼容中英文）
+            if episodic_type != "all":
+                type_mapping = {
+                    "conversation": "对话",
+                    "project_work": "项目/工作",
+                    "learning": "学习",
+                    "decision": "决策",
+                    "important_event": "重要事件"
+                }
+                chinese_type = type_mapping.get(episodic_type)
+                if chinese_type:
+                    where_clauses.append(
+                        "(s.memory_type = $episodic_type OR s.memory_type = $chinese_type)"
+                    )
+                    params["episodic_type"] = episodic_type
+                    params["chinese_type"] = chinese_type
+                else:
+                    where_clauses.append("s.memory_type = $episodic_type")
+                    params["episodic_type"] = episodic_type
+
+            where_str = " AND ".join(where_clauses)
+
+            # 3. 查询筛选后的总数
+            count_query = f"""
+            MATCH (s:MemorySummary)
+            WHERE {where_str}
+            RETURN count(s) AS total
+            """
+            count_result = await self.neo4j_connector.execute_query(count_query, **params)
+            filtered_total = count_result[0]["total"] if count_result else 0
+
+            # 4. 查询分页数据
+            skip = (page - 1) * pagesize
+            data_query = f"""
+            MATCH (s:MemorySummary)
+            WHERE {where_str}
+            RETURN elementId(s) AS id,
+                s.name AS title,
+                s.memory_type AS memory_type,
+                s.content AS content,
+                s.created_at AS created_at
+            ORDER BY s.created_at DESC
+            SKIP $skip LIMIT $limit
+            """
+            params["skip"] = skip
+            params["limit"] = pagesize
+
+            result = await self.neo4j_connector.execute_query(data_query, **params)
+
+            # 5. 处理结果
+            items = []
+            if result:
+                for record in result:
+                    raw_created_at = record.get("created_at")
+                    created_at_timestamp = self.parse_timestamp(raw_created_at)
+                    items.append({
+                        "id": record["id"],
+                        "title": record.get("title") or "未命名",
+                        "memory_type": record.get("memory_type") or "其他",
+                        "content": record.get("content") or "",
+                        "created_at": created_at_timestamp
+                    })
+
+            # 6. 构建返回结果
+            return {
+                "total": total_all,
+                "items": items,
+                "page": {
+                    "page": page,
+                    "pagesize": pagesize,
+                    "total": filtered_total,
+                    "hasnext": (page * pagesize) < filtered_total
+                }
+            }
+
+        except Exception as e:
+            logger.error(f"情景记忆分页查询出错: {str(e)}", exc_info=True)
+            raise
+
+    async def get_semantic_memory_list(
+        self,
+        end_user_id: str
+    ) -> list:
+        """
+        获取语义记忆全量列表
+
+        Args:
+            end_user_id: 终端用户ID
+
+        Returns:
+            [
+                {
+                    "id": str,
+                    "name": str,
+                    "entity_type": str,
+                    "core_definition": str
+                }
+            ]
+        """
+        try:
+            logger.info(f"语义记忆列表查询: end_user_id={end_user_id}")
+
+            semantic_query = """
+            MATCH (e:ExtractedEntity)
+            WHERE e.end_user_id = $end_user_id
+            AND e.is_explicit_memory = true
+            RETURN elementId(e) AS id,
+                e.name AS name,
+                e.entity_type AS entity_type,
+                e.description AS core_definition
+            ORDER BY e.name ASC
+            """
+
+            result = await self.neo4j_connector.execute_query(
+                semantic_query, end_user_id=end_user_id
+            )
+
+            items = []
+            if result:
+                for record in result:
+                    items.append({
+                        "id": record["id"],
+                        "name": record.get("name") or "未命名",
+                        "entity_type": record.get("entity_type") or "未分类",
+                        "core_definition": record.get("core_definition") or ""
+                    })
+
+            logger.info(f"语义记忆列表查询成功: end_user_id={end_user_id}, total={len(items)}")
+
+            return items
+
+        except Exception as e:
+            logger.error(f"语义记忆列表查询出错: {str(e)}", exc_info=True)
+            raise
+
     async def get_explicit_memory_details(
         self,
         end_user_id: str,

api/app/services/tool_service.py

@@ -815,11 +815,12 @@ class ToolService:
                         "default": param_info.get("default")
                     })
                 
-                # 请求体参数
+                # 请求体参数 — _extract_request_body 返回 {"schema": {...}, "required": bool, ...}
                 request_body = operation.get("request_body")
                 if request_body:
-                    schema_props = request_body.get("schema", {}).get("properties", {})
-                    required_props = request_body.get("schema", {}).get("required", [])
+                    body_schema = request_body.get("schema", {})
+                    schema_props = body_schema.get("properties", {})
+                    required_props = body_schema.get("required", [])
                     
                     for prop_name, prop_schema in schema_props.items():
                         parameters.append({

api/app/services/workflow_service.py

@@ -17,8 +17,9 @@ from app.core.workflow.executor import execute_workflow, execute_workflow_stream
 from app.core.workflow.nodes.enums import NodeType
 from app.core.workflow.validator import validate_workflow_config
 from app.db import get_db
+from sqlalchemy import select
 from app.models import App
-from app.models.workflow_model import WorkflowConfig, WorkflowExecution
+from app.models.workflow_model import WorkflowConfig, WorkflowExecution, WorkflowNodeExecution
 from app.repositories import knowledge_repository
 from app.repositories.workflow_repository import (
     WorkflowConfigRepository,
@@ -918,6 +919,7 @@ class WorkflowService:
                 input_data["conv_messages"] = conv_messages
             init_message_length = len(input_data.get("conv_messages", []))
             message_id = uuid.uuid4()
+            _cycle_items: dict[str, list] = {}
 
             # 新会话时写入开场白
             is_new_conversation = init_message_length == 0
@@ -948,6 +950,15 @@ class WorkflowService:
                     memory_storage_type=storage_type,
                     user_rag_memory_id=user_rag_memory_id
             ):
+                event_type = event.get("event")
+                event_data = event.get("data", {})
+
+                if event_type == "cycle_item":
+                    cycle_id = event_data.get("cycle_id")
+                    if cycle_id not in _cycle_items:
+                        _cycle_items[cycle_id] = []
+                    _cycle_items[cycle_id].append(event_data)
+
                 if event.get("event") == "workflow_end":
                     status = event.get("data", {}).get("status")
                     token_usage = event.get("data", {}).get("token_usage", {}) or {}
@@ -1019,6 +1030,18 @@ class WorkflowService:
                         )
                     else:
                         logger.error(f"unexpect workflow run status, status: {status}")
+                    # 把积累的 cycle_item 写入 workflow_executions.output_data["node_outputs"]
+                    if _cycle_items and execution.output_data:
+                        import copy
+                        new_output_data = copy.deepcopy(execution.output_data)
+                        node_outputs = new_output_data.setdefault("node_outputs", {})
+                        for cycle_node_id, items in _cycle_items.items():
+                            if cycle_node_id in node_outputs:
+                                node_outputs[cycle_node_id]["cycle_items"] = items
+                            else:
+                                node_outputs[cycle_node_id] = {"cycle_items": items}
+                        execution.output_data = new_output_data
+                        self.db.commit()
                 elif event.get("event") == "workflow_start":
                     event["data"]["message_id"] = str(message_id)
                 event = self._emit(public, event)

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 -m app.celery_task_scheduler
+    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