refactor: scheduler

src/memos/configs/mem_scheduler.py

@@ -16,6 +16,7 @@
     DEFAULT_CONSUME_INTERVAL_SECONDS,
     DEFAULT_CONTEXT_WINDOW_SIZE,
     DEFAULT_MAX_INTERNAL_MESSAGE_QUEUE_SIZE,
+    DEFAULT_MONITOR_INTERVAL_SECONDS,
     DEFAULT_MULTI_TASK_RUNNING_TIMEOUT,
     DEFAULT_SCHEDULER_RETRIEVER_BATCH_SIZE,
     DEFAULT_SCHEDULER_RETRIEVER_RETRIES,
@@ -68,6 +69,10 @@ class BaseSchedulerConfig(BaseConfig):
         default=DEFAULT_MAX_INTERNAL_MESSAGE_QUEUE_SIZE,
         description="Maximum size of internal message queue when not using Redis",
     )
+    monitor_interval_seconds: int = Field(
+        default=DEFAULT_MONITOR_INTERVAL_SECONDS,
+        description="Interval in seconds for monitoring memory usage",
+    )
     multi_task_running_timeout: int = Field(
         default=DEFAULT_MULTI_TASK_RUNNING_TIMEOUT,
         description="Default timeout for multi-task running operations in seconds",

src/memos/mem_scheduler/analyzer/scheduler_for_eval.py

@@ -164,11 +164,33 @@ def update_working_memory_for_eval(
             new_candidates = []
             for item in missing_evidences:
                 logger.info(f"missing_evidences: {item}")
-                results: list[TextualMemoryItem] = self.retriever.search(
+
+                # Determine search mode from method
+                from memos.mem_scheduler.schemas.general_schemas import (
+                    TreeTextMemory_FINE_SEARCH_METHOD,
+                    TreeTextMemory_SEARCH_METHOD,
+                )
+                from memos.types.general_types import SearchMode
+
+                # Convert search_method to SearchMode
+                if self.search_method == TreeTextMemory_FINE_SEARCH_METHOD:
+                    mode = SearchMode.FINE
+                elif self.search_method == TreeTextMemory_SEARCH_METHOD:
+                    mode = SearchMode.FAST
+                else:
+                    # Fallback to FAST mode for unknown methods
+                    logger.warning(
+                        f"Unknown search_method '{self.search_method}', falling back to SearchMode.FAST"
+                    )
+                    mode = SearchMode.FAST
+
+                # Use unified search service
+                results: list[TextualMemoryItem] = self.search_service.search(
                     query=item,
+                    user_id=user_id,
                     mem_cube=mem_cube,
                     top_k=k_per_evidence,
-                    method=self.search_method,
+                    mode=mode,
                 )
                 logger.info(
                     f"search results for {missing_evidences}: {[one.memory for one in results]}"
@@ -226,7 +248,7 @@ def evaluate_memory_answer_ability(
 
         try:
             # Extract JSON response
-            from memos.mem_scheduler.utils.misc_utils import extract_json_obj
+            from memos.utils import extract_json_obj
 
             result = extract_json_obj(response)