cortex/data/self_state.json

@@ -3,9 +3,9 @@
   "energy": 0.8,
   "focus": "user_request",
   "confidence": 0.7,
-  "curiosity": 0.7000000000000002,
+  "curiosity": 1.0,
-  "last_updated": "2025-12-14T19:29:49.051207",
+  "last_updated": "2025-12-15T05:38:06.084867",
-  "interaction_count": 5,
+  "interaction_count": 14,
   "learning_queue": [],
   "active_goals": [],
   "preferences": {

cortex/router.py

@@ -301,10 +301,9 @@ async def run_reason(req: ReasonRequest):
     if proactive_enabled:
         try:
             from autonomy.proactive.monitor import get_proactive_monitor
-            from autonomy.self.state import load_self_state
 
             monitor = get_proactive_monitor(min_priority=proactive_min_priority)
-            self_state = load_self_state()
+            self_state = load_self_state()  # Already imported at top of file
 
             suggestion = await monitor.analyze_session(
                 session_id=req.session_id,

neomem/neomem/vector_stores/qdrant.py

@@ -1,7 +1,9 @@
 import logging
 import os
 import shutil
+from typing import Optional
 
+from pydantic import BaseModel
 from qdrant_client import QdrantClient
 from qdrant_client.models import (
     Distance,
@@ -19,6 +21,13 @@ from mem0.vector_stores.base import VectorStoreBase
 logger = logging.getLogger(__name__)
 
 
+class OutputData(BaseModel):
+    """Standard output format for vector search results."""
+    id: Optional[str]
+    score: Optional[float]
+    payload: Optional[dict]
+
+
 class Qdrant(VectorStoreBase):
     def __init__(
         self,
@@ -170,7 +179,7 @@ class Qdrant(VectorStoreBase):
             filters (dict, optional): Filters to apply to the search. Defaults to None.
 
         Returns:
-            list: Search results.
+            list: Search results wrapped in OutputData format.
         """
         query_filter = self._create_filter(filters) if filters else None
         hits = self.client.query_points(
@@ -179,7 +188,16 @@ class Qdrant(VectorStoreBase):
             query_filter=query_filter,
             limit=limit,
         )
-        return hits.points
+
+        # Wrap results in OutputData format to match other vector stores
+        return [
+            OutputData(
+                id=str(hit.id),
+                score=hit.score,
+                payload=hit.payload
+            )
+            for hit in hits.points
+        ]
 
     def delete(self, vector_id: int):
         """
@@ -207,7 +225,7 @@ class Qdrant(VectorStoreBase):
         point = PointStruct(id=vector_id, vector=vector, payload=payload)
         self.client.upsert(collection_name=self.collection_name, points=[point])
 
-    def get(self, vector_id: int) -> dict:
+    def get(self, vector_id: int) -> OutputData:
         """
         Retrieve a vector by ID.
 
@@ -215,10 +233,17 @@ class Qdrant(VectorStoreBase):
             vector_id (int): ID of the vector to retrieve.
 
         Returns:
-            dict: Retrieved vector.
+            OutputData: Retrieved vector wrapped in OutputData format.
         """
         result = self.client.retrieve(collection_name=self.collection_name, ids=[vector_id], with_payload=True)
-        return result[0] if result else None
+        if result:
+            hit = result[0]
+            return OutputData(
+                id=str(hit.id),
+                score=None,  # No score for direct retrieval
+                payload=hit.payload
+            )
+        return None
 
     def list_cols(self) -> list:
         """
@@ -251,7 +276,7 @@ class Qdrant(VectorStoreBase):
             limit (int, optional): Number of vectors to return. Defaults to 100.
 
         Returns:
-            list: List of vectors.
+            list: List of vectors wrapped in OutputData format.
         """
         query_filter = self._create_filter(filters) if filters else None
         result = self.client.scroll(
@@ -261,7 +286,18 @@ class Qdrant(VectorStoreBase):
             with_payload=True,
             with_vectors=False,
         )
-        return result
+
+        # Wrap results in OutputData format
+        # scroll() returns tuple: (points, next_page_offset)
+        points = result[0] if isinstance(result, tuple) else result
+        return [
+            OutputData(
+                id=str(point.id),
+                score=None,  # No score for list operation
+                payload=point.payload
+            )
+            for point in points
+        ]
 
     def reset(self):
         """Reset the index by deleting and recreating it."""