add give a star action in logs of enable

memori/core/memory.py

@@ -1213,7 +1213,7 @@ def _get_auto_ingest_context(self, user_input: str) -> list[dict[str, Any]]:
                         results[:3]
                     ):  # Log first 3 results for debugging
                         logger.debug(
-                            f"Auto-ingest: Result {i+1}: {type(result)} with keys: {list(result.keys()) if isinstance(result, dict) else 'N/A'}"
+                            f"Auto-ingest: Result {i + 1}: {type(result)} with keys: {list(result.keys()) if isinstance(result, dict) else 'N/A'}"
                         )
             except Exception as db_search_e:
                 logger.error(f"Auto-ingest: Database search failed: {db_search_e}")

memori/database/mongodb_manager.py

@@ -928,6 +928,28 @@ def mark_conscious_memories_processed(
         except Exception as e:
             logger.error(f"Failed to mark conscious memories processed: {e}")
 
+    def _check_milestone(self, memory_count: int):
+        """
+        Check and celebrate memory storage milestones to encourage user engagement.
+        Displays celebration messages at key milestones: 10, 50, 100, 500, 1000 memories.
+
+        Args:
+            memory_count: Current count of long-term memories
+        """
+        milestones = [10, 50, 100, 500, 1000]
+
+        if memory_count in milestones:
+            celebration_msg = (
+                f"\n{'=' * 60}\n"
+                f"🎉 Milestone Achieved: {memory_count} memories stored!\n"
+                f"{'=' * 60}\n"
+                f"⭐️ Loving Memori? Give us a star on GitHub!\n"
+                f"👉 https://github.com/GibsonAI/memori\n"
+                f"Your support helps us build better open AI memory tools ❤️\n"
+                f"{'=' * 60}\n"
+            )
+            logger.info(celebration_msg)
+
     def store_long_term_memory_enhanced(
         self, memory: ProcessedLongTermMemory, chat_id: str, namespace: str = "default"
     ) -> str:
@@ -1000,6 +1022,13 @@ def store_long_term_memory_enhanced(
             collection.insert_one(document)
 
             logger.debug(f"Stored enhanced long-term memory {memory_id}")
+
+            # Get current memory count and check for milestones
+            total_memories = collection.count_documents({"namespace": namespace})
+
+            # Celebrate milestone if reached
+            self._check_milestone(total_memories)
+
             return memory_id
 
         except Exception as e:

memori/database/sqlalchemy_manager.py

@@ -572,6 +572,28 @@ def get_chat_history(
             except SQLAlchemyError as e:
                 raise DatabaseError(f"Failed to get chat history: {e}")
 
+    def _check_milestone(self, memory_count: int):
+        """
+        Check and celebrate memory storage milestones to encourage user engagement.
+        Displays celebration messages at key milestones: 10, 50, 100, 500, 1000 memories.
+
+        Args:
+            memory_count: Current count of long-term memories
+        """
+        milestones = [10, 50, 100, 500, 1000]
+
+        if memory_count in milestones:
+            celebration_msg = (
+                f"\n{'=' * 60}\n"
+                f"🎉 Milestone Achieved: {memory_count} memories stored!\n"
+                f"{'=' * 60}\n"
+                f"⭐️ Loving Memori? Give us a star on GitHub!\n"
+                f"👉 https://github.com/GibsonAI/memori\n"
+                f"Your support helps us build better open AI memory tools ❤️\n"
+                f"{'=' * 60}\n"
+            )
+            logger.info(celebration_msg)
+
     def store_long_term_memory_enhanced(
         self, memory: ProcessedLongTermMemory, chat_id: str, namespace: str = "default"
     ) -> str:
@@ -618,6 +640,17 @@ def store_long_term_memory_enhanced(
                 session.commit()
 
                 logger.debug(f"Stored enhanced long-term memory {memory_id}")
+
+                # Get current memory count and check for milestones
+                total_memories = (
+                    session.query(LongTermMemory)
+                    .filter(LongTermMemory.namespace == namespace)
+                    .count()
+                )
+
+                # Celebrate milestone if reached
+                self._check_milestone(total_memories)
+
                 return memory_id
 
             except SQLAlchemyError as e: