**Improved **

memory/embeddings/utils.py

@@ -96,12 +96,34 @@ def object_to_text(obj: Any) -> str:
         parts = []
         for key, value in obj.items():
             if isinstance(value, dict):
-                formatted = f"{key}: " + object_to_text(value)
+                # Special case for position
+                if key == "position":
+                    position_parts = []
+                    if "room" in value:
+                        position_parts.append(f"room is {value['room']}")
+                    if "x" in value and "y" in value:
+                        position_parts.append(f"coordinates: {value['x']}, {value['y']}")
+                    if "facing" in value:
+                        position_parts.append(f"facing {value['facing']}")
+                    parts.append(" | ".join(position_parts))
+                else:
+                    formatted = f"{key}: " + object_to_text(value)
+                    parts.append(formatted)
             elif isinstance(value, list):
-                formatted = f"{key}: " + ", ".join(str(item) for item in value)
+                # Special case for inventory
+                if key == "inventory":
+                    if not value:
+                        parts.append("empty inventory")
+                    else:
+                        # Add individual "has" statements for each item
+                        for item in value:
+                            parts.append(f"has {item}")
+                else:
+                    formatted = f"{key}: " + ", ".join(str(item) for item in value)
+                    parts.append(formatted)
             else:
                 formatted = f"{key}: {value}"
-            parts.append(formatted)
+                parts.append(formatted)
         return " | ".join(parts)
     elif isinstance(obj, list):
         if not obj:

memory/search/strategies/match.py

@@ -109,9 +109,6 @@ def search(
         if tier == "im" and fields_mask:
             # Field masking with IM tier needs lower threshold
             adjusted_min_score = 0.4  
-        elif tier == "ltm":
-            # LTM tier has more compressed vectors, lower threshold
-            adjusted_min_score = 0.4  
 
         # Find similar memories
         search_limit = limit * 2  # Get more results for post-filtering

tests/embeddings/test_embedding_utils.py

@@ -186,15 +186,16 @@ def test_text_processing_for_embeddings():
 
     # Dictionary with inventory
     inventory_dict = {"inventory": ["sword", "shield", "potion"]}
-    assert "has sword, shield, potion" in object_to_text(inventory_dict)
+    text = object_to_text(inventory_dict)
+    assert "has sword" in text
+    assert "has shield" in text
+    assert "has potion" in text
 
     # Dictionary with position
     position_dict = {"position": {"room": "kitchen", "x": 5, "y": 10}}
     position_text = object_to_text(position_dict)
     assert "room is kitchen" in position_text
-    assert "coordinates" in position_text
-    assert "5" in position_text
-    assert "10" in position_text
+    assert "coordinates: 5, 10" in position_text
 
 def test_object_to_text_realistic_agent_state():
     """Test object_to_text with realistic agent state information."""

tests/search/test_match_strategy.py

@@ -50,7 +50,7 @@ def test_search_with_example_object(self):
         ]
 
         # Mock memory retrieval
-        self.mock_stm_store.get.side_effect = lambda id: {
+        self.mock_stm_store.get.side_effect = lambda agent_id, memory_id: {
             "mem1": {
                 "id": "mem1",
                 "content": {"type": "task", "priority": "high", "status": "pending"},
@@ -70,7 +70,7 @@ def test_search_with_example_object(self):
                 "content": {"type": "note", "priority": "low"},
                 "metadata": {"tags": ["reference"]},
             },
-        }.get(id)
+        }.get(memory_id)
 
         # Perform search
         results = self.strategy.search(
@@ -105,7 +105,7 @@ def test_search_with_fields_mask(self):
         ]
 
         # Mock memory retrieval
-        self.mock_im_store.get.side_effect = lambda id: {
+        self.mock_im_store.get.side_effect = lambda agent_id, memory_id: {
             "mem1": {
                 "id": "mem1",
                 "content": {
@@ -125,7 +125,7 @@ def test_search_with_fields_mask(self):
                 },
                 "metadata": {},
             },
-        }.get(id)
+        }.get(memory_id)
 
         # Perform search with fields mask
         results = self.strategy.search(
@@ -158,11 +158,11 @@ def test_search_with_min_score(self):
         ]
 
         # Mock memory retrieval
-        self.mock_ltm_store.get.side_effect = lambda id: {
+        self.mock_ltm_store.get.side_effect = lambda agent_id, memory_id: {
             "mem1": {"id": "mem1", "content": "matching content 1", "metadata": {}},
             "mem2": {"id": "mem2", "content": "matching content 2", "metadata": {}},
             "mem3": {"id": "mem3", "content": "matching content 3", "metadata": {}},
-        }.get(id)
+        }.get(memory_id)
 
         # Perform search with min_score
         results = self.strategy.search(
@@ -193,7 +193,7 @@ def test_search_with_metadata_filter(self):
         ]
 
         # Mock memory retrieval
-        self.mock_stm_store.get.side_effect = lambda id: {
+        self.mock_stm_store.get.side_effect = lambda agent_id, memory_id: {
             "mem1": {
                 "id": "mem1",
                 "content": "content 1",
@@ -204,7 +204,7 @@ def test_search_with_metadata_filter(self):
                 "content": "content 2",
                 "metadata": {"type": "note"},
             },
-        }.get(id)
+        }.get(memory_id)
 
         # Perform search with metadata filter
         metadata_filter = {"type": "task"}
@@ -349,11 +349,11 @@ def test_search_with_none_memory(self):
         ]
 
         # Mock memory retrieval with one memory missing
-        self.mock_stm_store.get.side_effect = lambda id: {
+        self.mock_stm_store.get.side_effect = lambda agent_id, memory_id: {
             "mem1": {"id": "mem1", "content": "content 1", "metadata": {}},
             "mem2": None,  # Missing memory
             "mem3": {"id": "mem3", "content": "content 3", "metadata": {}},
-        }.get(id)
+        }.get(memory_id)
 
         # Perform search
         results = self.strategy.search(