refactor: add vector validation to OpenSearchDB to ensure non‑null, non‑empty, and correct‑dimension vectors

docs/templates/cookbook_template.mdx

@@ -166,13 +166,13 @@ Call out the most common mistake or edge case for this layer.
     title="[Related cookbook / deep dive]"
     description="[Why this pairs well with the current guide]"
     icon="arrow-right"
-    href="#related-link"
+    href="/[related-link]"
   />
   <Card
     title="[Next cookbook in journey]"
     description="[Set expectation for the next step]"
     icon="rocket"
-    href="#next-link"
+    href="/[next-link]"
   />
 </CardGroup>
 ```

docs/templates/migration_guide_template.mdx

@@ -145,13 +145,13 @@ npm install mem0ai@[version]
     title="[Deep dive reference]"
     description="[Why this reference matters post-migration]"
     icon="book"
-    href="#reference-link"
+    href="/[reference-link]"
   />
   <Card
     title="[Applied example or next step]"
     description="[What readers can build now]"
     icon="rocket"
-    href="#example-link"
+    href="/[example-link]"
   />
 </CardGroup>
 ```

mem0-ts/src/oss/src/prompts/index.ts

@@ -31,7 +31,6 @@ export const MemoryUpdateSchema = z.object({
         old_memory: z
           .string()
           .optional()
-          .nullable()
           .describe(
             "The previous content of the memory item if the event was UPDATE.",
           ),

mem0/memory/main.py

@@ -9,7 +9,7 @@ import uuid
 import warnings
 from copy import deepcopy
 from datetime import datetime, timezone
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, Optional
 
 from pydantic import ValidationError
 
@@ -479,8 +479,7 @@ class Memory(MemoryBase):
 
                 msg_content = message_dict["content"]
                 msg_embeddings = self.embedding_model.embed(msg_content, "add")
-                # Pass embeddings as a dict so _create_memory can reuse the cached embedding
+                mem_id = self._create_memory(msg_content, msg_embeddings, per_msg_meta)
-                mem_id = self._create_memory(msg_content, {msg_content: msg_embeddings}, per_msg_meta)
 
                 returned_memories.append(
                     {
@@ -609,9 +608,6 @@ class Memory(MemoryBase):
 
                     event_type = resp.get("event")
                     if event_type == "ADD":
-                        # Ensure action_text has an embedding cached to avoid redundant API calls
-                        if action_text not in new_message_embeddings:
-                            new_message_embeddings[action_text] = self.embedding_model.embed(action_text, "add")
                         memory_id = self._create_memory(
                             data=action_text,
                             existing_embeddings=new_message_embeddings,
@@ -619,9 +615,6 @@ class Memory(MemoryBase):
                         )
                         returned_memories.append({"id": memory_id, "memory": action_text, "event": event_type})
                     elif event_type == "UPDATE":
-                        # Ensure action_text has an embedding cached to avoid redundant API calls
-                        if action_text not in new_message_embeddings:
-                            new_message_embeddings[action_text] = self.embedding_model.embed(action_text, "update")
                         self._update_memory(
                             memory_id=temp_uuid_mapping[resp.get("id")],
                             data=action_text,
@@ -1160,13 +1153,10 @@ class Memory(MemoryBase):
         capture_event("mem0.history", self, {"memory_id": memory_id, "sync_type": "sync"})
         return self.db.get_history(memory_id)
 
-    def _create_memory(self, data: str, existing_embeddings: Union[Dict[str, List[float]], List[float]], metadata=None):
+    def _create_memory(self, data, existing_embeddings, metadata=None):
         logger.debug(f"Creating memory with {data=}")
-        # existing_embeddings may be a dict (preferred) or a precomputed vector
+        if data in existing_embeddings:
-        if isinstance(existing_embeddings, dict) and data in existing_embeddings:
             embeddings = existing_embeddings[data]
-        elif not isinstance(existing_embeddings, dict):
-            embeddings = existing_embeddings
         else:
             embeddings = self.embedding_model.embed(data, memory_action="add")
         memory_id = str(uuid.uuid4())
@@ -1230,7 +1220,7 @@ class Memory(MemoryBase):
 
         return result
 
-    def _update_memory(self, memory_id, data: str, existing_embeddings: Union[Dict[str, List[float]], List[float]], metadata=None):
+    def _update_memory(self, memory_id, data, existing_embeddings, metadata=None):
         logger.info(f"Updating memory with {data=}")
 
         try:
@@ -1263,10 +1253,8 @@ class Memory(MemoryBase):
         if "role" not in new_metadata and "role" in existing_memory.payload:
             new_metadata["role"] = existing_memory.payload["role"]
 
-        if isinstance(existing_embeddings, dict) and data in existing_embeddings:
+        if data in existing_embeddings:
             embeddings = existing_embeddings[data]
-        elif not isinstance(existing_embeddings, dict):
-            embeddings = existing_embeddings
         else:
             embeddings = self.embedding_model.embed(data, "update")
 
@@ -1535,8 +1523,7 @@ class AsyncMemory(MemoryBase):
 
                 msg_content = message_dict["content"]
                 msg_embeddings = await asyncio.to_thread(self.embedding_model.embed, msg_content, "add")
-                # Pass embeddings as a dict so _create_memory can reuse the cached embedding
+                mem_id = await self._create_memory(msg_content, msg_embeddings, per_msg_meta)
-                mem_id = await self._create_memory(msg_content, {msg_content: msg_embeddings}, per_msg_meta)
 
                 returned_memories.append(
                     {
@@ -1664,11 +1651,6 @@ class AsyncMemory(MemoryBase):
                     event_type = resp.get("event")
 
                     if event_type == "ADD":
-                        # Ensure action_text has an embedding cached to avoid redundant API calls
-                        if action_text not in new_message_embeddings:
-                            new_message_embeddings[action_text] = await asyncio.to_thread(
-                                self.embedding_model.embed, action_text, "add"
-                            )
                         task = asyncio.create_task(
                             self._create_memory(
                                 data=action_text,
@@ -1678,11 +1660,6 @@ class AsyncMemory(MemoryBase):
                         )
                         memory_tasks.append((task, resp, "ADD", None))
                     elif event_type == "UPDATE":
-                        # Ensure action_text has an embedding cached to avoid redundant API calls
-                        if action_text not in new_message_embeddings:
-                            new_message_embeddings[action_text] = await asyncio.to_thread(
-                                self.embedding_model.embed, action_text, "update"
-                            )
                         task = asyncio.create_task(
                             self._update_memory(
                                 memory_id=temp_uuid_mapping[resp["id"]],
@@ -2252,13 +2229,10 @@ class AsyncMemory(MemoryBase):
         capture_event("mem0.history", self, {"memory_id": memory_id, "sync_type": "async"})
         return await asyncio.to_thread(self.db.get_history, memory_id)
 
-    async def _create_memory(self, data: str, existing_embeddings: Union[Dict[str, List[float]], List[float]], metadata=None):
+    async def _create_memory(self, data, existing_embeddings, metadata=None):
         logger.debug(f"Creating memory with {data=}")
-        # existing_embeddings may be a dict (preferred) or a precomputed vector
+        if data in existing_embeddings:
-        if isinstance(existing_embeddings, dict) and data in existing_embeddings:
             embeddings = existing_embeddings[data]
-        elif not isinstance(existing_embeddings, dict):
-            embeddings = existing_embeddings
         else:
             embeddings = await asyncio.to_thread(self.embedding_model.embed, data, memory_action="add")
 
@@ -2341,7 +2315,7 @@ class AsyncMemory(MemoryBase):
 
         return result
 
-    async def _update_memory(self, memory_id, data: str, existing_embeddings: Union[Dict[str, List[float]], List[float]], metadata=None):
+    async def _update_memory(self, memory_id, data, existing_embeddings, metadata=None):
         logger.info(f"Updating memory with {data=}")
 
         try:
@@ -2375,10 +2349,8 @@ class AsyncMemory(MemoryBase):
         if "role" not in new_metadata and "role" in existing_memory.payload:
             new_metadata["role"] = existing_memory.payload["role"]
 
-        if isinstance(existing_embeddings, dict) and data in existing_embeddings:
+        if data in existing_embeddings:
             embeddings = existing_embeddings[data]
-        elif not isinstance(existing_embeddings, dict):
-            embeddings = existing_embeddings
         else:
             embeddings = await asyncio.to_thread(self.embedding_model.embed, data, "update")
 

mem0/vector_stores/mongodb.py

@@ -26,7 +26,7 @@ class OutputData(BaseModel):
 
 
 class MongoDB(VectorStoreBase):
-    VECTOR_TYPE = "vector"
+    VECTOR_TYPE = "knnVector"
     SIMILARITY_METRIC = "cosine"
 
     def __init__(self, db_name: str, collection_name: str, embedding_model_dims: int, mongo_uri: str):
@@ -69,16 +69,17 @@ class MongoDB(VectorStoreBase):
             else:
                 search_index_model = SearchIndexModel(
                     name=self.index_name,
-                    type="vectorSearch",
                     definition={
-                        "fields": [
+                        "mappings": {
-                            {
+                            "dynamic": False,
+                            "fields": {
+                                "embedding": {
                                     "type": self.VECTOR_TYPE,
-                                "path": "embedding",
+                                    "dimensions": self.embedding_model_dims,
-                                "numDimensions": self.embedding_model_dims,
                                     "similarity": self.SIMILARITY_METRIC,
                                 }
-                        ]
+                            },
+                        }
                     },
                 )
                 collection.create_search_index(search_index_model)
@@ -140,7 +141,7 @@ class MongoDB(VectorStoreBase):
                     "$vectorSearch": {
                         "index": self.index_name,
                         "limit": limit,
-                        "numCandidates": min(limit * 20, 10000),
+                        "numCandidates": limit,
                         "queryVector": vectors,
                         "path": "embedding",
                     }

openmemory/api/app/mcp_server.py

@@ -184,7 +184,7 @@ async def search_memory(query: str) -> str:
             for h in hits:
                 # All vector db search functions return OutputData class
                 id, score, payload = h.id, h.score, h.payload
-                if allowed and (h.id is None or h.id not in allowed):
+                if allowed and h.id is None or h.id not in allowed: 
                     continue
                 
                 results.append({

tests/test_memory.py

@@ -438,137 +438,3 @@ async def test_async_update_nonexistent_memory_raises_error(mock_sqlite, mock_ll
         await memory._update_memory("non-existent-id", "new data", {"new data": [0.1, 0.2]})
 
     mock_vector_store.update.assert_not_called()
-
-
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.storage.SQLiteManager')
-def test_add_infer_false_embeds_once(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """
-    Regression test for issue #3723: adding with infer=False should not trigger duplicate embedding calls.
-
-    Root cause: _create_memory expected a dict for existing_embeddings but received a raw list[float],
-    causing the cache check `data in existing_embeddings` to always fail and trigger a redundant embed.
-    """
-    embedder = MagicMock()
-    embedder.embed.return_value = [0.1, 0.2, 0.3]
-    embedder.config = MagicMock(embedding_dims=3)
-    mock_embedder_factory.return_value = embedder
-
-    mock_vector_store = MagicMock()
-    mock_vector_store.search.return_value = []
-    mock_vector_store.insert.return_value = None
-    mock_vector_store.get.return_value = None
-    telemetry_vector_store = MagicMock()
-    mock_vector_factory.side_effect = [mock_vector_store, telemetry_vector_store]
-
-    mock_llm_factory.return_value = MagicMock()
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import Memory as MemoryClass
-    memory = MemoryClass(MemoryConfig())
-
-    memory.add("foo", user_id="test_user", infer=False)
-
-    assert embedder.embed.call_count == 1
-    mock_vector_store.insert.assert_called_once()
-
-
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.storage.SQLiteManager')
-def test_add_infer_true_caches_embedding_on_llm_rewrite(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """
-    Regression test for issue #3723 (infer=True path): when the LLM rewrites a fact during the
-    ADD action, the embedding should be computed once and cached, not computed again inside _create_memory.
-    """
-    embedder = MagicMock()
-    embedder.embed.return_value = [0.1, 0.2, 0.3]
-    embedder.config = MagicMock(embedding_dims=3)
-    mock_embedder_factory.return_value = embedder
-
-    mock_vector_store = MagicMock()
-    mock_vector_store.search.return_value = []
-    mock_vector_store.insert.return_value = None
-    mock_vector_store.get.return_value = None
-    telemetry_vector_store = MagicMock()
-    mock_vector_factory.side_effect = [mock_vector_store, telemetry_vector_store]
-
-    # LLM extracts fact "User likes Python", then ADD action rewrites to "The user enjoys Python"
-    mock_llm = MagicMock()
-    mock_llm.generate_response.side_effect = [
-        json.dumps({"facts": ["User likes Python"]}),
-        json.dumps({"memory": [{"id": "0", "text": "The user enjoys Python", "event": "ADD", "old_memory": None}]}),
-    ]
-    mock_llm_factory.return_value = mock_llm
-
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import Memory as MemoryClass
-    memory = MemoryClass(MemoryConfig())
-
-    memory.add("I like Python", user_id="test_user", infer=True)
-
-    # embed should be called exactly twice:
-    # 1. For the extracted fact "User likes Python" (search)
-    # 2. For the rewritten text "The user enjoys Python" (pre-cached before _create_memory)
-    # It should NOT be called a 3rd time inside _create_memory
-    assert embedder.embed.call_count == 2
-    mock_vector_store.insert.assert_called_once()
-
-
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.storage.SQLiteManager')
-def test_update_infer_true_caches_embedding_on_llm_rewrite(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """
-    Regression test for issue #3723 (infer=True UPDATE path): when the LLM rewrites a fact during
-    an UPDATE action, the embedding should be computed once and cached, not computed again inside _update_memory.
-    """
-    embedder = MagicMock()
-    embedder.embed.return_value = [0.1, 0.2, 0.3]
-    embedder.config = MagicMock(embedding_dims=3)
-    mock_embedder_factory.return_value = embedder
-
-    # Existing memory that will be matched for update
-    existing_memory = MockVectorMemory(
-        memory_id="existing-mem-id",
-        payload={
-            "data": "User likes Python",
-            "hash": "abc123",
-            "created_at": "2025-01-01T00:00:00+00:00",
-        },
-    )
-
-    mock_vector_store = MagicMock()
-    mock_vector_store.search.return_value = [existing_memory]
-    mock_vector_store.get.return_value = existing_memory
-    mock_vector_store.insert.return_value = None
-    mock_vector_store.update.return_value = None
-    telemetry_vector_store = MagicMock()
-    mock_vector_factory.side_effect = [mock_vector_store, telemetry_vector_store]
-
-    # LLM extracts fact "User loves Python now", then UPDATE action rewrites to "The user loves Python"
-    mock_llm = MagicMock()
-    mock_llm.generate_response.side_effect = [
-        json.dumps({"facts": ["User loves Python now"]}),
-        json.dumps({"memory": [{"id": "0", "text": "The user loves Python", "event": "UPDATE", "old_memory": "User likes Python"}]}),
-    ]
-    mock_llm_factory.return_value = mock_llm
-
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import Memory as MemoryClass
-    memory = MemoryClass(MemoryConfig())
-
-    memory.add("I love Python now", user_id="test_user", infer=True)
-
-    # embed should be called exactly twice:
-    # 1. For the extracted fact "User loves Python now" (search)
-    # 2. For the rewritten text "The user loves Python" (pre-cached before _update_memory)
-    # It should NOT be called a 3rd time inside _update_memory
-    assert embedder.embed.call_count == 2
-    mock_vector_store.update.assert_called_once()

tests/vector_stores/test_mongodb.py

@@ -48,16 +48,17 @@ def test_initalize_create_col(mongo_vector_fixture):
     search_index_model = args[0].document
     assert search_index_model == {
         "name": "test_collection_vector_index",
-        "type": "vectorSearch",
         "definition": {
-            "fields": [
+            "mappings": {
-                {
+                "dynamic": False,
-                    "type": "vector",
+                "fields": {
-                    "path": "embedding",
+                    "embedding": {
-                    "numDimensions": 1536,
+                        "type": "knnVector",
+                        "dimensions": 1536,
                         "similarity": "cosine",
                     }
-            ]
+                },
+            }
         },
     }
     assert mongo_vector.collection == mock_collection
@@ -94,7 +95,7 @@ def test_search(mongo_vector_fixture):
                 "$vectorSearch": {
                     "index": "test_collection_vector_index",
                     "limit": 2,
-                    "numCandidates": 40,
+                    "numCandidates": 2,
                     "queryVector": query_vector,
                     "path": "embedding",
                 },