Merge remote-tracking branch 'origin/main' into mintlify/af82bda4

docs/components/embedders/models/together.mdx

@@ -11,7 +11,7 @@ To use Together embedding models, set the `TOGETHER_API_KEY` environment variabl
 <Note> The `embedding_model_dims` parameter for `vector_store` should be set to `1024` for Together embedder. </Note>
 
 <Warning>
-**Breaking default change.** The default Together embedding model is now `intfloat/multilingual-e5-large-instruct` (**1024-dim**), replacing the previous default `togethercomputer/m2-bert-80M-8k-retrieval` (**768-dim**). If you created a self-hosted vector store with the old default, its collection is 768-dim and will reject the new 1024-dim vectors **recreate/reindex the collection at 1024 dimensions** after upgrading. To defer the change, pin the previous values explicitly (`model="togethercomputer/m2-bert-80M-8k-retrieval"`, `embedding_dims=768`) note Together no longer lists this model among its recommended embeddings, so reindexing at 1024 is the durable path.
+**Breaking default change.** The default Together embedding model is now `intfloat/multilingual-e5-large-instruct` (**1024-dim**), replacing the previous default `togethercomputer/m2-bert-80M-8k-retrieval` (**768-dim**). If you created a self-hosted vector store with the old default, its collection is 768-dim and will reject the new 1024-dim vectors — **recreate/reindex the collection at 1024 dimensions** after upgrading. To defer the change, pin the previous values explicitly (`model="togethercomputer/m2-bert-80M-8k-retrieval"`, `embedding_dims=768`) — note Together no longer lists this model among its recommended embeddings, so reindexing at 1024 is the durable path.
 </Warning>
 
 <CodeGroup>

docs/components/vectordbs/dbs/azure.mdx

@@ -95,7 +95,7 @@ Uses the identity from Azure PowerShell (`Connect-AzAccount`).
 7. **Azure Developer CLI Credential:**
 Uses the session from Azure Developer CLI (`azd auth login`).
 
-<Note> If an API is provided, it will be used for authentication over an Azure Identity </Note>
+<Note> If an API key is provided, it will be used for authentication over an Azure Identity </Note>
 To enable Role-Based Access Control (RBAC) for Azure AI Search, follow these steps:
 
 1. In the Azure Portal, navigate to your **Azure AI Search** service.

docs/cookbooks/integrations/healthcare-google-adk.mdx

@@ -30,7 +30,7 @@ pip install google-adk mem0ai python-dotenv
 
 ## Code Breakdown
 
-Let's get started and understand the different components required in building a healthcare assistant powered by memory
+Let's get started and understand the different components required in building a healthcare assistant powered by memory.
 
 ```python
 # Import dependencies

docs/integrations/llama-index.mdx

@@ -45,7 +45,7 @@ memory_from_client = Mem0Memory.from_client(
 )
 ```
 
-Context is used to identify the user, agent or the conversation in the Mem0. It is required to be passed in the at least one of the fields in the `Mem0Memory` constructor. It can be any of the following:
+Context is used to identify the user, agent or the conversation in the Mem0. It is required to be passed in at least one of the fields in the `Mem0Memory` constructor. It can be any of the following:
 
 ```python
 context = {

mem0/vector_stores/valkey.py

@@ -299,13 +299,8 @@ class ValkeyDB(VectorStoreBase):
                 # Create the key for the hash
                 key = f"{self.prefix}:{id}"
 
-                # Check for required fields and provide defaults if missing
-                if "data" not in payload:
-                    # Silently use default value for missing 'data' field
-                    pass
-
-                # Ensure created_at is present
-                if "created_at" not in payload:
+                # Default created_at when missing or None to current time
+                if not payload.get("created_at"):
                     payload["created_at"] = datetime.now(pytz.timezone(self.timezone)).isoformat()
 
                 # Prepare the hash data
@@ -499,13 +494,8 @@ class ValkeyDB(VectorStoreBase):
         try:
             key = f"{self.prefix}:{vector_id}"
 
-            # Check for required fields and provide defaults if missing
-            if "data" not in payload:
-                # Silently use default value for missing 'data' field
-                pass
-
-            # Ensure created_at is present
-            if "created_at" not in payload:
+            # Default created_at when missing or None to current time
+            if not payload.get("created_at"):
                 payload["created_at"] = datetime.now(pytz.timezone(self.timezone)).isoformat()
 
             # Prepare the hash data
@@ -521,7 +511,7 @@ class ValkeyDB(VectorStoreBase):
                 hash_data["embedding"] = np.array(vector, dtype=np.float32).tobytes()
 
             # Add updated_at if available
-            if "updated_at" in payload:
+            if payload.get("updated_at"):
                 hash_data["updated_at"] = int(datetime.fromisoformat(payload["updated_at"]).timestamp())
 
             # Add optional fields

tests/vector_stores/test_valkey.py

@@ -158,6 +158,32 @@ def test_insert_handles_missing_created_at(valkey_db, mock_valkey_client):
     assert "created_at" in kwargs["mapping"]  # Should be added automatically
 
 
+def test_insert_and_update_with_none_timestamps(valkey_db, mock_valkey_client):
+    """Regression: a None timestamp must not crash insert() or update().
+
+    A None created_at falls back to now and a None updated_at is skipped, so
+    neither reaches fromisoformat() which only accepts a str.
+    """
+    vector = np.random.rand(1536).tolist()
+
+    valkey_db.insert(
+        vectors=[vector],
+        payloads=[{"hash": "h", "data": "d", "created_at": None, "updated_at": None}],
+        ids=["id1"],
+    )
+    _, insert_kwargs = mock_valkey_client.hset.call_args
+    assert isinstance(insert_kwargs["mapping"]["created_at"], int)
+
+    valkey_db.update(
+        vector_id="id1",
+        vector=vector,
+        payload={"hash": "h", "data": "d", "created_at": None, "updated_at": None},
+    )
+    _, update_kwargs = mock_valkey_client.hset.call_args
+    assert isinstance(update_kwargs["mapping"]["created_at"], int)
+    assert "updated_at" not in update_kwargs["mapping"]
+
+
 def test_delete(valkey_db, mock_valkey_client):
     """Test deleting a vector."""
     # Call delete