diff --git a/docs/components/embedders/models/together.mdx b/docs/components/embedders/models/together.mdx
index 75f44227e..a71c1764a 100644
--- a/docs/components/embedders/models/together.mdx
+++ b/docs/components/embedders/models/together.mdx
@@ -11,7 +11,7 @@ To use Together embedding models, set the `TOGETHER_API_KEY` environment variabl
The `embedding_model_dims` parameter for `vector_store` should be set to `1024` for Together embedder.
-**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.
diff --git a/docs/components/vectordbs/dbs/azure.mdx b/docs/components/vectordbs/dbs/azure.mdx
index 4c0554cde..6a43d8f14 100644
--- a/docs/components/vectordbs/dbs/azure.mdx
+++ b/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`).
- If an API is provided, it will be used for authentication over an Azure Identity
+ If an API key is provided, it will be used for authentication over an Azure Identity
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.
diff --git a/docs/cookbooks/integrations/healthcare-google-adk.mdx b/docs/cookbooks/integrations/healthcare-google-adk.mdx
index dcc31295b..2ecf85ab4 100644
--- a/docs/cookbooks/integrations/healthcare-google-adk.mdx
+++ b/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
diff --git a/docs/integrations/llama-index.mdx b/docs/integrations/llama-index.mdx
index f61d50116..141a290d3 100644
--- a/docs/integrations/llama-index.mdx
+++ b/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 = {
diff --git a/mem0/vector_stores/valkey.py b/mem0/vector_stores/valkey.py
index 0688a1288..189285539 100644
--- a/mem0/vector_stores/valkey.py
+++ b/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
diff --git a/tests/vector_stores/test_valkey.py b/tests/vector_stores/test_valkey.py
index 769f511d4..b42a54848 100644
--- a/tests/vector_stores/test_valkey.py
+++ b/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