# Storing & Retrieving Memory

### **Overview**

Memorylake enables storing, retrieving and managing conversational memory by associating user queries and responses with specific sessions. This allows AI-driven applications to recall past interactions and provide context-aware responses.

***

### **Method: `short_memory_create()`**

The `short_memory_create()` method stores a memory instance for a specific user. Each memory entry contains:

* **User UUID**: Identifies the user.
* **Memory Context**: Defines the scope of memory storage (e.g., chatbot session).
* **Memory Data**: Stores the user’s query, AI-generated response, timestamp, and cache expiration time.

Example Method

```python
user_uuid = "user123"
memory_context = {
    "context_entity_id": "chatbot",
    "context_id": "session1",
    "memory_id": "msg001"
}
memory = {
    "query_text": "Hello!",
    "response_text": "Hi there! How can I help you?",
    "time": "2025-02-05T12:00:00Z",
    "cache_ttl": 3600
}

response = memory_lake.short_memory_create(user_uuid, memory_context, memory)
print(response)
```

### Retrieving Memory <a href="#retrieving-memory" id="retrieving-memory"></a>

You can retrieve memory using the `short_memory_read` method. This method supports:

```python
response = memory_lake.short_memory_read(user_uuid, memory_context)
print(response)
```


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