> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memobase.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Retrieving the Memory Prompt

Memobase automatically extracts and structures various types of memories from user interactions, including:

* **User Profile**: Key-value attributes describing the user (e.g., name, location, preferences).
* **User Events**: Significant occurrences and interactions from the user's history.

This collection of memories forms a user's personalized context. Memobase provides a powerful `context()` API to retrieve this information as a structured string, ready to be injected directly into your LLM prompts.

### Basic Usage

The simplest way to get a user's context is to call the `context()` method on a user object.

<CodeGroup>
  ```python Python theme={null}
  from memobase import MemoBaseClient, ChatBlob

  # Initialize client and get/create a user
  client = MemoBaseClient(api_key="your_api_key")
  user = client.get_user(client.add_user(profile={"name": "Gus"}))

  # Insert data to generate memories
  user.insert(
      ChatBlob(
          messages=[
              {"role": "user", "content": "I live in California."},
              {"role": "assistant", "content": "Nice, I've heard it's sunny there!"}
          ]
      )
  )

  # Retrieve the default context prompt
  user_context = user.context()
  print(user_context)
  ```

  ```txt Output theme={null}
  # Memory
  Unless the user has relevant queries, do not actively mention these memories in the conversation.

  ## User Background:
  - basic_info:name: Gus
  - basic_info:location: California

  ## Latest Events:
  - User mentioned living in California.
  ```
</CodeGroup>

### Context-Aware Retrieval

To make the retrieved context more relevant to the ongoing conversation, you can provide recent chat messages. Memobase will perform a semantic search to prioritize the most relevant historical events, rather than simply returning the most recent ones.

<CodeGroup>
  ```python Python theme={null}
  # Continuing from the previous example...
  recent_chats = [
      {"role": "user", "content": "What is my name?"}
  ]

  # Get context relevant to the recent chat
  relevant_context = user.context(chats=recent_chats)
  print(relevant_context)
  ```

  ```txt Output theme={null}
  # Memory
  ...
  ## User Background:
  - basic_info:name: Gus
  - basic_info:location: California

  ## Latest Events:
  - User stated their name is Gus.
  - User previously mentioned being called John.
  ```
</CodeGroup>

### Controlling Context Size

You can manage the size and cost of your prompts by limiting the token count of the retrieved context using the `max_tokens` parameter.

<CodeGroup>
  ```python Python theme={null}
  # Get a condensed context with a token limit
  compact_context = user.context(max_tokens=20)
  print(compact_context)
  ```

  ```txt Output theme={null}
  # Memory
  ...
  ## User Background:
  - basic_info:name: Gus

  ## Latest Events:
  - User mentioned living in California.
  ```
</CodeGroup>

**Note**: The `max_tokens` limit applies to the profile and event content, not the final formatted string. If you use a large custom prompt template, the final output may still exceed the limit.

### Advanced Filtering

The `context()` API offers several parameters for fine-grained control:

* `prefer_topics`, `only_topics`: Prioritize or exclusively include certain profile topics.
* `max_subtopic_size`: Limit the number of sub-topics returned per topic.
* `profile_event_ratio`: Adjust the balance between profile and event information.
* `time_range_in_days`: Filter events to a specific time window.
* `customize_context_prompt`: Provide a custom template for the final output string.

For a full list of parameters, refer to the [API Reference for `get_context`](/api-reference/prompt/get_context).