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.

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Basic Usage

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

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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. 
# 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)

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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. 
# Get a condensed context with a token limit
compact_context = user.context(max_tokens=20)
print(compact_context)
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.

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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.