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Documentation Index

Fetch the complete documentation index at: https://docs.memobase.io/llms.txt

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While Memobase is designed to provide a comprehensive, global context for each user with very low latency, there are times when you need to search for specific information within a user’s profile. Memobase provides a powerful, context-aware search method to filter out irrelevant memories and retrieve only what’s needed for the current turn of the conversation.

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How Profile Search Works

Unlike simple keyword or semantic matching, Memobase’s profile search uses the LLM to perform a feature-based analysis. It reasons about what aspects of a user’s profile are relevant to their latest query. For example, if a user asks, “Can you recommend a good restaurant?”, Memobase doesn’t just search for the term “restaurant.” Instead, it identifies key features that would help answer the question, such as:
  • basic_info::location: To determine the city for the restaurant search.
  • interests::food: To understand the user’s cuisine preferences.
  • health::allergies: To know what ingredients to avoid.
This intelligent, feature-based approach results in a much more relevant and helpful set of memories than traditional search methods. 
# Assume 'user' is an initialized MemoBaseUser object
contextual_profile = user.profile(
    chats=[{"role": "user", "content": "Find some restaurants for me"}],
    need_json=True
)

print(contextual_profile)
See the API Reference for more details.

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Important Considerations

  • Latency: Profile search is a powerful but computationally intensive operation. It can add 2-5 seconds to your response time, depending on the size of the user’s profile. Use it judiciously.
  • Cost: Each profile search consumes Memobase tokens (roughly 100-1000 tokens per call), which will affect your usage costs.