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memoryLocal RAG

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💡 Why Local RAG

Local RAG enhances app with an intelligent local memory layer, enabling personalized and privacy-preserving AI interactions. Local RAG remembers user preferences, adapts to individual needs, and continuously learns over time, making it ideal for on-device AI assistants on iOS and autonomous agents.

link: https://fedml-inc.sg.larksuite.com/wiki/HtErwbkeBi2X9GkY5yUukpbCsz1arrow-up-right

Local RAG offers a powerful memory management for application with the following key advantages:

  • Simplified Development: Easily integrate intelligent memory into your AI agents with minimal code, letting you focus on feature innovation.

  • Privacy-Preserving: Data is stored and processed locally, ensuring maximum user privacy and security without cloud dependencies.

  • Personalization: Continuously adapts to user preferences, enabling more relevant and natural AI interactions.

  • Offline & Weak Network Friendly: Delivers smooth and reliable AI experiences even in offline or unstable network environments.

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🔍 Use Cases: Historical chat records, typing history, and personalized information enhance LLM-based question answering.

Challenges

  1. The ecosystem for RAG libraries on the device is underdeveloped.

  2. Due to limited resources on devices, efficient memory and storage management is essential.

Solution

link: https://fedml-inc.sg.larksuite.com/wiki/HtErwbkeBi2X9GkY5yUukpbCsz1arrow-up-right

  1. We have implemented fundamental RAG-related(indexing/retrieval) operators on iOS, enabling efficient expansion of customizable indexing pipelines.

  2. We use SQLite and a self-developed engine to efficiently manage on-device memory and storage, ensuring optimal performance.

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