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MemoRAG is an innovative RAG framework built on top of a highly efficient, super-long memory model. Unlike standard RAG, which primarily handles queries with explicit information needs, MemoRAG leverages its memory model to achieve a global understanding of the entire database. By recalling query-specific clues from memory, MemoRAG enhances evidence retrieval, resulting in more accurate and contextually rich response generation.
MemoRAG is currently under active development, with resources and prototypes continuously being published at this repository.
Note: The recent goals of MemoRAG are to achieve light-weight optimization through engineering improvements and to enhance its memory capabilities, enabling it to adapt to a wider range of applications and support longer context (e.g., more than one million tokens).
In this notebook, we run the complete MemoRAG pipeline (Memory Model + Retriever + Generation Model) on a single T4 GPU with 15GiB of memory provided by Google Colab. Despite the limited resources, MemoRAG can process half of the content from the example book (~68K tokens) and perform all of its functions.
