Efficient combination of BM25 algorithm and in-memory indexing
DiffMem uses in-memory BM25 indexing as its core technology for fast retrieval. This design achieves two important goals: fast response and interpretive output.BM25, as a classical information retrieval algorithm, is particularly well suited for dealing with text relevance ranking, and its implementation in DiffMem can achieve millisecond query speeds.
Unlike traditional vector databases, this retrieval method of DiffMem does not rely on vector embedding of deep learning models, but is based on keyword matching and statistical correlation. This approach brings three significant advantages: first, low computational overhead without GPU resources; second, the results are highly interpretable, so developers can intuitively understand the matching logic; and third, it is more suitable for dealing with frequently updated content and avoids the re-indexing overhead of vector databases on dynamic data.
By default, the system only indexes the "current state" of the memory, which not only improves the retrieval efficiency, but also optimizes the efficiency of token usage in large language models.
This answer comes from the articleDiffMem: a Git-based versioned memory repository for AI intelligencesThe
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