Rank Fusion for improved Code Context in Tabby | Tabby AI coding assistant

Introduction​Tabby has made significant advancements in its code context understanding with the introduction of a semantic relevance score (via vector embedding) and rank fusion in version 0.12. These enhancements have transformed the way Tabby ranks source code context, resulting in more accurate context for feeding into LLM.From BM25 to Rank Fusion​Tabby's initial approach to ranking involved the use of the BM25 algorithm, as described in Repository context for LLM assisted code completion. This algorithm indexed source code in chunks, which served as the basis for code completion and Q&A. In the latest release, Tabby has augmented this approach with a semantic relevance score calculated from embedding vector distances. This dual scoring system necessitated the implementation of a rank fusion technique to effectively combine these disparate ranks.The Mechanics of Reciprocal Rank Fusion​The RRF method adopted by Tabby is a well-established technique in information retrieval. It merges multiple rank lists to produce a single, more accurate ranking. In Tabby, the RRF is applied as follows:score = 0.0

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