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Using Llamafiles for Embeddings in Local RAG Applications
xt embedding model is the lynchpin of retrieval-augmented generation (RAG). Given the computational cost of indexing large datasets in a vector store, we think llamafile is a great option for scaleable RAG on local hardware, especially given llamafile's ongoing performance optimizations.
To make local RAG easier, we found some of the best embedding models with respect to performance on RAG-relevant tasks and released them as llamafiles. First, we filtered the results to include only "RAG-relevant" MTEB task categories that test embeddings in a scenario similar to how they might be used in a RAG application, where you need to retrieve text snippets related to a query from a vector store. What they meant was, while this leaderboard is a great way to see embedding quality in general, it doesn't necessarily make it obvious which model is best for your specific application.
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