VectorBoard/vectorboard
Open Source Embeddings Optimisation and Eval Framework for RAG/LLM Applications. Documentations at https://docs.vectorboard.ai/introduction
This tool helps AI engineers, data scientists, and ML practitioners optimize the 'embeddings' used in Retrieval-Augmented Generation (RAG) and Large Language Model (LLM) applications. You provide your documents and a set of parameters (like chunk sizes or embedding models), and it evaluates different combinations. The output is a detailed report showing which parameter settings yield the best performance and response quality for your specific RAG pipeline.
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Use this if you are building RAG applications and need to systematically test and identify the best embedding configurations to improve the accuracy and relevance of your LLM's responses.
Not ideal if you are looking for a general-purpose LLM evaluation framework that doesn't focus specifically on embedding optimization for RAG.
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Python
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Last pushed
Oct 19, 2023
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