Playground for comparing embedding models on Wikipedia+book retrieval

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Introducing embeds.ai: an embedding playground to compare how embedding models work on a real world use case (retrieval augmented generation for Wikipedia articles + Elad Gil's High growth handbook)A few weeks ago, Shreyan and I were looking for an embedding model to use for RAG. We eventually came across the MTEB leaderboard, but we struggled to understand the benchmark scores.We wanted a tool to test various embedding models with example queries on real-world datasets. After unsuccessfully looking for such a “playground”, we decided to just build one ourselves!We embedded HuggingFace’s Simple Wikipedia dataset using @OpenAI, @Cohere, and 2 open-source models via @Baseten. We then stored th

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Website: www.embeds.ai