Hello,While experimenting with personal, local RAG app setups, I kept having to (re)generate embeddings and really wanted precomputed embedding datasets that I could quickly pull and use in various environments.I built a library to test out the idea: lance-bundle lets you package precomputed embedding vectors alongside the actual embedding model so that everything can be loaded from a single file for querying against the vectors; initial version uses LanceDB + ONNX for low dependency footprint and fast cold start to vector queries.https://github.com/cloudkj/lance-bundleAs part of this, a few datasets that might be of interest to this audience have been precomputed as embedding vectors and hosted on a Hugging Face dataset hub and can be directly loaded and queried against:https://huggingface.co/lance-bundle/datasetsWith these datasets, you can simply load directly and run semantic queries to retrieve the nearest documents/embeddings: from lance_bundle import load_dataset bundle = load_dataset( lance-bundle/berkshire-hathaway-letters ) bundle.search( What does Warren Buffett think of passive index funds? ) Looking to share to see if anyone actually finds it useful, and to gather feedback on whether it makes sense for the local-first AI enthusiasts. Let me know what you think!