# Show HN: Lance-bundle – Portable embeddings to embed once, query forever

> Source: <https://github.com/cloudkj/lance-bundle>
> Published: 2026-08-11 14:53:41+00:00

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-bundle](https://github.com/cloudkj/lance-bundle)

As 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/datasets](https://huggingface.co/lance-bundle/datasets)

With these datasets, you can simply load directly and run semantic queries to retrieve the nearest documents/embeddings:

``` python
    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!Comments URL: [https://news.ycombinator.com/item?id=49259385](https://news.ycombinator.com/item?id=49259385)

Points: 1

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