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Benchmark study on LanceDB, an embedded vector DB, for full-text search and vector search
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https://github.com/kagisearch/vectordb/blob/453bb658bb710838...
Looks like it uses one of these, depending on your settings:
Fast model: google/universal-sentence-encoder/4
Multilingual model: universal-sentence-encoder-multilingual-large/3
Normal model (Alternative): BAAI/bge-small-en-v1.5
Best model: BAAI/bge-base-en-v1.5
I've seen a number of projects come over the last couple years. I'm the author of txtai (https://github.com/neuml/txtai) which I started in 2020. How you approach performance is the key point.
You can write performant code in any language. For example, for standard keyword search, I wrote a component to make sparse/keyword search just as efficient as Apache Lucene in Python. https://neuml.hashnode.dev/building-an-efficient-sparse-keyw....
I thought the API here was quite neat. It's fairly simple to implement a lancedb backend for it instead of sklearn/faiss/mrpt as the source code is really simple.
This repo is basically just a nice api and the needed chunking and batching logic. Using lancedb, you'd still have to write that, as exemplified here: https://github.com/prrao87/lancedb-study/blob/main/lancedb/i...
What about models besides GPT? Most of the popular vector encoding models aren't using this architecture.
If you really didn't want PyTorch/Transformers, you could consider exporting your models to ONNX (https://github.com/microsoft/onnxruntime).
https://github.com/wallabag/wallabag
No one has mentioned wallabag yet, so wanted to. Been working well for me - has apps and extensions. If youβre not excited to self-host - https://www.wallabag.it/en has been flawless with the exorbitant price ofβ¦ 11 euro a year.