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Looks interesting! Have you considered a proper vector database like Qdrant (https://qdrant.tech)? FAISS runs on a single machine, but if you want to scale things up, then a real database makes it a lot easier. And with a free 1GB cluster on Qdrant Cloud (https://cloud.qdrant.io), you can store quite a lot of vectors. Qdrant is also already integrated with Langchain.

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Probably not very helpful at the scale most people would run this. Even brute forcing the search on CPU gives results in a few ms on small datasets.

Using something like Weaviate, which can be started in Docker with a one-liner, will give the ability to move away or toward dense vectors by concept. While doing dot product with manual code is fairly easy, using Weaviate to do the lifting (for embeddings as well) makes things super simple.

https://github.com/FeatureBaseDB/slothbot/blob/slothbot-work...

that means you need docker running and the dependencies explode if you take this approach. I really like the tight dependency tree.

Thanks for the suggestion, but for my fun small experiment, FAISS was more than enough.

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