6 Hard Problems Scaling Vector Search

• Multiple indices. Splitting the document corpus up into multiple indices and then routing queries based on some criteria. This means that the search is over a much smaller set of documents rather than the entire dataset. Again, it is not always useful, but it can be helpful for certain datasets. The same approach works with the LLMs themselves.


• Cu

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Why is Continuous Delivery for ML/AI hard(er)?

Since the challenge is not new and many valid solutions exist targeting traditional software projects, is there a reason to treat ML/AI systems any differently? Consider these three core challenges that are endemic in ML, AI, and data projects:

1. Development and debugging cycles are more tedious due to c

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latest research on text embeddings!

TLDR: Vector databases are NOT safe. Text embeddings can be inverted. We can do this exactly for sentence-length inputs and get very close with paragraphs...