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Show HN: PageIndex – Vectorless RAG

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Re: Show HN: PageIndex – Vectorless RAG

#51

Post by page_index »

lewisjoe wrote: post #13

This will scale when you have a single/a small set of document(s) and want your questions answered. When you have a question and you don't know which of the million documents in your dataspace contains the answer - I'm not sure how this approach will perform. In that case we are looking at either feeding an enormously large tree as context to LLM or looping through potentially thousands of iterations between a tree &…

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Re: Show HN: PageIndex – Vectorless RAG

#53

Post by mritchie712 »

an effective "vectorless RAG" is to have an LLM write search queries against the documents. e.g. if you store your documents in postgres, allow the LLM to construct a regex string that will find relevant matches. If you were searching for “Martin Luther King Jr.”, it might write something like:

    SELECT id, body
    FROM docs
    WHERE body ~* E'(?x)                                     -- x = allow whitespace/comments
      (?:\\m(?:dr|rev(?:erend)?)\\.?\\M[\\s.]+)?             -- optional title: Dr., Rev., Reverend
      (                                                      -- name forms
        (?:\\mmartin\\M[\\s.]+(?:\\mluther\\M[\\s.]+)?\\mking\\M)  -- "Martin (Luther)? King"
      | (?:\\mm\\.?\\M[\\s.]+(?:\\ml\\.?\\M[\\s.]+)?\\mking\\M)     -- "M. (L.)? King" / "M L King"
      | (?:\\mmlk\\M)                                       -- "MLK"
      )
      (?:[\\s.,-]*\\m(?:jr|junior)\\M\\.?)*                  -- optional suffix(es): Jr, Jr., Junior
    ';

Re: Show HN: PageIndex – Vectorless RAG

#58

Post by mingtianzhang »

dmezzetti wrote: post #38

Context and prompt engineering is the most important of AI, hands down. There are plenty of lightweight retrieval options that don't require a separate vector database (I'm the author of txtai [ https://github.com/neuml/txtai ], which is one of them). It can be as simple this in Python: you pass an index operation a data generator and save the index to a local folder. Then use that for RAG.

Strongly agree, I also found txtai is super interesting! Thank you for your open-source effort!

Re: Show HN: PageIndex – Vectorless RAG

#59

Post by mingtianzhang »

Koaisu wrote: post #27

Sounds a bit like generative retrieval (e.g. this Google paper here: https://arxiv.org/abs/2202.06991 )

Yeah, they share a similar intuition. I found that the difference is that PageIndex is more of a learning-free approach, more like how a human would do retrieval?

Re: Show HN: PageIndex – Vectorless RAG

#60

Post by mingtianzhang »

jdthedisciple wrote: post #30

Looks like this should scale spectacularly poorly. Might be useful for a few hundred documents max though.

A good thing about tree representation compared to a 'list' representation is that you can search hierarchically, layer by layer, in a large tree. For example, AlphaGo performs search in a large tree. Since the scale of retrieval is smaller than that of the Go game, I guess this framework can scale very well.
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