🚨 𝗩𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗮𝗿𝗲 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝗳𝗼𝗿 𝗥𝗔𝗚.
I came across a tool called PageIndex this week and honestly didn't expect to be this impressed.

𝘕𝘰 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨𝘴. 𝘕𝘰 𝘤𝘩𝘶𝘯𝘬𝘪𝘯𝘨. 𝘕𝘰 𝘷𝘦𝘤𝘵𝘰𝘳 𝘴𝘦𝘢𝘳𝘤𝘩.

We've all built RAG systems. You know the drill - split the doc, embed the chunks, find the closest vector match, hope the context is good enough.

The problem? Closest match is not the same as the right answer.

𝗣𝗮𝗴𝗲𝗜𝗻𝗱𝗲𝘅 𝗳𝗹𝗶𝗽𝘀 𝘁𝗵𝗮𝘁 𝗲𝗻𝘁𝗶𝗿𝗲 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵.
Instead of chopping documents into pieces, 𝗶𝘁 𝗯𝘂𝗶𝗹𝗱𝘀 𝗮 𝘁𝗿𝗲𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, essentially an AI-native table of contents. When a question comes in, the model doesn't search. It reasons. It walks the tree, eliminates irrelevant branches, and drills into exactly the right section.

Think about how you'd use a textbook. You don't read it cover to cover. You scan the index, find the chapter, jump to the page.
That's the behavior this replicates, but for LLMs.

𝗧𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗯𝗮𝗰𝗸 𝗶𝘁 𝘂𝗽:
𝟵𝟴.𝟳% on FinanceBench, answering real questions from SEC filings and earnings reports. That's not a toy benchmark. That's one of the harder document QA evaluations out there.

Also supports PDFs, markdown, and raw page images without any OCR pipeline.

MIT licensed. Fully open source.

If you're working on document-heavy AI, this is genuinely worth exploring.

GitHub: https://github.com/VectifyAI/PageIndex

♻️ Share if someone on your team needs to see this

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