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freezed8 | 1 year ago

(disclaimer I am CEO of llamaindex, which includes LlamaParse)

Nice article! We're actively benchmarking Gemini 2.0 right now and if the results are as good as implied by this article, heck we'll adapt and improve upon it. Our goal (and in fact the reason our parser works so well) is to always use and stay on top of the latest SOTA models and tech :) - we blend LLM/VLM tech with best-in-class heuristic techniques.

Some quick notes: 1. I'm glad that LlamaParse is mentioned in the article, but it's not mentioned in the performance benchmarks. I'm pretty confident that our most accurate modes are at the top of the table benchmark - our stuff is pretty good.

2. There's a long tail of issues beyond just tables - this includes fonts, headers/footers, ability to recognize charts/images/form fields, and as other posters said, the ability to have fine-grained bounding boxes on the source elements. We've optimized our parser to tackle all of these modes, and we need proper benchmarks for that.

3. DIY'ing your own pipeline to run a VLM at scale to parse docs is surprisingly challenging. You need to orchestrate a robust system that can screenshot a bunch of pages at the right resolution (which can be quite slow), tune the prompts, and make sure you're obeying rate limits + can retry on failure.

discuss

order

M4v3R|1 year ago

The very first (and probably hand-picked & checked) example on your website [0] suffers from the very problem people are talking about here - in "Fiscal 2024" row it contains an error for CEO CAP column. On the image it says "$234.1" but the parsed result says "$234.4". A small error, but error nonetheless. I wonder if we can ever fix these kind of errors with LLM parsing.

[0] https://www.llamaindex.ai/llamaparse

dilDDoS|1 year ago

Looks like this was fixed, the parsed result says "$234.1" on my end. I wonder if the error was fixed manually or with another round of LLM parsing?

heidarb|1 year ago

I'm a happy customer. I wrote a ruby client for your API and have been parsing thousands of different types of PDFs through it with great results. I tested almost everything out there at the time and I couldn't find anything that came close to being as good as llamaparse.

BenGosub|1 year ago

Indeed, this is also my experience. I have tried a lot of things and where quality is more important than quantity, I doubt there are many tools that can come close to Llamaparse.

rendaw|1 year ago

All your examples are exquisitely clean digital renders of digital documents. How does it fare with real scans (noise, folds) or photos? Receipts?

Or is there a use case for digital non-text pdfs? Are people really generating image and not text-based PDFs? Or is the primary use case extracting structure, rather than text?

rahimnathwani|1 year ago

Hi Jerry,

How well does llamaparse work on foreign-language documents?

I have pipeline for Arabic-language docs using Azure for OCR and GPT-4o-mini to extract structured information. Would it be worth trying llamaparse to replace part of the pipeline or the whole thing?