Okay I just checked/tried this out with my own use case at work and it's insane.
Ingesting PDFs and why Gemini 2.0 changes everything
191–200 of 456 posts
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#192Curious to see how well this works on technical/mechanical documentation (manuals parts list etc). Has any one tried? My company Airwave had to jump through all sorts of hoops to get accurate information for our use case: getting accurate info to the technicians in the field.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#193How is it for image recognition/classification? OCR can be a huge chunk of the image classification pipeline. Presumably, it works just as well in this domain?
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#194I work in fintech and we replaced an OCR vendor with Gemini at work for ingesting some PDFs. After trial and error with different models Gemini won because it was so darn easy to use and it worked with minimal effort. I think one shouldn't underestimate that multi-modal, large context window model in terms of ease-of-use. Ironically this vendor is the best known and most successful vendor for OCR'ing this specific ty…
> After trial and error with different models As a mere occasional customer I've been scanning 4 to 5 pages of the same document layout every week in gemini for half a year, and every single week the results were slightly different. To note the docs are bilingual so it could affect the results, but what stroke me is the lack of consistency, and even with the same model, running it two or three times in a row gives di…
At temperature zero, if you're using the same API/model, this really should not be the case. None of the big players update their APIs without some name / version change.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#195We've previously tried Sonnet in our PDF extraction pipelines. It was very, very accurate, gpt-4o did not come close. Its more expensive, however.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#196Earlier quoted context omitted.
This is a big aha moment for me. If Gemini can do semantic chunking at the same time as extraction, all for so cheap and with nearly perfect accuracy, and without brittle prompting incantation magic, this is huge.
If I used Gemini 2.0 for extraction and chunking to feed into a RAG that I maintain on my local network, then what sort of locally-hosted LLM would I need to gain meaningful insights from my knowledge base? Would a 13B parameter model be sufficient?
Do you get meaningful insights with current RAG solutions?
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#197I wish I could do this locally. I don't feel comfortable uploading all of my private documents to Google.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#198I work in fintech and we replaced an OCR vendor with Gemini at work for ingesting some PDFs. After trial and error with different models Gemini won because it was so darn easy to use and it worked with minimal effort. I think one shouldn't underestimate that multi-modal, large context window model in terms of ease-of-use. Ironically this vendor is the best known and most successful vendor for OCR'ing this specific ty…
This is spot on, any legacy vendor focusing on a specific type of PDF is going to get obliterated by LLMs. The problem with using an off-the-shelf provider like this is, you get stuck with their data schema. With an LLM, you have full control over the schema meaning you can parse and extract much more unique data.
The problem then shifts from "can we extract this data from the PDF" to "how do we teach an LLM to extract the data we need, validate its performance, and deploy it with confidence into prod?"
You could improve your accuracy further by adding some chain-of-thought to your prompt btw. e.g. Make each field in your json schema have a `reasoning` field beforehand so the model can CoT how it got to its answer. If you want to take it to the next level, `citations` in our experience also improves performance (and when combined with bounding boxes, is powerful for human-in-the-loop tooling).
Disclaimer: I started an LLM doc processing infra company (https://extend.app/)
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#199Earlier quoted context omitted.
yes! we have foreign language support for better OCR on scans. Here's some more details. Docs: https://docs.cloud.llamaindex.ai/llamaparse/features/parsing... Notebook: https://github.com/run-llama/llama_parse/blob/main/examples/...
What is disable_ocr=True for? Is it for documents that already have a text layer, that you don't want to OCR again?
yeah disable OCR is for documents where you don't need to OCR a scanned image, it'll just parse out the text
it's faster if True
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#200I work in fintech and we replaced an OCR vendor with Gemini at work for ingesting some PDFs. After trial and error with different models Gemini won because it was so darn easy to use and it worked with minimal effort. I think one shouldn't underestimate that multi-modal, large context window model in terms of ease-of-use. Ironically this vendor is the best known and most successful vendor for OCR'ing this specific ty…
Is privacy a concern?