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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?
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> 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.
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This sounds extremely like my old tax accounting job. OCR existed and "worked" but it was faster to just enter the numbers manually than fix all the errors. Also, the real solution to the problem should have been for the IRS to just pre-fill tax returns with all the accounting data that they obviously already have. But that would require the government to care.
They finally made filing free. So, maybe this century?
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How do today’s LLM’s like Gemini compare with the Document Understanding services google/aws/azure have offered for a few years, particularly when dealing with known forms? I think Google’s is Document AI.
member of the gemini team here -- personally, i'd recommend directly using gemini vs the document understanding services for OCR & general docs understanding tasks. From our internal evals gemini is now stronger than these solutions and is only going to get much better (higher precision, lower hallucination rates) from here.
This sounds amazing & I'd love your input on our specific use case.
joelatoutboundin.com
Finally, I must point out that statements in the vein of "Why [product] 2.0 Changes Everything" are more often than not a load of humbug.
I 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…
I 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…
The struggle which almost every ocr usecase faces is with handwritten documents(doctor prescriptions with bad handwriting) With gemini 1.5 flash we've had ~75-80% percent accuracy (based on random sampling by pharmacists). we're planning to improve this further by fine-tuning gemini models with medical data.
What could be other alternative services/models for accurate handwriting ocr?
This is using exactly the wrong tools at every stage of the OCR pipeline, and the cost is astronomical as a result. You don't use multimodal models to extract a wall of text from an image. They hallucinate constantly the second you get past perfect 100% high-fidelity images. You use an object detection model trained on documents to find the bounding boxes of each document section as _images_; each bounding box comes…