I fed my own resume into the tool and asked a few questions about it. Someone also mentioned using something like this to screen resumes. This could be useful for scenarios where the relevant data is always on the 1st couple of pages or for scenarios where one must filter to only the relevant pages/images because the full packet of PDF page data or image data would exceed the GPT prompt token / character limit. Version 1.8 adds a PageNumbers compose action that allows one to input specific pages of a PDF or image packet to pass on to the text conversion & GPT prompt. Version 1.7 simplifies some expressions, & reduced the need for additional files to help explain the text conversion piece. If you are new, you can get a trial license to test things out: Depending on your license & organization, you may already have a few credits assigned to the environment. Lastly, you may need AI Builder credits for the OCR & GPT actions in the flow to work. So you should be able to process PDF, Image, and/or Word documents on the same type of set-up. Or if you just want it to output a regular text list of the data labels & data, then you could ask for that in the prompt instead of the JSON.Īlso if you are working with some Word/.docx files, there are built in OneDrive actions to convert them to. So if you wanted to enforce specific JSON key values, then you could specify that in the prompt & probably give an example of the JSON structure you want it to output. "description": "KIT COBAS HBV/HCV/HIV-1 CONTROL CE-IVD",Īnd remember you can always adjust the prompt to extract the different data you want on different documents & adjust how you want the data to be represented in the output. "description": "KIT COBAS LYS REAGENT IVD", Agency, Plot 1075 Diplomatic Drive, ABUJA NIGERIA, NIGERIA", ![]() "consignee_or_ship_to_entity": "Dr The Mission Director, U.S. That is then fed into this GPT action prompt. Then the Convert to txt loop produces this output shown in the final Compose.Īnd if we copy that output over to a text (txt) notebook, then this is what it looks like. The AI Builder action uses optical character recognition (OCR) on this invoice PDF to return each piece of text & its associated x, y coordinates. The Convert to txt loop goes through each vertical line in the PDF or image & creates a line of text to approximately match both the text & spacing between text for that line.Įach vertical line of text is then combined into a single block of text like a big txt file in the final Compose action, before it is then passed to GPT through the AI Builder Create text action. The AI Builder Recognize text action returns a JSON array of each piece of text found in the PDF or image. ![]() Does not use additional 3rd party services, maintaining better data privacy Runs in less than a minute, so it can respond in time for a Power Apps call And you can always adjust the prompt to extract the different data you want on different documents & adjust how you want the data to be represented in the output. ![]() It may be good enough to do more direct data entry on some use-cases with well formatted, clean PDFs, and in many other cases it may be best at doing a 1st pass on a file & providing a default / pre-fill value for fields before a person then checks & completes something with the data.īut it does not require training on different formats, styles, wording, etc. Seems to have a 80% or greater reliability for returning requested data fields from most PDFs. This template uses AI Builder's OCR for PDFs & Images to extract the text present in a file, replicates the file in a text (txt) format, then passes it off to a GPT prompt action for things like data extraction. Or if you were scripting this, you could dd with bs=1 up to 32k-aligned, then maybe tail -c +$misalignment lastpart/file | dd. Or maybe there's some other tool which can seek to an arbitrary byte position and then do normal-sized I/Os. dd really does make read and write system calls with that block size, so bs=1 really sucks.Ī large ibs (input block size) would save half the CPU time, since seek is in units of obs (output block size). dd can only seek to multiples of the output block size. If the offset you need to seek to isn't prime, then use a block size larger than one. Ls -l dir/file # note the size of the last partĭd if=dir/file of=p1/file conv=notrunc bs=1 seek=$((full_size - lastpart_size)) For example, if you have the first and last rar-parts: 7z x p1.rar 7-zip will extract the part of a file from a multi-part rar, and then you can stitch them back together with dd.
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