Part 7/11:
Through iterative testing, the developer reduced chunk sizes from four pages to smaller segments of about two pages, ensuring each remains within model token limits. This optimization was vital for converting lengthy legal texts into JSON outputs without data truncation.
Automating Data Processing with Python and GPT Integration
Creating Modular Scripts
The process was broken down into sequential Python scripts, each responsible for:
Reading and splitting files into smaller chunks
Deduplicating OCR errors
Generating JSON nodes using GPT-3's
text-davinci-003modelSaving outputs systematically into designated folders
This modular approach not only streamlined the workflow but also acted as in-code documentation, guiding future iterations.