Most PDF extraction projects start with the document. You open a PDF, look at the text, write a regex, extract a value. Repeat for each field.
This works for one document type with one layout. It doesn’t scale.
Schema-first extraction inverts the order: define exactly what the output should look like before you write a single line of extraction code. The schema becomes the specification that every extraction function has to satisfy — and the tool that tells you, immediately and explicitly, when an extraction fails.
Intelligent Document Processing (IDP) is a category of software that extracts structured data from unstructured documents — automatically, reliably, and at scale.
The document arrives as a PDF, an image, or a scan. IDP reads it, identifies what matters, and outputs structured data: fields, values, tables — in the format your system expects. No manual entry. No copy-paste.
Every document automation project starts the same way. You pick a tool, write some code, test it on a handful of documents — and it works. Fields are extracted, outputs look right. You ship it.
Then the edge cases arrive.
IndexFlatIP works for small corpora. For production with 100K+ vectors, you need smarter indexes. Here’s how to choose and implement them.
FAISS Index Types Overview # Index Corpus Size Memory Accuracy Build Time IndexFlatIP < 50K High Exact Fast IndexIVFFlat 50K - 1M Medium ~95-99% Medium IndexHNSWFlat 50K - 10M High ~95-99% Slow IndexIVFPQ 1M+ Low ~90-95% Slow IndexFlatIP (Baseline) # Exact search, no training required. Use for prototypes and small corpora.
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