# Iterative Query Rewriting

India's Punjab Vidhan Sabha records from 1950 to 2024 survive as roughly 200,000 scanned pages mixing Hindi, Punjabi and English, where broken layouts and OCR errors propagate into retrieval and question answering. The system couples layout segmentation and multi-script OCR with an agentic retrieval-augmented generation pipeline, in which an evaluator model judges whether retrieved evidence suffices and rewrites the query when it does not. On a stratified benchmark of 1,000 pages and 200 gold-annotated question-answer pairs, the optimized pipeline reaches MRR@1 of 0.810 and Hit@3 of 0.925; raising the retrieval-rewrite iteration cap from 1 to 3 lifts answer F1 from about 0.18 to about 0.35.

India's Punjab Vidhan Sabha records from 1950 to 2024 survive as roughly 200,000 scanned pages mixing Hindi, Punjabi and English, where broken layouts and OCR errors propagate into retrieval and question answering. The system couples layout segmentation and multi-script OCR with an agentic retrieval-augmented generation pipeline, in which an evaluator model judges whether retrieved evidence suffices and rewrites the query when it does not. On a stratified benchmark of 1,000 pages and 200 gold-annotated question-answer pairs, the optimized pipeline reaches MRR@1 of 0.810 and Hit@3 of 0.925; raising the retrieval-rewrite iteration cap from 1 to 3 lifts answer F1 from about 0.18 to about 0.35.