updated readme and progress_report

added rag-only or rag + kg-graph option for LLM response

added comments for better code readability

tracked progres report and data folder also and added correct evaluation results, Add graph-seeded multi-hop QA generator and --qa-file eval; document in README

Add 3-way eval (no-RAG/RAG/RAG+graph) with per-call graph toggle and --sample flag

Add --sample flag for random-subset eval and document GraphRAG in README

added knowledge graphs and fix: correct BERTScore unpacking to use recall not precision (bert_score returns P,R,F1); relabel stale precision values in eval_results.json

ignored iipp report

added new webpage with feature of generating report using LLM + RAG

Document Hugging Face Spaces deployment in the README

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improved comment in config for LLM_MODEL variable

ignored Progress Report in gitignore

Added a --merge flag to run_eval that re-scores a model's cached predictions across all books into one row (named by book, no LLM calls), erroring if the model hasn't fully evaluated every QA-bearing book — and documented it in the README.

Consolidated all per-(book,model) eval files into one eval_predictions.jsonl and one eval_results.json (tagged internally by model/provider/book), migrated existing data in, and updated the readme

switched BERTScore/ROUGE-L to recall (paper Eq. 4/7), updated the README, and back-filled the hauschild tag onto 250 untagged QA pairs (backup at qa_pairs.jsonl.bak).

Add --books flag to generate_qa for per-book QA generation; fix evaluation docs in README

Add --books flag to generate_qa for restricting QA generation to specific book ids

updated comments for evaluation

Multi-book RAG support: config.BOOKS registry ingesting multiple PDFs into one Chroma collection with book-aware citations, robust chapter tagging for TOC-less/junk-TOC PDFs, an incremental --add flag to index a new PDF without a full re-embed, and updated README (multi-book docs + Limitations).

improved code to remove balanced generation from each pair and replaced with random choosing chunks from the whole book and generating qa pairs from those chunk with parameter --pairs-per-chunk and --num-chunks

improved code to add more qa pairs on generation instead of replacing old one

improved comments - 1 generate_qa.py

Added Content in Readme

improved comments of app - 1

improved comments of phase_b_query - 1

improvement of comments - 2 for phase_a_ingestion

improved comments of phase_a_ingestion

added comment for each code files for better understanding

completed evaluation by generating 240 QA pairs

completed evaluation by generating 5 QA pairs

created complete streamlit app and it is working now next step is evaluation

completed phase b and created streamlit UI for anthropic only LLM

completed extract->clean->chunk

added basic plan made by claude

Added basic readme