I am confused by the benchmarks you provided.

(1) The "baseline" comparison was to PyPDF + Naive RAG. For the LlamaParse evaluation, you appear to have used a different RAG pipeline, called "recursive retrieval." Why not use the same pipeline to demonstrate the improvement from LlamaParse? Can you share the code to your evaluation for LlamaParse?

(2) I ran the benchmark for the PyPDF + Naive RAG solution, directly copying the code on the linked LlamaIndex repo [1]

I got very different numbers: mean_correctness_score 3.941 mean_relevancy_score 0.826 mean_faithfulness_score 0.980

You reported: mean_correctness_score 3.874 mean_relevancy_score 0.844 mean_faithfulness_score 0.667

Notably, the faithfulness score I measured for the baseline solution was actually higher than that reported for your proprietary LlamaParse based solution.

[1] https://github.com/run-llama/llama-hub/tree/main/llama_hub/l...