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4f0a44e6c5148b22a5a920e552f77373148795d8d35f546296e3575bc87a84f9  benchmark.py
66193c13e7f26eaefc0cc0b5d4d37a78052a6d1a97af9c07400142a31fab1696  download_inputs.py
18b0e72011656460799737b5d74226ab4b1b4de2c93839470e1eda597c7943bb  verify_results.py
79c545487c6e68b37370a998527454d847b73e77f1ee78b1d3c4752cb82aaf8b  build_manuscript.py
4a15e371ac86bee36e15ae1f417edb9734b59e94381d0181d6f96924af793a63  requirements.txt
1b032ae76d7c4b79d949b80889bb4983467ab2757ccd523e2ee75a217a2015e3  results/summary.json
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4122ac6fea9d3b19ad235e4c88f1f32a46da5b4ed58f85cf3ce089f1cd7e07b1  results/latency_raw.csv
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52bf0bdb6cc5fd86072f30dc51de0e582b87d7e6427467651bf7c3f7972b6600  results/run.log
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b06f0f67eba4d3d81138771c56583e4e0d87f5a158b150910acd17fb1fb0fd54  figures/Figure_1.pdf
533f6372cebc73d634df19f678df922176f42687d6017531b02c8d703a37c37a  manuscript/FiQA_RAG_Research_Manuscript.md
d2a9d47b4b8066541912c0e48f6d98355465e7c02c88c4fc5a089ac78c3d09ea  manuscript/ResearchSquare_Abstract.txt
192c4ae7a3fb10e3795006058df2630c61255f25fad25c077f4f4bf66226eb8c  manuscript/ResearchSquare_Submission_Notes.txt
