Source code for our paper :
ActiveRAG: Revealing the Treasures of Knowledge via Active Learning
If you find this work useful, please cite our paper and give us a shining star 🌟
git clone https://github.com/OpenMatch/ActiveRAG
pip install -r requirements.txt
We provide our request logs, so the results in the paper can be quickly reproduced:
python -m logs.eval --dataset nq --topk 5
Parameters:
dataset
: dataset name.topk
: using top-k of retrieved passages to augment.
We also provide the full request code, you can re-request for further exploration.
First, set your own api-key in agent file:
openai.api_key = 'sk-<your-api-key>'
Then, run the following script:
python -m scripts.run --dataset nq --topk 5
Analyzing log files:
python -m scripts.build --dataset nq --topk 5
Evaluate:
python -m scripts.evaluate --dataset nq --topk 5
If you have questions, suggestions, and bug reports, please send a email to us, we will try our best to help you.
xuzhipeng@stumail.neu.edu.cn