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 🌟

ActiveRAG is an innovative RAG framework that shifts from passive knowledge acquisition to an active learning mechanism. This approach utilizes the Knowledge Construction mechanism to develop a deeper understanding of external knowledge by associating it with previously acquired or memorized knowledge. Subsequently, it designs the Cognitive Nexus mechanism to incorporate the outcomes from both chains of thought and knowledge construction, thereby calibrating the intrinsic cognition of LLMs.

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

@article{xu2024activerag,
  title={ActiveRAG: Revealing the Treasures of Knowledge via Active Learning},
  author={Xu, Zhipeng and Liu, Zhenghao and Liu, Yibin and Xiong, Chenyan and Yan, Yukun and Wang, Shuo and Yu, Shi and Liu, Zhiyuan and Yu, Ge},
  journal={arXiv preprint arXiv:2402.13547},
  year={2024}
}

If you have questions, suggestions, and bug reports, please send a email to us, we will try our best to help you.

[email protected]