Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+# ExtAgents
+
+<p align="center">
+  <a href="https://github.com/THUNLP-MT/ExtAgents">🌐 Github</a> |
+  <a href="https://arxiv.org/abs/2505.21471">📖 Paper</a> |
+  <a href="https://huggingface.co/datasets/zhennan1/ExtAgents">🤗 Data</a>
+</p>
+
+## Introduction
+
+ExtAgents is a framework for scaling external knowledge input beyond the context length of LLMs via multi-agent collaboration.
+
+## Setup
+
+```bash
+conda create -n extagents python=3.10 -y
+conda activate extagents
+pip install -r requirements.txt
+```
+
+## Data
+
+You can download the data with the script:
+
+```bash
+bash scripts/download_data.sh
+```
+
+Or you can download the data manually from one of the following links:
+
+- [Google Drive](https://drive.google.com/drive/folders/1FQSojqgF1VdumXxSh1UbIoE6lQ2E_xJn?usp=sharing)
+- [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/b8aab568cf5c4785b457/)
+
+The data should be organized as follows:
+
+```bash
+./
+└── data/
+    ├── sampled_hotpot_questions.json
+    ├── rag_1000k.jsonl
+    ├── longbook_qa_eng.jsonl
+    └── longbook_qa_chn.jsonl
+```
+
+## Usage
+
+### Generation
+
+We currently support three tasks: RAG, En.QA, Zh.QA.
+
+The RAG task is a question answering task, where the input is a question and a context. The question and answer are sampled from the [HotpotQA](https://github.com/hotpotqa/hotpot). The context is a long text, which is the concatenation of documents retrieved from Wikipedia using BM25 embedding. We use [KILT knowledge source](http://dl.fbaipublicfiles.com/KILT/kilt_knowledgesource.json) as our knowledge source. It is based on the [2019/08/01 Wikipedia dump](http://dl.fbaipublicfiles.com/BLINK/enwiki-pages-articles.xml.bz2). We have provided the context in the `data/rag_1000k.jsonl` file.
+
+The En.QA and Zh.QA tasks are question answering tasks, where the input is a question and a context. The question, answer and context are from the [InfiniteBench](https://github.com/OpenBMB/InfiniteBench).
+
+Here is an example command to generate predictions for RAG task:
+
+```bash
+python main.py \
+    --task rag \
+    --output_dir results_rag \
+    --chunk_length 8000 \
+    --input_length 128000 \
+    --api_url "YOUR_API_URL" \
+    --api_key "YOUR_API_KEY" \
+    --model "gpt-4o-mini-2024-07-18" \
+    --num_workers 8 \
+    > rag.log
+```
+
+The generated predictions will be saved in the `results_rag` directory.
+
+- `--task`: Task, can be `rag`, `en`, `zh`.
+- `--output_dir`: Directory to save the generated predictions.
+- `--chunk_length`: Chunk length.
+- `--input_length`: Input length.
+- `--model`: Model to use, default is `gpt-4o-mini-2024-07-18`.
+- `--api_url`: Your API URL, default is os.getenv("OPENAI_BASE_URL").
+- `--api_key`: Your API Key, default is os.getenv("OPENAI_API_KEY").
+- `--num_workers`: Number of workers, each worker will process one example.
+
+You can also set the environment variables `OPENAI_BASE_URL` and `OPENAI_API_KEY` to avoid typing them in the command line.
+
+```bash
+export OPENAI_BASE_URL="YOUR_API_URL"
+export OPENAI_API_KEY="YOUR_API_KEY"
+```
+
+### Evaluation
+
+We provide a script to evaluate the generated predictions. For RAG task, the evaluation is based on the [HotpotQA](https://github.com/hotpotqa/hotpot). For En.QA and Zh.QA task, the evaluation is based on the [InfiniteBench](https://github.com/OpenBMB/InfiniteBench).
+
+For RAG task:
+
+```bash
+bash scripts/eval_rag.sh /path/to/your/output_dir
+```
+
+For En.QA task: 
+
+```bash
+bash scripts/eval_en.sh /path/to/your/output_dir
+```
+
+For Zh.QA task:
+
+```bash
+bash scripts/eval_zh.sh /path/to/your/output_dir
+```
+
+## Citation
+
+If you find this project helpful, please cite it as follows:
+
+```bibtex
+@article{liu2025extagents,
+  title={Scaling External Knowledge Input Beyond The Context Length of LLMs via Multi-Agent Collaboration},
+  author={Zijun Liu and Zhennan Wan and Peng Li and Ming Yan and Ji Zhang and Fei Huang and Yang Liu},
+  year={2025}
+}
+```