add chinese readme

README.md

@@ -1,11 +1,11 @@
 # Baguetter
+- [简体中文](README_CN.md)
+
 
 Baguetter is a flexible, efficient, and hackable search engine library implemented in Python. It's designed for quickly benchmarking, implementing, and testing new search methods. Baguetter supports sparse (traditional), dense (semantic), and hybrid retrieval methods.
 
 **Note:** Baguetter is not built for production use-cases or scale. For such use-cases, please check out other search engine projects.
 
-Paper: https://arxiv.org/abs/2408.06643
-
 ## Features
 
 - Sparse retrieval using BM25 and BMX algorithms
@@ -99,22 +99,12 @@ Baguetter builds upon the work of several open-source projects:
 2. [bm25s](https://github.com/xhluca/bm25s) by [xhluca](https://github.com/xhluca):
    Our BM25 implementation is based on this project, which provides an efficient and effective implementation of the BM25 algorithm with different scoring functions.
 
-3. [USearch](https://github.com/unum-cloud/usearch) by [unum-cloud](https://github.com/unum-cloud) for dense retrieval.
+3. [USearch](https://github.com/unum-cloud/usearch) by [unum-cloud](https://github.com/unum-cloud) and [Faiss](https://github.com/facebookresearch/faiss) by [facebook research](https://github.com/facebookresearch) for dense retrival.
 
 4. [ranx](https://github.com/AmenRa/ranx) by [AmenRa](https://github.com/AmenRa) for evaluation.
 
 Please check out the respective repositories and show some appreciation to the authors.
 
-## Citing
+## Citing & Authors
 
-```
-@article{li2024bmx,
-      title={BMX: Entropy-weighted Similarity and Semantic-enhanced Lexical Search},
-      author={Xianming Li and Julius Lipp and Aamir Shakir and Rui Huang and Jing Li},
-      year={2024},
-      eprint={2408.06643},
-      archivePrefix={arXiv},
-      primaryClass={cs.IR},
-      url={https://arxiv.org/abs/2408.06643},
-}
-```
+...

README_CN.md

@@ -0,0 +1,109 @@
+# Baguetter
+- [English](README.md)
+
+Baguetter是一个Python实现的灵活、高效、可定制的搜索引擎库。它旨在快速对新搜索方法进行基准测试(Benchmark)、实现和测试。Baguetter支持稀疏(Sparse Retrieval)、密集(Dense Retrieval)和混合检索(Hybrid Retrieval)方法。
+
+**注意：** Baguetter并非为生产环境或大规模使用而设计。如需此类使用场景，请查看其他搜索引擎项目。
+
+## 功能特点
+
+- 基于BM25和BMX算法的稀疏检索
+- 基于向量的密集检索
+- 融合稀疏和密集方法的混合检索
+- 自定义文本预处理
+- 支持高性能的并发检索
+- 提供丰富的基准测试评估工具
+- 集成HuggingFace数据集和模型API
+- 面向扩展的接口，便于快速实现新检索方法
+
+## 安装
+
+```bash
+pip install baguetter
+```
+
+## 快速入门
+
+```python
+from baguetter.indices import BMXSparseIndex
+
+# 创建索引
+idx = BMXSparseIndex()
+
+# 添加文档
+docs = [
+  "我们都爱法棍和奶酪",
+  "法棍是一种很棒的面包",
+  "奶酪是很好的蛋白质来源",
+  "法棍是很好的碳水化合物来源",
+]
+doc_ids = ["1", "2", "3", "4"]
+
+idx.add_many(doc_ids, docs, show_progress=True)
+
+# 搜索
+results = idx.search("敏捷的狐狸")
+print(results)
+
+# 批量搜索
+results = idx.search_many(["敏捷的狐狸", "法棍很棒"])
+print(results)
+```
+
+## 评估
+
+Baguetter包含用于在标准基准上评估搜索性能的工具：
+
+```python
+from baguetter.evaluation import datasets, evaluate_retrievers
+from baguetter.indices import BM25SparseIndex, BMXSparseIndex
+
+results = evaluate_retrievers(datasets.mteb_datasets_small, {"bm25": BM25SparseIndex, "bmx": BMXSparseIndex})
+results.save("eval_results")
+```
+
+## 文档
+
+有关更详细的使用说明和API文档，请参阅[完整文档](https://github.com/mixedbread-ai/baguetter/docs)。
+
+## 贡献
+
+欢迎贡献代码！我们使用 GitHub Pull Request 工作流。您可以先打开一个 issue，然后创建 PR，或者在打开 PR 时包含详细的提交信息。
+
+要开始，请先克隆（或 fork）该仓库。我们建议在Python虚拟环境中进行开发。
+
+```sh
+python -m pip install -e ".[dev]"
+
+pre-commit install
+```
+
+要测试您的更改，运行：
+
+```sh
+pytest
+```
+
+## 许可证
+
+本项目采用 Apache 2.0 许可证 - 详情请参见 [LICENSE](LICENSE) 文件。
+
+## 致谢
+
+Baguetter 基于几个开源项目的工作：
+
+1. [retriv](https://github.com/AmenRa/retriv) by [AmenRa](https://github.com/AmenRa):
+   Baguetter是retriv的一个分支项目。
+
+2. [bm25s](https://github.com/xhluca/bm25s) by [xhluca](https://github.com/xhluca):
+   我们的 BM25 实现基于该项目，它提供了 BM25 算法的高效实现和不同的评分函数。
+
+3. [USearch](https://github.com/unum-cloud/usearch) by [unum-cloud](https://github.com/unum-cloud) 和 [Faiss](https://github.com/facebookresearch/faiss) by [facebook research](https://github.com/facebookresearch) 用于密集检索。
+
+4. [ranx](https://github.com/AmenRa/ranx) by [AmenRa](https://github.com/AmenRa) 用于评估。
+
+请访问相关仓库并向作者表示感谢。
+
+## 引用与作者
+
+...