[**🇨🇳中文**](https://github.com/shibing624/similarities/blob/main/README.md) | [**🌐English**](https://github.com/shibing624/similarities/blob/main/README_EN.md) | [**📖文档/Docs**](https://github.com/shibing624/similarities/wiki) | [**🤖模型/Models**](https://huggingface.co/shibing624)
<div align="center">
<a href="https://github.com/shibing624/similarities">
<img src="https://github.com/shibing624/similarities/blob/main/docs/logo.png" height="150" alt="Logo">
</a>
</div>
-----------------
# Similarities: Similarity Calculation and Semantic Search
[](https://badge.fury.io/py/similarities)
[](https://pepy.tech/project/similarities)
[](CONTRIBUTING.md)
[](LICENSE)
[](requirements.txt)
[](https://github.com/shibing624/similarities/issues)
[](#Contact)
**similarities**: a toolkit for similarity calculation and semantic search, supports text and image. 相似度计算、语义匹配搜索工具包。
**similarities** 实现了多种文本和图片的相似度计算、语义匹配检索算法，支持亿级数据文搜文、文搜图、图搜图，python3开发，pip安装，开箱即用。
**Guide**
- [Features](#Features)
- [Install](#install)
- [Usage](#usage)
- [Contact](#Contact)
- [Acknowledgements](#Acknowledgements)
## Features
### 文本相似度计算 + 文本搜索
- 语义匹配模型【推荐】：本项目基于text2vec实现了CoSENT模型的文本相似度计算和文本搜索
- 支持中英文、多语言多种SentenceBERT类预训练模型
- 支持 Cos Similarity/Dot Product/Hamming Distance/Euclidean Distance 等多种相似度计算方法
- 支持 SemanticSearch/Faiss/Annoy/Hnsw 等多种文本搜索算法
- 支持亿级数据高效检索
- 支持命令行文本转向量（多卡）、建索引、批量检索、启动服务
- 字面匹配模型：本项目实现了Word2Vec、BM25、RankBM25、TFIDF、SimHash、同义词词林、知网Hownet义原匹配等多种字面匹配模型
### 图像相似度计算/图文相似度计算 + 图搜图/文搜图
- CLIP(Contrastive Language-Image Pre-Training)模型：图文匹配模型，可用于图文特征（embeddings）、相似度计算、图文检索、零样本图片分类，本项目基于PyTorch实现了CLIP模型的向量表征、构建索引（基于AutoFaiss）、批量检索、后台服务（基于FastAPI）、前端展现（基于Gradio）功能
- 支持[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32)等CLIP系列模型
- 支持[OFA-Sys/chinese-clip-vit-huge-patch14](https://huggingface.co/OFA-Sys/chinese-clip-vit-huge-patch14)等Chinese-CLIP系列模型
- 支持前后端分离部署，FastAPI后端服务，Gradio前端展现
- 支持亿级数据高效检索，基于Faiss检索，支持GPU加速
- 支持图搜图、文搜图、向量搜图
- 支持图像embedding提取、文本embedding提取
- 支持图像相似度计算、图文相似度计算
- 支持命令行图像转向量（多卡）、建索引、批量检索、启动服务
- 图像特征提取：本项目基于cv2实现了pHash、dHash、wHash、aHash、SIFT等多种图像特征提取算法
## Demo
Image Search Demo: https://huggingface.co/spaces/shibing624/CLIP-Image-Search
Text Search Demo: https://huggingface.co/spaces/shibing624/similarities
## Install
```
pip install torch # conda install pytorch
pip install -U similarities
```
or
```
git clone https://github.com/shibing624/similarities.git
cd similarities
pip install -e .
```
## Usage
### 1. 文本向量相似度计算
example: [examples/text_similarity_demo.py](https://github.com/shibing624/similarities/blob/main/examples/text_similarity_demo.py)
```python
from similarities import BertSimilarity
m = BertSimilarity(model_name_or_path="shibing624/text2vec-base-chinese")
r = m.similarity('如何更换花呗绑定银行卡', '花呗更改绑定银行卡')
print(f"similarity score: {float(r)}") # similarity score: 0.855146050453186
```
- `model_name_or_path`：模型名称或者路径，默认会从HF model hub下载并使用中文语义匹配模型[shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese)，如果需要多语言，可以替换为[shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual)模型，支持中、英、韩、日、德、意等多国语言
### 2. 文本向量搜索
在文档候选集中找与query最相似的文本，常用于QA场景的问句相似匹配、文本搜索等任务。
#### SemanticSearch精准搜索算法，Cos Similarity + topK 聚类检索，适合百万内数据集
example: [examples/text_semantic_search_demo.py](https://github.com/shibing624/similarities/blob/main/examples/text_semantic_search_demo.py)
#### Annoy、Hnswlib等近似搜索算法，适合百万级数据集
example: [examples/fast_text_semantic_search_demo.py](https://github.com/shibing624/similarities/blob/main/examples/fast_text_semantic_search_demo.py)
#### Faiss高效向量检索，适合亿级数据集
- 文本转向量，建索引，批量检索，启动服务：[examples/faiss_bert_search_server_demo.py](https://github.com/shibing624/similarities/blob/main/examples/faiss_bert_search_server_demo.py)
- 前端python调用：[examples/faiss_bert_search_client_demo.py](https://github.com/shibing624/similarities/blob/main/examples/faiss_bert_search_client_demo.py)
### 3. 基于字面的文本相似度计算和文本搜索
支持同义词词林（Cilin）、知网Hownet、词向量（WordEmbedding）、Tfidf、SimHash、BM25等算法的相似度计算和字面匹配搜索，常用于文本匹配冷启动。
example: [examples/literal_text_semantic_search_demo.py](https://github.com/shibing624/similarities/blob/main/examples/literal_text_semantic_search_demo.py)
### 4. 图像相似度计算和图片搜索
支持CLIP、pHash、SIFT等算法的图像相似度计算和匹配搜索，中文CLIP模型支持图搜图，文搜图、还支持中英文图文互搜。
example: [examples/image_semantic_search_demo.py](https://github.com/shibing624/similarities/blob/main/examples/image_semantic_search_demo.py)
#### Faiss高效向量检索，适合亿级数据集
- 图像转向量，建索引，批量检索，启动服务：[examples/faiss_clip_search_server_demo.py](https://github.com/shibing624/similarities/blob/main/examples/faiss_clip_search_server_demo.py)
- 前端python调用：[examples/faiss_clip_search_client_demo.py](https://github.com/shibing624/similarities/blob/main/examples/faiss_clip_search_client_demo.py)
- 前端gradio调用：[examples/faiss_clip_search_gradio_demo.py](https://github.com/shibing624/similarities/blob/main/examples/faiss_clip_search_gradio_demo.py)
<img src="https://github.com/shibing624/similarities/blob/main/docs/dog-img.png"/>
### 命令行模式（CLI）
- 支持批量获取文本向量、图像向量（embedding）
- 支持构建索引（index）
- 支持批量检索（filter）
- 支持启动服务（server）
code: [cli.py](https://github.com/shibing624/similarities/blob/main/similarities/cli.py)
```
> similarities -h
NAME
similarities
SYNOPSIS
similarities COMMAND
COMMANDS
COMMAND is one of the following:
bert_embedding
Compute embeddings for a list of sentences
bert_index
Build indexes from text embeddings using autofaiss
bert_filter
Entry point of bert filter, batch search index
bert_server
Main entry point of bert search backend, start the server
clip_embedding
Embedding text and image with clip model
clip_index
Build indexes from embeddings using autofaiss
clip_filter
Entry point of clip filter, batch search index
clip_server
Main entry point of clip search backend, start the server
```
run：
```shell
pip install similarities -U
similarities clip_embedding -h
# example
cd examples
similarities clip_embedding data/toy_clip/
```
- `bert_embedding`等是二级命令，bert开头的是文本相关，clip开头的是图像相关
- 各二级命令使用方法见`similarities clip_embedding -h`
- 上面示例中`data/toy_clip/`是`clip_embedding`方法的`input_dir`参数，输入文件目录（required）
## Contact
- Issue(建议)
：[](https://github.com/shibing624/similarities/issues)
- 邮件我：xuming: xuming624@qq.com
- 微信我： 加我*微信号：xuming624, 备注：姓名-公司-NLP* 进NLP交流群。
<img src="https://github.com/shibing624/similarities/blob/main/docs/wechat.jpeg" width="200" />
## Citation
如果你在研究中使用了similarities，请按如下格式引用：
APA:
```
Xu, M. Similarities: Compute similarity score for humans (Version 1.0.1) [Computer software]. https://github.com/shibing624/similarities
```
BibTeX:
```
@misc{Xu_Similarities_Compute_similarity,
title={Similarities: similarity calculation and semantic search toolkit},
author={Xu Ming},
year={2022},
howpublished={\url{https://github.com/shibing624/similarities}},
}
```
## License
授权协议为 [The Apache License 2.0](/LICENSE)，可免费用做商业用途。请在产品说明中附加similarities的链接和授权协议。
## Contribute
项目代码还很粗糙，如果大家对代码有所改进，欢迎提交回本项目，在提交之前，注意以下两点：
- 在`tests`添加相应的单元测试
- 使用`python -m pytest`来运行所有单元测试，确保所有单测都是通过的
之后即可提交PR。
## Acknowledgements
- [A Simple but Tough-to-Beat Baseline for Sentence Embeddings[Sanjeev Arora and Yingyu Liang and Tengyu Ma, 2017]](https://openreview.net/forum?id=SyK00v5xx)
- [https://github.com/liuhuanyong/SentenceSimilarity](https://github.com/liuhuanyong/SentenceSimilarity)
- [https://github.com/qwertyforce/image_search](https://github.com/qwertyforce/image_search)
- [ImageHash - Official Github repository](https://github.com/JohannesBuchner/imagehash)
- [https://github.com/openai/CLIP](https://github.com/openai/CLIP)
- [https://github.com/OFA-Sys/Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)
- [https://github.com/UKPLab/sentence-transformers](https://github.com/UKPLab/sentence-transformers)
- [https://github.com/rom1504/clip-retrieval](https://github.com/rom1504/clip-retrieval)
Thanks for their great work!
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"description": "[**\ud83c\udde8\ud83c\uddf3\u4e2d\u6587**](https://github.com/shibing624/similarities/blob/main/README.md) | [**\ud83c\udf10English**](https://github.com/shibing624/similarities/blob/main/README_EN.md) | [**\ud83d\udcd6\u6587\u6863/Docs**](https://github.com/shibing624/similarities/wiki) | [**\ud83e\udd16\u6a21\u578b/Models**](https://huggingface.co/shibing624) \n\n<div align=\"center\">\n <a href=\"https://github.com/shibing624/similarities\">\n <img src=\"https://github.com/shibing624/similarities/blob/main/docs/logo.png\" height=\"150\" alt=\"Logo\">\n </a>\n</div>\n\n-----------------\n\n# Similarities: Similarity Calculation and Semantic Search\n[](https://badge.fury.io/py/similarities)\n[](https://pepy.tech/project/similarities)\n[](CONTRIBUTING.md)\n[](LICENSE)\n[](requirements.txt)\n[](https://github.com/shibing624/similarities/issues)\n[](#Contact)\n\n\n**similarities**: a toolkit for similarity calculation and semantic search, supports text and image. \u76f8\u4f3c\u5ea6\u8ba1\u7b97\u3001\u8bed\u4e49\u5339\u914d\u641c\u7d22\u5de5\u5177\u5305\u3002\n\n**similarities** \u5b9e\u73b0\u4e86\u591a\u79cd\u6587\u672c\u548c\u56fe\u7247\u7684\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u3001\u8bed\u4e49\u5339\u914d\u68c0\u7d22\u7b97\u6cd5\uff0c\u652f\u6301\u4ebf\u7ea7\u6570\u636e\u6587\u641c\u6587\u3001\u6587\u641c\u56fe\u3001\u56fe\u641c\u56fe\uff0cpython3\u5f00\u53d1\uff0cpip\u5b89\u88c5\uff0c\u5f00\u7bb1\u5373\u7528\u3002\n\n**Guide**\n\n- [Features](#Features)\n- [Install](#install)\n- [Usage](#usage)\n- [Contact](#Contact)\n- [Acknowledgements](#Acknowledgements)\n\n## Features\n\n### \u6587\u672c\u76f8\u4f3c\u5ea6\u8ba1\u7b97 + \u6587\u672c\u641c\u7d22\n\n- \u8bed\u4e49\u5339\u914d\u6a21\u578b\u3010\u63a8\u8350\u3011\uff1a\u672c\u9879\u76ee\u57fa\u4e8etext2vec\u5b9e\u73b0\u4e86CoSENT\u6a21\u578b\u7684\u6587\u672c\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u548c\u6587\u672c\u641c\u7d22\n - \u652f\u6301\u4e2d\u82f1\u6587\u3001\u591a\u8bed\u8a00\u591a\u79cdSentenceBERT\u7c7b\u9884\u8bad\u7ec3\u6a21\u578b\n - \u652f\u6301 Cos Similarity/Dot Product/Hamming Distance/Euclidean Distance \u7b49\u591a\u79cd\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u65b9\u6cd5\n - \u652f\u6301 SemanticSearch/Faiss/Annoy/Hnsw \u7b49\u591a\u79cd\u6587\u672c\u641c\u7d22\u7b97\u6cd5\n - \u652f\u6301\u4ebf\u7ea7\u6570\u636e\u9ad8\u6548\u68c0\u7d22\n - \u652f\u6301\u547d\u4ee4\u884c\u6587\u672c\u8f6c\u5411\u91cf\uff08\u591a\u5361\uff09\u3001\u5efa\u7d22\u5f15\u3001\u6279\u91cf\u68c0\u7d22\u3001\u542f\u52a8\u670d\u52a1\n- \u5b57\u9762\u5339\u914d\u6a21\u578b\uff1a\u672c\u9879\u76ee\u5b9e\u73b0\u4e86Word2Vec\u3001BM25\u3001RankBM25\u3001TFIDF\u3001SimHash\u3001\u540c\u4e49\u8bcd\u8bcd\u6797\u3001\u77e5\u7f51Hownet\u4e49\u539f\u5339\u914d\u7b49\u591a\u79cd\u5b57\u9762\u5339\u914d\u6a21\u578b\n\n\n### \u56fe\u50cf\u76f8\u4f3c\u5ea6\u8ba1\u7b97/\u56fe\u6587\u76f8\u4f3c\u5ea6\u8ba1\u7b97 + \u56fe\u641c\u56fe/\u6587\u641c\u56fe\n- CLIP(Contrastive Language-Image Pre-Training)\u6a21\u578b\uff1a\u56fe\u6587\u5339\u914d\u6a21\u578b\uff0c\u53ef\u7528\u4e8e\u56fe\u6587\u7279\u5f81\uff08embeddings\uff09\u3001\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u3001\u56fe\u6587\u68c0\u7d22\u3001\u96f6\u6837\u672c\u56fe\u7247\u5206\u7c7b\uff0c\u672c\u9879\u76ee\u57fa\u4e8ePyTorch\u5b9e\u73b0\u4e86CLIP\u6a21\u578b\u7684\u5411\u91cf\u8868\u5f81\u3001\u6784\u5efa\u7d22\u5f15\uff08\u57fa\u4e8eAutoFaiss\uff09\u3001\u6279\u91cf\u68c0\u7d22\u3001\u540e\u53f0\u670d\u52a1\uff08\u57fa\u4e8eFastAPI\uff09\u3001\u524d\u7aef\u5c55\u73b0\uff08\u57fa\u4e8eGradio\uff09\u529f\u80fd\n - \u652f\u6301[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32)\u7b49CLIP\u7cfb\u5217\u6a21\u578b\n - \u652f\u6301[OFA-Sys/chinese-clip-vit-huge-patch14](https://huggingface.co/OFA-Sys/chinese-clip-vit-huge-patch14)\u7b49Chinese-CLIP\u7cfb\u5217\u6a21\u578b\n - \u652f\u6301\u524d\u540e\u7aef\u5206\u79bb\u90e8\u7f72\uff0cFastAPI\u540e\u7aef\u670d\u52a1\uff0cGradio\u524d\u7aef\u5c55\u73b0\n - \u652f\u6301\u4ebf\u7ea7\u6570\u636e\u9ad8\u6548\u68c0\u7d22\uff0c\u57fa\u4e8eFaiss\u68c0\u7d22\uff0c\u652f\u6301GPU\u52a0\u901f\n - \u652f\u6301\u56fe\u641c\u56fe\u3001\u6587\u641c\u56fe\u3001\u5411\u91cf\u641c\u56fe\n - \u652f\u6301\u56fe\u50cfembedding\u63d0\u53d6\u3001\u6587\u672cembedding\u63d0\u53d6\n - \u652f\u6301\u56fe\u50cf\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u3001\u56fe\u6587\u76f8\u4f3c\u5ea6\u8ba1\u7b97\n - \u652f\u6301\u547d\u4ee4\u884c\u56fe\u50cf\u8f6c\u5411\u91cf\uff08\u591a\u5361\uff09\u3001\u5efa\u7d22\u5f15\u3001\u6279\u91cf\u68c0\u7d22\u3001\u542f\u52a8\u670d\u52a1\n- \u56fe\u50cf\u7279\u5f81\u63d0\u53d6\uff1a\u672c\u9879\u76ee\u57fa\u4e8ecv2\u5b9e\u73b0\u4e86pHash\u3001dHash\u3001wHash\u3001aHash\u3001SIFT\u7b49\u591a\u79cd\u56fe\u50cf\u7279\u5f81\u63d0\u53d6\u7b97\u6cd5\n\n## Demo\nImage Search Demo: https://huggingface.co/spaces/shibing624/CLIP-Image-Search\n\n\n\nText Search Demo: https://huggingface.co/spaces/shibing624/similarities\n\n\n\n\n## Install\n\n```\npip install torch # conda install pytorch\npip install -U similarities\n```\n\nor\n\n```\ngit clone https://github.com/shibing624/similarities.git\ncd similarities\npip install -e .\n```\n\n## Usage\n\n### 1. \u6587\u672c\u5411\u91cf\u76f8\u4f3c\u5ea6\u8ba1\u7b97\n\nexample: [examples/text_similarity_demo.py](https://github.com/shibing624/similarities/blob/main/examples/text_similarity_demo.py)\n\n\n```python\nfrom similarities import BertSimilarity\nm = BertSimilarity(model_name_or_path=\"shibing624/text2vec-base-chinese\")\nr = m.similarity('\u5982\u4f55\u66f4\u6362\u82b1\u5457\u7ed1\u5b9a\u94f6\u884c\u5361', '\u82b1\u5457\u66f4\u6539\u7ed1\u5b9a\u94f6\u884c\u5361')\nprint(f\"similarity score: {float(r)}\") # similarity score: 0.855146050453186\n```\n\n- `model_name_or_path`\uff1a\u6a21\u578b\u540d\u79f0\u6216\u8005\u8def\u5f84\uff0c\u9ed8\u8ba4\u4f1a\u4eceHF model hub\u4e0b\u8f7d\u5e76\u4f7f\u7528\u4e2d\u6587\u8bed\u4e49\u5339\u914d\u6a21\u578b[shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese)\uff0c\u5982\u679c\u9700\u8981\u591a\u8bed\u8a00\uff0c\u53ef\u4ee5\u66ff\u6362\u4e3a[shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual)\u6a21\u578b\uff0c\u652f\u6301\u4e2d\u3001\u82f1\u3001\u97e9\u3001\u65e5\u3001\u5fb7\u3001\u610f\u7b49\u591a\u56fd\u8bed\u8a00\n\n### 2. \u6587\u672c\u5411\u91cf\u641c\u7d22\n\n\u5728\u6587\u6863\u5019\u9009\u96c6\u4e2d\u627e\u4e0equery\u6700\u76f8\u4f3c\u7684\u6587\u672c\uff0c\u5e38\u7528\u4e8eQA\u573a\u666f\u7684\u95ee\u53e5\u76f8\u4f3c\u5339\u914d\u3001\u6587\u672c\u641c\u7d22\u7b49\u4efb\u52a1\u3002\n\n#### SemanticSearch\u7cbe\u51c6\u641c\u7d22\u7b97\u6cd5\uff0cCos Similarity + topK \u805a\u7c7b\u68c0\u7d22\uff0c\u9002\u5408\u767e\u4e07\u5185\u6570\u636e\u96c6\n\nexample: [examples/text_semantic_search_demo.py](https://github.com/shibing624/similarities/blob/main/examples/text_semantic_search_demo.py)\n\n#### Annoy\u3001Hnswlib\u7b49\u8fd1\u4f3c\u641c\u7d22\u7b97\u6cd5\uff0c\u9002\u5408\u767e\u4e07\u7ea7\u6570\u636e\u96c6\n\nexample: [examples/fast_text_semantic_search_demo.py](https://github.com/shibing624/similarities/blob/main/examples/fast_text_semantic_search_demo.py)\n\n#### Faiss\u9ad8\u6548\u5411\u91cf\u68c0\u7d22\uff0c\u9002\u5408\u4ebf\u7ea7\u6570\u636e\u96c6\n\n- \u6587\u672c\u8f6c\u5411\u91cf\uff0c\u5efa\u7d22\u5f15\uff0c\u6279\u91cf\u68c0\u7d22\uff0c\u542f\u52a8\u670d\u52a1\uff1a[examples/faiss_bert_search_server_demo.py](https://github.com/shibing624/similarities/blob/main/examples/faiss_bert_search_server_demo.py)\n\n- \u524d\u7aefpython\u8c03\u7528\uff1a[examples/faiss_bert_search_client_demo.py](https://github.com/shibing624/similarities/blob/main/examples/faiss_bert_search_client_demo.py)\n\n\n### 3. \u57fa\u4e8e\u5b57\u9762\u7684\u6587\u672c\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u548c\u6587\u672c\u641c\u7d22\n\n\u652f\u6301\u540c\u4e49\u8bcd\u8bcd\u6797\uff08Cilin\uff09\u3001\u77e5\u7f51Hownet\u3001\u8bcd\u5411\u91cf\uff08WordEmbedding\uff09\u3001Tfidf\u3001SimHash\u3001BM25\u7b49\u7b97\u6cd5\u7684\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u548c\u5b57\u9762\u5339\u914d\u641c\u7d22\uff0c\u5e38\u7528\u4e8e\u6587\u672c\u5339\u914d\u51b7\u542f\u52a8\u3002\n\nexample: [examples/literal_text_semantic_search_demo.py](https://github.com/shibing624/similarities/blob/main/examples/literal_text_semantic_search_demo.py)\n\n### 4. \u56fe\u50cf\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u548c\u56fe\u7247\u641c\u7d22\n\n\u652f\u6301CLIP\u3001pHash\u3001SIFT\u7b49\u7b97\u6cd5\u7684\u56fe\u50cf\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u548c\u5339\u914d\u641c\u7d22\uff0c\u4e2d\u6587CLIP\u6a21\u578b\u652f\u6301\u56fe\u641c\u56fe\uff0c\u6587\u641c\u56fe\u3001\u8fd8\u652f\u6301\u4e2d\u82f1\u6587\u56fe\u6587\u4e92\u641c\u3002\n\nexample: [examples/image_semantic_search_demo.py](https://github.com/shibing624/similarities/blob/main/examples/image_semantic_search_demo.py)\n\n\n\n#### Faiss\u9ad8\u6548\u5411\u91cf\u68c0\u7d22\uff0c\u9002\u5408\u4ebf\u7ea7\u6570\u636e\u96c6\n\n- \u56fe\u50cf\u8f6c\u5411\u91cf\uff0c\u5efa\u7d22\u5f15\uff0c\u6279\u91cf\u68c0\u7d22\uff0c\u542f\u52a8\u670d\u52a1\uff1a[examples/faiss_clip_search_server_demo.py](https://github.com/shibing624/similarities/blob/main/examples/faiss_clip_search_server_demo.py)\n\n- \u524d\u7aefpython\u8c03\u7528\uff1a[examples/faiss_clip_search_client_demo.py](https://github.com/shibing624/similarities/blob/main/examples/faiss_clip_search_client_demo.py)\n\n- \u524d\u7aefgradio\u8c03\u7528\uff1a[examples/faiss_clip_search_gradio_demo.py](https://github.com/shibing624/similarities/blob/main/examples/faiss_clip_search_gradio_demo.py)\n\n<img src=\"https://github.com/shibing624/similarities/blob/main/docs/dog-img.png\"/>\n\n\n\n\n### \u547d\u4ee4\u884c\u6a21\u5f0f\uff08CLI\uff09\n\n- \u652f\u6301\u6279\u91cf\u83b7\u53d6\u6587\u672c\u5411\u91cf\u3001\u56fe\u50cf\u5411\u91cf\uff08embedding\uff09\n- \u652f\u6301\u6784\u5efa\u7d22\u5f15\uff08index\uff09\n- \u652f\u6301\u6279\u91cf\u68c0\u7d22\uff08filter\uff09\n- \u652f\u6301\u542f\u52a8\u670d\u52a1\uff08server\uff09\n\ncode: [cli.py](https://github.com/shibing624/similarities/blob/main/similarities/cli.py)\n\n```\n> similarities -h \n\nNAME\n similarities\n\nSYNOPSIS\n similarities COMMAND\n\nCOMMANDS\n COMMAND is one of the following:\n\n bert_embedding\n Compute embeddings for a list of sentences\n\n bert_index\n Build indexes from text embeddings using autofaiss\n\n bert_filter\n Entry point of bert filter, batch search index\n\n bert_server\n Main entry point of bert search backend, start the server\n\n clip_embedding\n Embedding text and image with clip model\n\n clip_index\n Build indexes from embeddings using autofaiss\n\n clip_filter\n Entry point of clip filter, batch search index\n\n clip_server\n Main entry point of clip search backend, start the server\n```\n\nrun\uff1a\n\n```shell\npip install similarities -U\nsimilarities clip_embedding -h\n\n# example\ncd examples\nsimilarities clip_embedding data/toy_clip/\n```\n\n- `bert_embedding`\u7b49\u662f\u4e8c\u7ea7\u547d\u4ee4\uff0cbert\u5f00\u5934\u7684\u662f\u6587\u672c\u76f8\u5173\uff0cclip\u5f00\u5934\u7684\u662f\u56fe\u50cf\u76f8\u5173\n- \u5404\u4e8c\u7ea7\u547d\u4ee4\u4f7f\u7528\u65b9\u6cd5\u89c1`similarities clip_embedding -h`\n- \u4e0a\u9762\u793a\u4f8b\u4e2d`data/toy_clip/`\u662f`clip_embedding`\u65b9\u6cd5\u7684`input_dir`\u53c2\u6570\uff0c\u8f93\u5165\u6587\u4ef6\u76ee\u5f55\uff08required\uff09\n\n\n\n## Contact\n\n- Issue(\u5efa\u8bae)\n \uff1a[](https://github.com/shibing624/similarities/issues)\n- \u90ae\u4ef6\u6211\uff1axuming: xuming624@qq.com\n- \u5fae\u4fe1\u6211\uff1a \u52a0\u6211*\u5fae\u4fe1\u53f7\uff1axuming624, \u5907\u6ce8\uff1a\u59d3\u540d-\u516c\u53f8-NLP* \u8fdbNLP\u4ea4\u6d41\u7fa4\u3002\n\n<img src=\"https://github.com/shibing624/similarities/blob/main/docs/wechat.jpeg\" width=\"200\" />\n\n## Citation\n\n\u5982\u679c\u4f60\u5728\u7814\u7a76\u4e2d\u4f7f\u7528\u4e86similarities\uff0c\u8bf7\u6309\u5982\u4e0b\u683c\u5f0f\u5f15\u7528\uff1a\n\nAPA:\n\n```\nXu, M. Similarities: Compute similarity score for humans (Version 1.0.1) [Computer software]. https://github.com/shibing624/similarities\n```\n\nBibTeX:\n\n```\n@misc{Xu_Similarities_Compute_similarity,\n title={Similarities: similarity calculation and semantic search toolkit},\n author={Xu Ming},\n year={2022},\n howpublished={\\url{https://github.com/shibing624/similarities}},\n}\n```\n\n## License\n\n\u6388\u6743\u534f\u8bae\u4e3a [The Apache License 2.0](/LICENSE)\uff0c\u53ef\u514d\u8d39\u7528\u505a\u5546\u4e1a\u7528\u9014\u3002\u8bf7\u5728\u4ea7\u54c1\u8bf4\u660e\u4e2d\u9644\u52a0similarities\u7684\u94fe\u63a5\u548c\u6388\u6743\u534f\u8bae\u3002\n\n## Contribute\n\n\u9879\u76ee\u4ee3\u7801\u8fd8\u5f88\u7c97\u7cd9\uff0c\u5982\u679c\u5927\u5bb6\u5bf9\u4ee3\u7801\u6709\u6240\u6539\u8fdb\uff0c\u6b22\u8fce\u63d0\u4ea4\u56de\u672c\u9879\u76ee\uff0c\u5728\u63d0\u4ea4\u4e4b\u524d\uff0c\u6ce8\u610f\u4ee5\u4e0b\u4e24\u70b9\uff1a\n\n- \u5728`tests`\u6dfb\u52a0\u76f8\u5e94\u7684\u5355\u5143\u6d4b\u8bd5\n- \u4f7f\u7528`python -m pytest`\u6765\u8fd0\u884c\u6240\u6709\u5355\u5143\u6d4b\u8bd5\uff0c\u786e\u4fdd\u6240\u6709\u5355\u6d4b\u90fd\u662f\u901a\u8fc7\u7684\n\n\u4e4b\u540e\u5373\u53ef\u63d0\u4ea4PR\u3002\n\n## Acknowledgements \n\n- [A Simple but Tough-to-Beat Baseline for Sentence Embeddings[Sanjeev Arora and Yingyu Liang and Tengyu Ma, 2017]](https://openreview.net/forum?id=SyK00v5xx)\n- [https://github.com/liuhuanyong/SentenceSimilarity](https://github.com/liuhuanyong/SentenceSimilarity)\n- [https://github.com/qwertyforce/image_search](https://github.com/qwertyforce/image_search)\n- [ImageHash - Official Github repository](https://github.com/JohannesBuchner/imagehash)\n- [https://github.com/openai/CLIP](https://github.com/openai/CLIP)\n- [https://github.com/OFA-Sys/Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)\n- [https://github.com/UKPLab/sentence-transformers](https://github.com/UKPLab/sentence-transformers)\n- [https://github.com/rom1504/clip-retrieval](https://github.com/rom1504/clip-retrieval)\n\nThanks for their great work!",
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