docs: Update readme

readme.md

@@ -1,12 +1,16 @@
 # NekoImageGallery
 
-An online AI image search engine based on the Clip model and Qdrant vector database. Supports keyword search and similar image search.
+An online AI image search engine based on the Clip model and Qdrant vector database. Supports keyword search and similar
+image search.
 
 [中文文档](readme_cn.md)
 
 ## ✨ Features
 
-- Use the Clip model to generate 768-dimensional vectors for each image as the basis for search. No need for manual annotation or classification, unlimited classification categories.
+- Use the Clip model to generate 768-dimensional vectors for each image as the basis for search. No need for manual
+  annotation or classification, unlimited classification categories.
+- OCR Text search is supported, use PaddleOCR to extract text from images and use BERT to generate text vectors for
+  search.
 - Use Qdrant vector database for efficient vector search.
 
 ## 📷Screenshots
@@ -16,68 +20,88 @@ An online AI image search engine based on the Clip model and Qdrant vector datab
 ![Screenshot3](web/screenshots/3.png)
 ![Screenshot4](web/screenshots/4.png)
 
-> The above screenshots may contain copyrighted images from different artists, please do not use them for other purposes.
+> The above screenshots may contain copyrighted images from different artists, please do not use them for other
+> purposes.
 
 ## ✈️ Deployment
 
 ### Local Deployment
+
 #### Deploy Qdrant Database
 
-Please deploy the Qdrant database according to the [Qdrant documentation](https://qdrant.tech/documentation/quick-start/). It is recommended to use Docker for deployment.
+Please deploy the Qdrant database according to
+the [Qdrant documentation](https://qdrant.tech/documentation/quick-start/). It is recommended to use Docker for
+deployment.
 
-If you don't want to deploy Qdrant yourself, you can use the [online service provided by Qdrant](https://qdrant.tech/documentation/cloud/).
+If you don't want to deploy Qdrant yourself, you can use
+the [online service provided by Qdrant](https://qdrant.tech/documentation/cloud/).
 
 #### Deploy NekoImageGallery
+
 1. Clone the project directory to your own PC or server.
-2. It is highly recommended to install the dependencies required for this project in a Python venv virtual environment. Run the following command:
+2. It is highly recommended to install the dependencies required for this project in a Python venv virtual environment.
+   Run the following command:
     ```shell
     python -m venv .venv
     . .venv/bin/activate
     ```
-3. Install PyTorch. Follow the [PyTorch documentation](https://pytorch.org/get-started/locally/) to install the torch version suitable for your system using pip.
-   > If you want to use CUDA acceleration for inference, be sure to install a CUDA-supported PyTorch version in this step. After installation, you can use `torch.cuda.is_available()` to confirm whether CUDA is available.
+3. Install PyTorch. Follow the [PyTorch documentation](https://pytorch.org/get-started/locally/) to install the torch
+   version suitable for your system using pip.
+   > If you want to use CUDA acceleration for inference, be sure to install a CUDA-supported PyTorch version in this
+   step. After installation, you can use `torch.cuda.is_available()` to confirm whether CUDA is available.
 4. Install other dependencies required for this project:
     ```shell
     pip install -r requirements.txt
     ```
-5. Modify the project configuration file inside `config/`, you can edit `default.env` directly, but it's recommended to create a new file named `local.env` and override the configuration in `default.env`.
+5. Modify the project configuration file inside `config/`, you can edit `default.env` directly, but it's recommended to
+   create a new file named `local.env` and override the configuration in `default.env`.
 6. Initialize the Qdrant database by running the following command:
     ```shell
     python main.py --init-database
     ```
-   This operation will create a collection in the Qdrant database with the same name as `config.QDRANT_COLL` to store image vectors.
-7. (Optional) In development deployment and small-scale deployment, you can use the built-in static file indexing and service functions of this application. Use the following command to index your local image directory:
+   This operation will create a collection in the Qdrant database with the same name as `config.QDRANT_COLL` to store
+   image vectors.
+7. (Optional) In development deployment and small-scale deployment, you can use the built-in static file indexing and
+   service functions of this application. Use the following command to index your local image directory:
     ```shell
    python main.py --local-index <path-to-your-image-directory>
     ```
-   This operation will copy all image files in the `<path-to-your-image-directory>` directory to the `config.STATIC_FILE_PATH` directory (default is `./static`) and write the image information to the Qdrant database.
+   This operation will copy all image files in the `<path-to-your-image-directory>` directory to
+   the `config.STATIC_FILE_PATH` directory (default is `./static`) and write the image information to the Qdrant
+   database.
 
    Then run the following command to generate thumbnails for all images in the static directory:
 
    ```shell
      python main.py --local-create-thumbnail
    ```
-   
-   If you want to deploy on a large scale, you can use OSS storage services like `MinIO` to store image files in OSS and then write the image information to the Qdrant database.
+
+   If you want to deploy on a large scale, you can use OSS storage services like `MinIO` to store image files in OSS and
+   then write the image information to the Qdrant database.
 8. Run this application:
     ```shell
     python main.py
     ```
-   You can use `--host` to specify the IP address you want to bind to (default is 0.0.0.0) and `--port` to specify the port you want to bind to (default is 8000).
-9. (Optional) Deploy the front-end application: [NekoImageGallery.App](https://github.com/hv0905/NekoImageGallery.App) is a simple web front-end application for this project. If you want to deploy it, please refer to its [deployment documentation](https://github.com/hv0905/NekoImageGallery.App).
+   You can use `--host` to specify the IP address you want to bind to (default is 0.0.0.0) and `--port` to specify the
+   port you want to bind to (default is 8000).
+9. (Optional) Deploy the front-end application: [NekoImageGallery.App](https://github.com/hv0905/NekoImageGallery.App)
+   is a simple web front-end application for this project. If you want to deploy it, please refer to
+   its [deployment documentation](https://github.com/hv0905/NekoImageGallery.App).
 
 ### Docker Compose Containerized Deployment
 
 > [!WARNING]
 > Docker compose support is in an alpha state, and may not work for everyone(especially CUDA acceleration).
-> Please make sure you are familiar with [Docker documentation](https://docs.docker.com/) before using this deployment method.
+> Please make sure you are familiar with [Docker documentation](https://docs.docker.com/) before using this deployment
+> method.
 > If you encounter any problems during deployment, please submit an issue.
 
 #### Prepare `nvidia-container-runtime`
 
-If you want to use CUDA acceleration, you need to install `nvidia-container-runtime` on your system. Please refer to the [official documentation](https://docs.docker.com/config/containers/resource_constraints/#gpu) for installation.
+If you want to use CUDA acceleration, you need to install `nvidia-container-runtime` on your system. Please refer to
+the [official documentation](https://docs.docker.com/config/containers/resource_constraints/#gpu) for installation.
 
-> Related Document:  
+> Related Document:
 > 1. https://docs.docker.com/config/containers/resource_constraints/#gpu
 > 2. https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker
 > 3. https://nvidia.github.io/nvidia-container-runtime/
@@ -88,11 +112,27 @@ If you want to use CUDA acceleration, you need to install `nvidia-container-runt
 2. Run the following command to start the server:
     ```shell
     # start in foreground
-    docker-compose up
+    docker compose up
     # start in background(detached mode)
-    docker-compose up -d
+    docker compose up -d
     ```
 
+## 📊 Repository Summary
+
+![Alt](https://repobeats.axiom.co/api/embed/ac080afa0d2d8af0345f6818b9b7c35bf8de1d31.svg "Repobeats analytics image")
+
+## ♥ Contributing
+
+There are many ways to contribute to the project: logging bugs, submitting pull requests, reporting issues, and creating
+suggestions.
+
+Even if you with push access on the repository, you should create a personal feature branches when you need them.
+This keeps the main repository clean and your workflow cruft out of sight.
+
+We're also interested in your feedback on the future of this project. You can submit a suggestion or feature request
+through the issue tracker. To make this process more effective, we're asking that these include more information to help
+define them more clearly.
+
 ## Copyright
 
 Copyright 2023 EdgeNeko

readme_cn.md

@@ -7,6 +7,7 @@
 ## ✨特性
 
 - 使用Clip模型为每张图片生成768维向量作为搜索依据。无需人工标注或分类，无限分类类别。
+- 支持OCR文本搜索，使用PaddleOCR提取图片文本并使用BERT模型生成文本特征向量。
 - 使用Qdrant向量数据库进行高效的向量搜索。
 
 ## 📷截图
@@ -88,11 +89,23 @@
 2. 运行下面命令启动docker-compose
    ```shell
    # start in foreground
-   docker-compose up
+   docker compose up
    # start in background(detached mode)
-   docker-compose up -d
+   docker compose up -d
    ```
 
+## 📊仓库信息
+
+![Alt](https://repobeats.axiom.co/api/embed/ac080afa0d2d8af0345f6818b9b7c35bf8de1d31.svg "Repobeats analytics image")
+
+## ❤️贡献指南
+
+有很多种可以为本项目提供贡献的方式：记录 Bug，提交 Pull Request，报告问题，提出建议等等。
+
+即使您拥有对本仓库的写入权限，您也应该在有需要时创建自己的功能分支并通过 Pull Request 的方式提交您的变更。
+这有助于让我们的主仓库保持整洁并使您的个人工作流程不可见。
+
+我们也很感兴趣听到您关于这个项目未来的反馈。您可以通过 Issues 追踪器提交建议或功能请求。为了使这个过程更加有效，我们希望这些内容包含更多信息，以更清晰地定义它们。
 
 ## Copyright