[Feat] - Add Sitemap Feature for the Docs

docs/develop/rust/wasinn/mediapipe.md

@@ -4,10 +4,7 @@ sidebar_position: 1
 
 # Mediapipe solutions
 
-Mediapipe is a collection of highly popular AI models developed by Google. They focus on intelligent processing of media files
-and streams. The `mediapipe-rs` crate is a Rust library for data processing using the Mediapipe suite of models. The crate provides
-Rust APIs to pre-process the data in media files or streams, run AI model inference to analyze the data, and then post-process
-or manipulate the media data based on the AI output.
+Mediapipe is a collection of highly popular AI models developed by Google. They focus on intelligent processing of media files and streams. The `mediapipe-rs` crate is a Rust library for data processing using the Mediapipe suite of models. The crate provides Rust APIs to pre-process the data in media files or streams, run AI model inference to analyze the data, and then post-process or manipulate the media data based on the AI output.
 
 ## Prerequisite
 
@@ -63,10 +60,7 @@ DetectionResult:
 
 ## Understand the code
 
-The [main.rs](https://github.com/juntao/demo-object-detection/blob/main/src/main.rs) is the complete example Rust source.
-All `mediapipe-rs` APIs follow a common pattern. A Rust struct is designed to work with a model. It contains functions
-required to pre- and post-process data for the model. For example, we can create an `detector` instance
-using the builder pattern, which can build from any "object detection" model in the Mediapipe model library.
+The [main.rs](https://github.com/juntao/demo-object-detection/blob/main/src/main.rs) is the complete example Rust source. All `mediapipe-rs` APIs follow a common pattern. A Rust struct is designed to work with a model. It contains functions required to pre- and post-process data for the model. For example, we can create an `detector` instance using the builder pattern, which can build from any "object detection" model in the Mediapipe model library.
 
 ```rust
 let model_data: &[u8] = include_bytes!("mobilenetv2_ssd_256_uint8.tflite");
@@ -75,17 +69,15 @@ let detector = ObjectDetectorBuilder::new()
         .build_from_buffer(model_data)?;
 ```
 
-The `detect()` function takes in an image, pre-processes it into a tensor array, runs inference on the mediapipe object detection model,
-and the post-processes the returned tensor array into a human redable format stored in the `detection_result`.
+The `detect()` function takes in an image, pre-processes it into a tensor array, runs inference on the mediapipe object detection model, and the post-processes the returned tensor array into a human redable format stored in the `detection_result`.
 
 ```rust
 let mut input_img = image::open(img_path)?;
 let detection_result = detector.detect(&input_img)?;
 println!("{}", detection_result);
 ```
 
-Furthermore, the `mediapipe-rs` crate provides additional utility functions to post-process the data. For example,
-the `draw_detection()` utility function draws the data in `detection_result` onto the input image.
+Furthermore, the `mediapipe-rs` crate provides additional utility functions to post-process the data. For example, the `draw_detection()` utility function draws the data in `detection_result` onto the input image.
 
 ```rust
 draw_detection(&mut input_img, &detection_result);

docs/start/usage/use-cases.md

@@ -20,4 +20,4 @@ Featuring AOT compiler optimization, WasmEdge is one of the fastest WebAssembly
 
 - WasmEdge applications can be plugged into existing application frameworks or platforms.
 
-If you have any great ideas on WasmEdge, don't hesitate to open a GitHub issue to discuss together.
+If you have any great ideas on WasmEdge, don't hesitate to open a GitHub issue to discuss together.

docusaurus.config.js

@@ -64,6 +64,12 @@ const config = {
                 theme: {
                     customCss: require.resolve('./src/css/custom.css'),
                 },
+                sitemap: {
+                    changefreq: 'weekly',
+                    priority: 0.5,
+                    ignorePatterns: ['/tags/**'],
+                    filename: 'sitemap.xml',
+                },
             }),
         ],
     ],
@@ -172,13 +178,16 @@ const config = {
                             {
                                 label: 'Getting Started',
                                 to: '/start/overview',
-                            }, {
+                            },
+                            {
                                 label: 'Develop',
                                 to: '/develop/overview',
-                            }, {
+                            },
+                            {
                                 label: 'Embeds',
                                 to: '/embed/overview',
-                            }, {
+                            },
+                            {
                                 label: 'Contribute',
                                 to: '/contribute/overview',
                             }
@@ -189,18 +198,26 @@ const config = {
                             {
                                 label: 'Github',
                                 href: 'https://github.com/WasmEdge/WasmEdge',
-                            }, {
+                            },
+                            {
                                 label: 'Second State',
                                 href: 'https://www.secondstate.io/',
-                            }, {
+                            },
+                            {
                                 label: 'Articles & Blog',
                                 href: 'https://www.secondstate.io/articles/'
-                            }, {
+                            },
+                            {
                                 label: 'WasmEdge Talks',
                                 to: '/talks'
-                            }, {
+                            },
+                            {
                                 label: 'Releases',
                                 to: '/releases'
+                            },
+                            {
+                                label: 'Sitemap',
+                                href: 'https://wasmedge.org/docs/sitemap.xml'
                             }
                         ],
                     },
@@ -210,16 +227,20 @@ const config = {
                             {
                                 label: 'Mailing List',
                                 href: 'https://groups.google.com/g/wasmedge/'
-                            }, {
+                            },
+                            {
                                 label: 'Discord',
                                 href: 'https://discord.gg/U4B5sFTkFc',
-                            }, {
+                            },
+                            {
                                 label: 'Twitter',
                                 href: 'https://twitter.com/realwasmedge',
-                            }, {
+                            },
+                            {
                                 label: 'Slack #WasmEdge',
                                 href: 'https://cloud-native.slack.com/archives/C0215BBK248'
-                            }, {
+                            },
+                            {
                                 label: 'Community Meeting',
                                 href: 'https://docs.google.com/document/d/1iFlVl7R97Lze4RDykzElJGDjjWYDlkI8Rhf8g4dQ5Rk/edit?usp=sharing'
                             }

i18n/zh/docusaurus-plugin-content-docs/current/develop/rust/wasinn/mediapipe.md

@@ -4,10 +4,7 @@ sidebar_position: 1
 
 # Mediapipe solutions
 
-Mediapipe is a collection of highly popular AI models developed by Google. They focus on intelligent processing of media files
-and streams. The `mediapipe-rs` crate is a Rust library for data processing using the Mediapipe suite of models. The crate provides
-Rust APIs to pre-process the data in media files or streams, run AI model inference to analyze the data, and then post-process
-or manipulate the media data based on the AI output.
+Mediapipe is a collection of highly popular AI models developed by Google. They focus on intelligent processing of media files and streams. The `mediapipe-rs` crate is a Rust library for data processing using the Mediapipe suite of models. The crate provides Rust APIs to pre-process the data in media files or streams, run AI model inference to analyze the data, and then post-process or manipulate the media data based on the AI output.
 
 ## Prerequisite
 
@@ -63,10 +60,7 @@ DetectionResult:
 
 ## Understand the code
 
-The [main.rs](https://github.com/juntao/demo-object-detection/blob/main/src/main.rs) is the complete example Rust source.
-All `mediapipe-rs` APIs follow a common pattern. A Rust struct is designed to work with a model. It contains functions
-required to pre- and post-process data for the model. For example, we can create an `detector` instance
-using the builder pattern, which can build from any "object detection" model in the Mediapipe model library.
+The [main.rs](https://github.com/juntao/demo-object-detection/blob/main/src/main.rs) is the complete example Rust source. All `mediapipe-rs` APIs follow a common pattern. A Rust struct is designed to work with a model. It contains functions required to pre- and post-process data for the model. For example, we can create an `detector` instance using the builder pattern, which can build from any "object detection" model in the Mediapipe model library.
 
 ```rust
 let model_data: &[u8] = include_bytes!("mobilenetv2_ssd_256_uint8.tflite");
@@ -75,17 +69,15 @@ let detector = ObjectDetectorBuilder::new()
         .build_from_buffer(model_data)?;
 ```
 
-The `detect()` function takes in an image, pre-processes it into a tensor array, runs inference on the mediapipe object detection model,
-and the post-processes the returned tensor array into a human redable format stored in the `detection_result`.
+The `detect()` function takes in an image, pre-processes it into a tensor array, runs inference on the mediapipe object detection model, and the post-processes the returned tensor array into a human redable format stored in the `detection_result`.
 
 ```rust
 let mut input_img = image::open(img_path)?;
 let detection_result = detector.detect(&input_img)?;
 println!("{}", detection_result);
 ```
 
-Furthermore, the `mediapipe-rs` crate provides additional utility functions to post-process the data. For example,
-the `draw_detection()` utility function draws the data in `detection_result` onto the input image.
+Furthermore, the `mediapipe-rs` crate provides additional utility functions to post-process the data. For example, the `draw_detection()` utility function draws the data in `detection_result` onto the input image.
 
 ```rust
 draw_detection(&mut input_img, &detection_result);

i18n/zh/docusaurus-plugin-content-docs/current/start/usage/use-cases.md

@@ -20,4 +20,4 @@ Featuring AOT compiler optimization, WasmEdge is one of the fastest WebAssembly
 
 - WasmEdge applications can be plugged into existing application frameworks or platforms.
 
-If you have any great ideas on WasmEdge, don't hesitate to open a GitHub issue to discuss together.
+If you have any great ideas on WasmEdge, don't hesitate to open a GitHub issue to discuss together.

package.json

@@ -31,6 +31,7 @@
   "dependencies": {
     "@docusaurus/core": "^2.4.0",
     "@docusaurus/plugin-google-gtag": "^2.4.1",
+    "@docusaurus/plugin-sitemap": "^2.4.1",
     "@docusaurus/preset-classic": "^2.4.0",
     "@docusaurus/theme-mermaid": "^2.4.1",
     "@easyops-cn/docusaurus-search-local": "^0.35.0",