Merge pull request #2 from ServerlessLLM/fix/relative_path

Fix/relative path

Author: future-xy

docs/README.md

@@ -36,4 +36,19 @@ This command generates static content into the `build` directory, which can be s
 
 ### About the image path
 
-Images are stored in `images` path. For example, we have an image called `a.jpg` in `images`. When we use this image in any position in the documents, we just use `/img/a.jpg`. (The document sync bot can copy `images` path into `img` folder in `serverlessllm.github.io` repo)
+Images are stored in the `images` directory alongside the documentation files. This ensures that each documentation version has its own images, preventing version conflicts.
+
+**Usage:**
+- Store images in: `docs/images/` (for Latest) or `versioned_docs/version-X.X.X/images/` (for versioned docs)
+- Reference images using relative paths: `./images/filename.jpg`
+
+**Examples:**
+```markdown
+<!-- Markdown syntax (no width control) -->
+![Alt text](./images/a.jpg)
+
+<!-- HTML syntax with sizing (use require for Docusaurus) -->
+<img src={require('./images/a.jpg').default} alt="Description" width="256px"/>
+```
+
+When you create a new documentation version using `npm run docusaurus docs:version X.X.X`, the images directory is automatically copied with the versioned docs.

docs/intro.md

@@ -5,7 +5,7 @@ sidebar_position: 0
 # Serverless LLM
 
 <!-- Scaled logo -->
-<img src="/img/serverlessllm.jpg" alt="ServerlessLLM" width="256px"/>
+<img src={require('./images/serverlessllm.jpg').default} alt="ServerlessLLM" width="256px"/>
 
 ServerlessLLM is a **fast** and **easy-to-use** serving system designed for **affordable** multi-LLM serving, also known as LLM-as-a-Service. ServerlessLLM is ideal for environments with multiple LLMs that need to be served on limited GPU resources, as it enables efficient dynamic loading of LLMs onto GPUs. By elastically scaling model instances and multiplexing GPUs, ServerlessLLM can significantly reduce costs compared to traditional GPU-dedicated serving systems while still providing low-latency (Time-to-First-Token, TTFT) LLM completions.
 

versioned_docs/version-0.7.0/README.md

@@ -36,4 +36,19 @@ This command generates static content into the `build` directory, which can be s
 
 ### About the image path
 
-Images are stored in `images` path. For example, we have an image called `a.jpg` in `images`. When we use this image in any position in the documents, we just use `/img/a.jpg`. (The document sync bot can copy `images` path into `img` folder in `serverlessllm.github.io` repo)
+Images are stored in the `images` directory alongside the documentation files. This ensures that each documentation version has its own images, preventing version conflicts.
+
+**Usage:**
+- Store images in: `docs/images/` (for Latest) or `versioned_docs/version-X.X.X/images/` (for versioned docs)
+- Reference images using relative paths: `./images/filename.jpg`
+
+**Examples:**
+```markdown
+<!-- Markdown syntax (no width control) -->
+![Alt text](./images/a.jpg)
+
+<!-- HTML syntax with sizing (use require for Docusaurus) -->
+<img src={require('./images/a.jpg').default} alt="Description" width="256px"/>
+```
+
+When you create a new documentation version using `npm run docusaurus docs:version X.X.X`, the images directory is automatically copied with the versioned docs.

versioned_docs/version-0.7.0/intro.md

@@ -5,7 +5,7 @@ sidebar_position: 0
 # Serverless LLM
 
 <!-- Scaled logo -->
-<img src="/img/serverlessllm.jpg" alt="ServerlessLLM" width="256px"/>
+<img src={require('./images/serverlessllm.jpg').default} alt="ServerlessLLM" width="256px"/>
 
 ServerlessLLM is a **fast** and **easy-to-use** serving system designed for **affordable** multi-LLM serving, also known as LLM-as-a-Service. ServerlessLLM is ideal for environments with multiple LLMs that need to be served on limited GPU resources, as it enables efficient dynamic loading of LLMs onto GPUs. By elastically scaling model instances and multiplexing GPUs, ServerlessLLM can significantly reduce costs compared to traditional GPU-dedicated serving systems while still providing low-latency (Time-to-First-Token, TTFT) LLM completions.
 

versioned_docs/version-0.8.0/README.md

@@ -36,4 +36,19 @@ This command generates static content into the `build` directory, which can be s
 
 ### About the image path
 
-Images are stored in `images` path. For example, we have an image called `a.jpg` in `images`. When we use this image in any position in the documents, we just use `/img/a.jpg`. (The document sync bot can copy `images` path into `img` folder in `serverlessllm.github.io` repo)
+Images are stored in the `images` directory alongside the documentation files. This ensures that each documentation version has its own images, preventing version conflicts.
+
+**Usage:**
+- Store images in: `docs/images/` (for Latest) or `versioned_docs/version-X.X.X/images/` (for versioned docs)
+- Reference images using relative paths: `./images/filename.jpg`
+
+**Examples:**
+```markdown
+<!-- Markdown syntax (no width control) -->
+![Alt text](./images/a.jpg)
+
+<!-- HTML syntax with sizing (use require for Docusaurus) -->
+<img src={require('./images/a.jpg').default} alt="Description" width="256px"/>
+```
+
+When you create a new documentation version using `npm run docusaurus docs:version X.X.X`, the images directory is automatically copied with the versioned docs.

versioned_docs/version-0.8.0/intro.md

@@ -5,7 +5,7 @@ sidebar_position: 0
 # Serverless LLM
 
 <!-- Scaled logo -->
-<img src="/img/serverlessllm.jpg" alt="ServerlessLLM" width="256px"/>
+<img src={require('./images/serverlessllm.jpg').default} alt="ServerlessLLM" width="256px"/>
 
 ServerlessLLM is a **fast** and **easy-to-use** serving system designed for **affordable** multi-LLM serving, also known as LLM-as-a-Service. ServerlessLLM is ideal for environments with multiple LLMs that need to be served on limited GPU resources, as it enables efficient dynamic loading of LLMs onto GPUs. By elastically scaling model instances and multiplexing GPUs, ServerlessLLM can significantly reduce costs compared to traditional GPU-dedicated serving systems while still providing low-latency (Time-to-First-Token, TTFT) LLM completions.