docs: update docs icons (#12465)

### What problem does this PR solve?

Update icons for docs.
Trailing spaces are auto truncated by the editor, does not affect real
content.

### Type of change

- [x] Documentation Update

docs/guides/models/_category_.json

@@ -4,5 +4,8 @@
   "link": {
     "type": "generated-index",
     "description": "Guides on model settings."
+  },
+  "customProps": {
+    "categoryIcon": "LucideBox"
   }
 }

docs/guides/models/deploy_local_llm.mdx

@@ -1,6 +1,9 @@
 ---
 sidebar_position: 2
 slug: /deploy_local_llm
+sidebar_custom_props: {
+  categoryIcon: LucideMonitorCog
+}
 ---
 
 # Deploy local models
@@ -53,9 +56,9 @@ $ sudo docker exec ollama ollama pull llama3.2
 ```
 
 ```bash
-$ sudo docker exec ollama ollama pull bge-m3                 
-> pulling daec91ffb5dd... 100% ▕████████████████▏ 1.2 GB                                  
-> success 
+$ sudo docker exec ollama ollama pull bge-m3
+> pulling daec91ffb5dd... 100% ▕████████████████▏ 1.2 GB
+> success
 ```
 
 ### 2. Find Ollama URL and ensure it is accessible
@@ -105,7 +108,7 @@ Max retries exceeded with url: /api/chat (Caused by NewConnectionError('<urllib3
 ### 5. Update System Model Settings
 
 Click on your logo **>** **Model providers** **>** **System Model Settings** to update your model:
-   
+
 - *You should now be able to find **llama3.2** from the dropdown list under **Chat model**, and **bge-m3** from the dropdown list under **Embedding model**.*
 
 ### 6. Update Chat Configuration
@@ -125,7 +128,7 @@ To deploy a local model, e.g., **Mistral**, using Xinference:
 
 ### 1. Check firewall settings
 
-Ensure that your host machine's firewall allows inbound connections on port 9997. 
+Ensure that your host machine's firewall allows inbound connections on port 9997.
 
 ### 2. Start an Xinference instance
 
@@ -148,13 +151,13 @@ In RAGFlow, click on your logo on the top right of the page **>** **Model provid
 
 ### 5. Complete basic Xinference settings
 
-Enter an accessible base URL, such as `http://<your-xinference-endpoint-domain>:9997/v1`. 
+Enter an accessible base URL, such as `http://<your-xinference-endpoint-domain>:9997/v1`.
 > For rerank model, please use the `http://<your-xinference-endpoint-domain>:9997/v1/rerank` as the base URL.
 
 ### 6. Update System Model Settings
 
 Click on your logo **>** **Model providers** **>** **System Model Settings** to update your model.
-   
+
 *You should now be able to find **mistral** from the dropdown list under **Chat model**.*
 
 ### 7. Update Chat Configuration
@@ -170,7 +173,7 @@ To deploy a local model, e.g., **Qwen2**, using IPEX-LLM-accelerated Ollama:
 ### 1. Check firewall settings
 
 Ensure that your host machine's firewall allows inbound connections on port 11434. For example:
-   
+
 ```bash
 sudo ufw allow 11434/tcp
 ```
@@ -179,7 +182,7 @@ sudo ufw allow 11434/tcp
 
 #### 2.1 Install IPEX-LLM for Ollama
 
-:::tip NOTE 
+:::tip NOTE
 IPEX-LLM's supports Ollama on Linux and Windows systems.
 :::
 
@@ -191,7 +194,7 @@ For detailed information about installing IPEX-LLM for Ollama, see [Run llama.cp
 
 #### 2.2 Initialize Ollama
 
-1. Activate the `llm-cpp` Conda environment and initialize Ollama: 
+1. Activate the `llm-cpp` Conda environment and initialize Ollama:
 
 <Tabs
   defaultValue="linux"
@@ -200,7 +203,7 @@ For detailed information about installing IPEX-LLM for Ollama, see [Run llama.cp
     {label: 'Windows', value: 'windows'},
   ]}>
   <TabItem value="linux">
-  
+
   ```bash
   conda activate llm-cpp
   init-ollama
@@ -218,7 +221,7 @@ For detailed information about installing IPEX-LLM for Ollama, see [Run llama.cp
 </Tabs>
 
 2. If the installed `ipex-llm[cpp]` requires an upgrade to the Ollama binary files, remove the old binary files and reinitialize Ollama using `init-ollama` (Linux) or `init-ollama.bat` (Windows).
-   
+
    *A symbolic link to Ollama appears in your current directory, and you can use this executable file following standard Ollama commands.*
 
 #### 2.3 Launch Ollama service
@@ -226,7 +229,7 @@ For detailed information about installing IPEX-LLM for Ollama, see [Run llama.cp
 1. Set the environment variable `OLLAMA_NUM_GPU` to `999` to ensure that all layers of your model run on the Intel GPU; otherwise, some layers may default to CPU.
 2. For optimal performance on Intel Arc™ A-Series Graphics with Linux OS (Kernel 6.2), set the following environment variable before launching the Ollama service:
 
-   ```bash 
+   ```bash
    export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
    ```
 3. Launch the Ollama service:
@@ -314,12 +317,12 @@ To enable IPEX-LLM accelerated Ollama in RAGFlow, you must also complete the con
 3. [Update System Model Settings](#6-update-system-model-settings)
 4. [Update Chat Configuration](#7-update-chat-configuration)
 
-### 5. Deploy VLLM 
+### 5. Deploy VLLM
 
 ubuntu 22.04/24.04
 
 ```bash
-  pip install vllm 
+  pip install vllm
   ```
 ### 5.1 RUN VLLM WITH BEST PRACTISE
 

docs/guides/models/llm_api_key_setup.md

@@ -1,6 +1,9 @@
 ---
 sidebar_position: 1
 slug: /llm_api_key_setup
+sidebar_custom_props: {
+  categoryIcon: LucideKey
+}
 ---
 
 # Configure model API key
@@ -30,7 +33,7 @@ You have two options for configuring your model API key:
    - Update `api_key` with yours.
    - Update `base_url` if you use a proxy to connect to the remote service.
 3. Reboot your system for your changes to take effect.
-4. Log into RAGFlow.  
+4. Log into RAGFlow.
    _After logging into RAGFlow, you will find your chosen model appears under **Added models** on the **Model providers** page._
 
 ### Configure model API key after logging into RAGFlow