Markdownlit corrections in formatting

Markdownlit corrections in formatting

docs/features/banners.md

@@ -37,9 +37,9 @@ Environment Variable Description
 ---------------------------------
 
 * `WEBUI_BANNERS`:
-	+ Type: list of dict
-	+ Default: `[]`
-	+ Description: List of banners to show to users.
+  * Type: list of dict
+  * Default: `[]`
+  * Description: List of banners to show to users.
 
 Banner Options
 ----------------

docs/features/chat-params.md

@@ -59,9 +59,9 @@ Suppose a user wants to set their own system prompt for their account. They can
 :::tip **Bonus Tips**
 **This tip applies for both administrators and user accounts. To achieve maximum flexibility with your system prompts, we recommend considering the following setup:**
 
-* Assign your primary System Prompt (**i.e., to give an LLM a defining character**) you want to use in your **General** settings **System Prompt** field. This sets it on a per-account level and allows it to work as the system prompt across all your LLMs without requiring adjustments within a model from the **Workspace** section.
+- Assign your primary System Prompt (**i.e., to give an LLM a defining character**) you want to use in your **General** settings **System Prompt** field. This sets it on a per-account level and allows it to work as the system prompt across all your LLMs without requiring adjustments within a model from the **Workspace** section.
 
-* For your secondary System Prompt (**i.e, to give an LLM a task to perform**), choose whether to place it in the **System Prompt** field within the **Chat Controls** sidebar (on a per-chat basis) or the **Models** section of the **Workspace** section (on a per-model basis) for Admins, allowing you to set them directly. This allows your account-level system prompt to work in conjunction with either the per-chat level system prompt provided by **Chat Controls**, or the per-model level system prompt provided by **Models**.
+- For your secondary System Prompt (**i.e, to give an LLM a task to perform**), choose whether to place it in the **System Prompt** field within the **Chat Controls** sidebar (on a per-chat basis) or the **Models** section of the **Workspace** section (on a per-model basis) for Admins, allowing you to set them directly. This allows your account-level system prompt to work in conjunction with either the per-chat level system prompt provided by **Chat Controls**, or the per-model level system prompt provided by **Models**.
 
-* As an administrator, you should assign your LLM parameters on a per-model basis using **Models** section for optimal flexibility. For both of these secondary System Prompts, ensure to set them in a manner that maximizes flexibility and minimizes required adjustments across different per-account or per-chat instances. It is essential for both your Admin account as well as all User accounts to understand the priority order by which system prompts within **Chat Controls** and the **Models** section will be applied to the **LLM**.
+- As an administrator, you should assign your LLM parameters on a per-model basis using **Models** section for optimal flexibility. For both of these secondary System Prompts, ensure to set them in a manner that maximizes flexibility and minimizes required adjustments across different per-account or per-chat instances. It is essential for both your Admin account as well as all User accounts to understand the priority order by which system prompts within **Chat Controls** and the **Models** section will be applied to the **LLM**.
 :::

docs/features/conversation-organization.md

@@ -24,7 +24,7 @@ If you are managing multiple projects, you can create separate folders for each
 
 Tags provide an additional layer of organization by allowing you to label conversations with keywords or phrases.
 
-- **Adding Tags to Conversations**: Tags can be applied to conversations based on their content or purpose. Tags are flexible and can be added or removed as needed. 
+- **Adding Tags to Conversations**: Tags can be applied to conversations based on their content or purpose. Tags are flexible and can be added or removed as needed.
 ![Tag Demo](/img/tag-demo.gif)
 - **Using Tags for Searching**: Tags make it easy to locate specific conversations by using the search feature. You can filter conversations by tags to quickly find those related to specific topics.
 

docs/features/evaluation/index.mdx

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 2
+sidebar_position: 4
 title: "📝 Evaluation"
 ---
 

docs/features/images.md

@@ -15,10 +15,13 @@ Open WebUI supports image generation through the **AUTOMATIC1111** [API](https:/
 
 1. Ensure that you have [AUTOMATIC1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) installed.
 2. Launch AUTOMATIC1111 with additional flags to enable API access:
+
    ```
    ./webui.sh --api --listen
    ```
+
 3. For Docker installation of WebUI with the environment variables preset, use the following command:
+
    ```
    docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -e AUTOMATIC1111_BASE_URL=http://host.docker.internal:7860/ -e ENABLE_IMAGE_GENERATION=True -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
    ```
@@ -28,9 +31,11 @@ Open WebUI supports image generation through the **AUTOMATIC1111** [API](https:/
 1. In Open WebUI, navigate to the **Admin Panel** > **Settings** > **Images** menu.
 2. Set the `Image Generation Engine` field to `Default (Automatic1111)`.
 3. In the API URL field, enter the address where AUTOMATIC1111's API is accessible:
+
    ```
    http://<your_automatic1111_address>:7860/
    ```
+
    If you're running a Docker installation of Open WebUI and AUTOMATIC1111 on the same host, use `http://host.docker.internal:7860/` as your address.
 
 ## ComfyUI
@@ -41,34 +46,46 @@ ComfyUI provides an alternative interface for managing and interacting with imag
 
 1. Download and extract the ComfyUI software package from [GitHub](https://github.com/comfyanonymous/ComfyUI) to your desired directory.
 2. To start ComfyUI, run the following command:
+
    ```
    python main.py
    ```
+
    For systems with low VRAM, launch ComfyUI with additional flags to reduce memory usage:
+
    ```
    python main.py --lowvram
    ```
+
 3. For Docker installation of WebUI with the environment variables preset, use the following command:
+
    ```
    docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -e COMFYUI_BASE_URL=http://host.docker.internal:7860/ -e ENABLE_IMAGE_GENERATION=True -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
    ```
 
 ### Setting Up Open WebUI with ComfyUI
 
-#### Setting Up FLUX.1 Models:
+#### Setting Up FLUX.1 Models
 
 1. **Model Checkpoints**:
-	* Download either the `FLUX.1-schnell` or `FLUX.1-dev` model from the [black-forest-labs HuggingFace page](https://huggingface.co/black-forest-labs).
-	* Place the model checkpoint(s) in both the `models/checkpoints` and `models/unet` directories of ComfyUI. Alternatively, you can create a symbolic link between `models/checkpoints` and `models/unet` to ensure both directories contain the same model checkpoints.
+
+* Download either the `FLUX.1-schnell` or `FLUX.1-dev` model from the [black-forest-labs HuggingFace page](https://huggingface.co/black-forest-labs).
+* Place the model checkpoint(s) in both the `models/checkpoints` and `models/unet` directories of ComfyUI. Alternatively, you can create a symbolic link between `models/checkpoints` and `models/unet` to ensure both directories contain the same model checkpoints.
+
 2. **VAE Model**:
-	* Download `ae.safetensors` VAE from [here](https://huggingface.co/black-forest-labs/FLUX.1-schnell/blob/main/ae.safetensors).
-	* Place it in the `models/vae` ComfyUI directory.
+
+* Download `ae.safetensors` VAE from [here](https://huggingface.co/black-forest-labs/FLUX.1-schnell/blob/main/ae.safetensors).
+* Place it in the `models/vae` ComfyUI directory.
+
 3. **CLIP Model**:
-	* Download `clip_l.safetensors` from [here](https://huggingface.co/comfyanonymous/flux_text_encoders/tree/main).
-	* Place it in the `models/clip` ComfyUI directory.
+
+* Download `clip_l.safetensors` from [here](https://huggingface.co/comfyanonymous/flux_text_encoders/tree/main).
+* Place it in the `models/clip` ComfyUI directory.
+
 4. **T5XXL Model**:
-	* Download either the `t5xxl_fp16.safetensors` or `t5xxl_fp8_e4m3fn.safetensors` model from [here](https://huggingface.co/comfyanonymous/flux_text_encoders/tree/main).
-	* Place it in the `models/clip` ComfyUI directory.
+
+* Download either the `t5xxl_fp16.safetensors` or `t5xxl_fp8_e4m3fn.safetensors` model from [here](https://huggingface.co/comfyanonymous/flux_text_encoders/tree/main).
+* Place it in the `models/clip` ComfyUI directory.
 
 To integrate ComfyUI into Open WebUI, follow these steps:
 
@@ -77,8 +94,8 @@ To integrate ComfyUI into Open WebUI, follow these steps:
 1. Navigate to the **Admin Panel** in Open WebUI.
 2. Click on **Settings** and then select the **Images** tab.
 3. In the `Image Generation Engine` field, choose `ComfyUI`.
-4. In the **API URL** field, enter the address where ComfyUI's API is accessible, following this format: `http://<your_comfyui_address>:8188/`. 
-   - Set the environment variable `COMFYUI_BASE_URL` to this address to ensure it persists within the WebUI.
+4. In the **API URL** field, enter the address where ComfyUI's API is accessible, following this format: `http://<your_comfyui_address>:8188/`.
+   * Set the environment variable `COMFYUI_BASE_URL` to this address to ensure it persists within the WebUI.
 
 #### Step 2: Verify the Connection and Enable Image Generation
 
@@ -93,6 +110,7 @@ To integrate ComfyUI into Open WebUI, follow these steps:
 3. Return to Open WebUI and click the **Click here to upload a workflow.json file** button.
 4. Select the `workflow_api.json` file to import the exported workflow from ComfyUI into Open WebUI.
 5. After importing the workflow, you must map the `ComfyUI Workflow Nodes` according to the imported workflow node IDs.
+
 :::info
 You may need to adjust an `Input Key` or two within Open WebUI's `ComfyUI Workflow Nodes` section to match a node within your workflow.
 For example, `seed` may need to be renamed to `noise_seed` to match a node ID within your imported workflow.
@@ -100,16 +118,18 @@ For example, `seed` may need to be renamed to `noise_seed` to match a node ID wi
 :::tip
 Some workflows, such as ones that use any of the Flux models, may utilize multiple nodes IDs that is necessary to fill in for their node entry fields within Open WebUI. If a node entry field requires multiple IDs, the node IDs should be comma separated (e.g. `1` or `1, 2`).
 :::
+
 6. Click `Save` to apply the settings and enjoy image generation with ComfyUI integrated into Open WebUI!
 
 After completing these steps, your ComfyUI setup should be integrated with Open WebUI, and you can use the Flux.1 models for image generation.
 
 ### Configuring with SwarmUI
 
-SwarmUI utilizes ComfyUI as its backend. In order to get Open WebUI to work with SwarmUI you will have to append `ComfyBackendDirect` to the `ComfyUI Base URL`. Additionally, you will want to setup SwarmUI with LAN access. After aforementioned adjustments, setting up SwarmUI to work with Open WebUI will be the same as [Step one: Configure Open WebUI Settings](https://github.com/open-webui/docs/edit/main/docs/features/images.md#step-1-configure-open-webui-settings) as outlined above. 
+SwarmUI utilizes ComfyUI as its backend. In order to get Open WebUI to work with SwarmUI you will have to append `ComfyBackendDirect` to the `ComfyUI Base URL`. Additionally, you will want to setup SwarmUI with LAN access. After aforementioned adjustments, setting up SwarmUI to work with Open WebUI will be the same as [Step one: Configure Open WebUI Settings](https://github.com/open-webui/docs/edit/main/docs/features/images.md#step-1-configure-open-webui-settings) as outlined above.
 ![Install SwarmUI with LAN Access](https://github.com/user-attachments/assets/a6567e13-1ced-4743-8d8e-be526207f9f6)
 
 #### SwarmUI API URL
+
 The address you will input as the ComfyUI Base URL will look like: `http://<your_swarmui_address>:7801/ComfyBackendDirect`
 
 ## OpenAI DALL·E
@@ -126,8 +146,8 @@ Open WebUI also supports image generation through the **OpenAI DALL·E APIs**. T
 2. Set the `Image Generation Engine` field to `Open AI (Dall-E)`.
 3. Enter your OpenAI API key.
 4. Choose the DALL·E model you wish to use. Note that image size options will depend on the selected model:
-   - **DALL·E 2**: Supports `256x256`, `512x512`, or `1024x1024` images.
-   - **DALL·E 3**: Supports `1024x1024`, `1792x1024`, or `1024x1792` images.
+   * **DALL·E 2**: Supports `256x256`, `512x512`, or `1024x1024` images.
+   * **DALL·E 3**: Supports `1024x1024`, `1792x1024`, or `1024x1792` images.
 
 ### Azure OpenAI
 
@@ -142,9 +162,7 @@ Using Azure OpenAI Dall-E directly is unsupported, but you can [set up a LiteLLM
 3. After the image has finished generating, it will be returned automatically in chat.
 
 :::tip
-
     You can also edit the LLM's response and enter your image generation prompt as the message
     to send off for image generation instead of using the actual response provided by the
     LLM.
-
 :::

docs/features/index.mdx

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 2
+sidebar_position: 400
 title: "⭐ Features"
 ---
 

docs/features/jwttoken.md

@@ -36,14 +36,14 @@ Environment Variable Descriptions
 ---------------------------------
 
 * `JWT_EXPIRES_IN`:
-	+ Type: int
-	+ Default: -1
-	+ Description: Sets the JWT expiration time in seconds. A value of -1 disables expiration.
+  * Type: int
+  * Default: -1
+  * Description: Sets the JWT expiration time in seconds. A value of -1 disables expiration.
 * `WEBUI_SECRET_KEY`:
-	+ Type: str
-	+ Default: t0p-s3cr3t
-	+ Docker Default: Randomly generated on first start
-	+ Description: Overrides the randomly generated string used for JSON Web Token.
+  * Type: str
+  * Default: t0p-s3cr3t
+  * Docker Default: Randomly generated on first start
+  * Description: Overrides the randomly generated string used for JSON Web Token.
 
 Valid Time Units
 -----------------

docs/features/openai.md

@@ -8,6 +8,7 @@ In this tutorial, we will demonstrate how to configure multiple OpenAI (or compa
 ## Docker Run
 
 Here's an example `docker run` command similar to what you might use for Open WebUI:
+
 ```bash
 docker run -d -p 3000:8080 \
   -v open-webui:/app/backend/data \
@@ -17,6 +18,7 @@ docker run -d -p 3000:8080 \
   --restart always \
   ghcr.io/open-webui/open-webui:main
 ```
+
 This command sets the following environment variables:
 
 * `OPENAI_API_BASE_URLS`: A list of API base URLs separated by semicolons (;). In this example, we use OpenAI and Mistral.
@@ -27,6 +29,7 @@ You can adapt this command to your own needs, and add even more endpoint/key pai
 ## Docker Compose
 
 Alternatively, you can use a `docker-compose.yaml` file to define and run the Open WebUI container. Here's an abridged version of what that might look like:
+
 ```yaml
 services:
   open-webui:
@@ -34,7 +37,9 @@ services:
       - 'OPENAI_API_BASE_URLS=${OPENAI_API_BASE_URLS}'
       - 'OPENAI_API_KEYS=${OPENAI_API_KEYS}'
 ```
+
 You can edit the `${VARIABLES}` directly, or optionally define the values of these variables in an `.env` file, which should be placed in the same directory as the `docker-compose.yaml` file:
+
 ```ini
 OPENAI_API_BASE_URLS="https://api.openai.com/v1;https://api.mistral.ai/v1"
 OPENAI_API_KEYS="<OPENAI_API_KEY_1>;<OPENAI_API_KEY_2>"

docs/features/playground(beta)/chat.md

@@ -0,0 +1,4 @@
+---
+sidebar_position: 0
+title: "💬 Chat"
+---

docs/features/playground(beta)/completions.md

@@ -0,0 +1,4 @@
+---
+sidebar_position: 1
+title: "✅ Completions"
+---

docs/features/playground(beta)/index.mdx

@@ -0,0 +1,8 @@
+---
+sidebar_position: 1
+title: "🛝 Playground (Beta)"
+---
+
+import { TopBanners } from "@site/src/components/TopBanners";
+
+<TopBanners />

docs/features/plugin/functions/actions.md

@@ -3,10 +3,9 @@ sidebar_position: 6
 title: "Actions"
 ---
 
-# Actions
-Action functions allow you to write custom buttons to the message toolbar for end users to interact 
-with. This feature enables more interactive messaging, enabling users to grant permission before a 
-task is performed, generate visualizations of structured data, download an audio snippet of chats, 
+Action functions allow you to write custom buttons to the message toolbar for end users to interact
+with. This feature enables more interactive messaging, enabling users to grant permission before a
+task is performed, generate visualizations of structured data, download an audio snippet of chats,
 and many other use cases.
 
 A scaffold of Action code can be found [in the community section](https://openwebui.com/f/hub/custom_action/).

docs/features/rag.md

@@ -3,8 +3,6 @@ sidebar_position: 4
 title: "Retrieval Augmented Generation (RAG)"
 ---
 
-# Retrieval Augmented Generation (RAG)
-
 Retrieval Augmented Generation (RAG) is a a cutting-edge technology that enhances the conversational capabilities of chatbots by incorporating context from diverse sources. It works by retrieving relevant information from a wide range of sources such as local and remote documents, web content, and even multimedia sources like YouTube videos. The retrieved text is then combined with a predefined RAG template and prefixed to the user's prompt, providing a more informed and contextually relevant response.
 
 One of the key advantages of RAG is its ability to access and integrate information from a variety of sources, making it an ideal solution for complex conversational scenarios. For instance, when a user asks a question related to a specific document or web page, RAG can retrieve and incorporate the relevant information from that source into the chat response. RAG can also retrieve and incorporate information from multimedia sources like YouTube videos. By analyzing the transcripts or captions of these videos, RAG can extract relevant information and incorporate it into the chat response.

docs/features/url-params.md

@@ -24,7 +24,7 @@ The following table lists the available URL parameters, their function, and exam
 
 - **Description**: The `models` and `model` parameters allow you to specify which [language models](/features/workspace/models.md) should be used for a particular chat session.
 - **How to Set**: You can use either `models` for multiple models or `model` for a single model.
-- **Example**: 
+- **Example**:
   - `/?models=model1,model2` – This initializes the chat with `model1` and `model2`.
   - `/?model=model1` – This sets `model1` as the sole model for the chat.
 
@@ -80,15 +80,15 @@ Suppose a user wants to initiate a quick chat session without saving the history
 ## Using Multiple Parameters Together
 
 These URL parameters can be combined to create highly customized chat sessions. For example:
+
 ```bash
 /chat?models=model1,model2&youtube=VIDEO_ID&web-search=true&tools=tool1,tool2&call=true&q=Hello%20there&temporary-chat=true
 ```
 
 This URL will:
+
 - Initialize the chat with `model1` and `model2`.
 - Enable YouTube transcription, web search, and specified tools.
 - Display a call overlay.
 - Set an initial prompt of "Hello there."
 - Mark the chat as temporary, avoiding any history saving.
-
-

docs/features/usergroups/assign-user.md

@@ -0,0 +1,4 @@
+---
+sidebar_position: 1
+title: "👨‍👧‍👦 Assign Users to Usergroup"
+---

docs/features/usergroups/index.mdx

@@ -0,0 +1,4 @@
+---
+sidebar_position: 3
+title: "👨‍👧‍👦 User Groups"
+---

docs/features/usergroups/providing-access.md

@@ -0,0 +1,4 @@
+---
+sidebar_position: 2
+title: "👨‍👧‍👦 Provide User Group Access"
+---

docs/features/usergroups/usergroup-perms.md

@@ -0,0 +1,4 @@
+---
+sidebar_position: 0
+title: "👨‍👧‍👦 User Group Permissions"
+---

docs/features/whitelist.md

@@ -3,8 +3,6 @@ sidebar_position: 8
 title: "Model Whitelisting"
 ---
 
-# Model Whitelisting
-
 Open WebUI allows you to filter specific models for use in your instance. This feature is especially useful for administrators who want to control which models are available to users. Filtering can be done through the WebUI or by adding environment variables to the backend.
 
 ## Filtering via WebUI

docs/features/workspace/knowledge.md

@@ -1,10 +1,8 @@
 ---
-sidebar_position: 3
+sidebar_position: 1
 title: "🧠 Knowledge"
 ---
 
-# 🧠 Knowledge
-
  Knowledge part of Open WebUI is like a memory bank that makes your interactions even more powerful and context-aware. Let's break down what "Knowledge" really means in Open WebUI, how it works, and why it’s incredibly helpful for enhancing your experience.
 
 ## TL;DR
@@ -24,15 +22,17 @@ The **Knowledge section** is a storage area within Open WebUI where you can save
 Imagine you're working on a long-term project and want the system to remember certain parameters, settings, or even key notes about the project without having to remind it every time. Or perhaps, you want it to remember specific personal preferences for chats and responses. The Knowledge section is where you can store this kind of **persistent information** so that Open WebUI can reference it in future conversations, creating a more **coherent, personalized experience**.
 
 Some examples of what you might store in Knowledge:
+
 - Important project parameters or specific data points you’ll frequently reference.
 - Custom commands, workflows, or settings you want to apply.
 - Personal preferences, guidelines, or rules that Open WebUI can follow in every chat.
 
 ### How to Use Knowledge in Chats
 
-Accessing stored Knowledge in your chats is easy! By simply referencing what’s saved(using '#' before the name), Open WebUI can pull in data or follow specific guidelines that you’ve set up in the Knowledge section. 
+Accessing stored Knowledge in your chats is easy! By simply referencing what’s saved(using '#' before the name), Open WebUI can pull in data or follow specific guidelines that you’ve set up in the Knowledge section.
 
 For example:
+
 - When discussing a project, Open WebUI can automatically recall your specified project details.
 - It can apply custom preferences to responses, like formality levels or preferred phrasing.
 
@@ -52,5 +52,4 @@ Admins can add knowledge to the workspace, which users can access and use; howev
 - **Use Knowledge to keep the system aware** of important details, ensuring a personalized chat experience.
 - You can **directly reference Knowledge in chats** to bring in stored data whenever you need it using '#' + name of the knowlege.
 
-
 🌟 Remember, there’s always more to discover, so dive in and make Open WebUI truly your own!

docs/features/workspace/models.md

@@ -1,11 +1,8 @@
 ---
-sidebar_position: 16
-title: "Models"
+sidebar_position: 0
+title: "🤖 Models"
 ---
 
-**Models**
-=======================
-
 The `Models` section of the `Workspace` within Open WebUI is a powerful tool that allows you to create and manage custom models tailored to specific purposes. This section serves as a central hub for all your modelfiles, providing a range of features to edit, clone, share, export, and hide your models.
 
 ### Modelfile Management
@@ -39,7 +36,7 @@ The `Models` section also includes features for importing and exporting models:
 * **Import Models**: Use this button to import models from a .json file or other sources.
 * **Export Models**: Use this button to export all your modelfiles in a single .json file.
 
-To download models, navigate to the **Ollama Settings** in the Connections tab. 
+To download models, navigate to the **Ollama Settings** in the Connections tab.
 Alternatively, you can also download models directly by typing a command like `ollama run hf.co/[model creator]/[model name]` in the model selection dropdown.
 This action will create a button labeled "Pull [Model Name]" for downloading.
 
@@ -47,12 +44,12 @@ This action will create a button labeled "Pull [Model Name]" for downloading.
 
    **Example**: Switching between **Mistral**, **LLaVA**, and **GPT-3.5** in a Multi-Stage Task
 
-   - **Scenario**: A multi-stage conversation involves different task types, such as starting with a simple FAQ, interpreting an image, and then generating a creative response.
-   - **Reason for Switching**: The user can leverage each model's specific strengths for each stage:
-      - **Mistral** for general questions to reduce computation time and costs.
-      - **LLaVA** for visual tasks to gain insights from image-based data.
-      - **GPT-3.5** for generating more sophisticated and nuanced language output.
-   - **Process**: The user switches between models, depending on the task type, to maximize efficiency and response quality.
+* **Scenario**: A multi-stage conversation involves different task types, such as starting with a simple FAQ, interpreting an image, and then generating a creative response.
+* **Reason for Switching**: The user can leverage each model's specific strengths for each stage:
+  * **Mistral** for general questions to reduce computation time and costs.
+  * **LLaVA** for visual tasks to gain insights from image-based data.
+  * **GPT-3.5** for generating more sophisticated and nuanced language output.
+* **Process**: The user switches between models, depending on the task type, to maximize efficiency and response quality.
 
     **How To**:
     1. **Select the Model**: Within the chat interface, select the desired models from the model switcher dropdown. You can select up to two models simultaneously, and both responses will be generated. You can then navigate between them by using the back and forth arrows.

docs/features/workspace/prompts.md

@@ -0,0 +1,4 @@
+---
+sidebar_position: 2
+title: "📚 Prompts"
+---

docs/features/workspace/tools.md

@@ -0,0 +1,4 @@
+---
+sidebar_position: 3
+title: "🛠️ Tools"
+---