feat: Update sidebar link to Langflow workspace

docs/docs/tutorials/chatprompttemplate_guide.mdx

@@ -62,7 +62,7 @@ In this guide, we will modify the "Basic Chat with Prompt and History" example,
 
 16. You should now be able to see and use the defined variables in the chat interface.
 
-17. Click on 'role' to examine the variable you established in the canvas.
+17. Click on 'role' to examine the variable you established in the workspace.
 
 18. Now, let's define the 'behavior' variable.
 

docs/docs/tutorials/custom_components.mdx

@@ -196,7 +196,7 @@ Let's create a custom component that processes a document (_`langchain.schema.Do
 
 ### Pick a display name
 
-To start, let's choose a name for our component by adding a _`display_name`_ attribute. This name will appear on the canvas. The name of the class is not relevant, but let's call it _`DocumentProcessor`_.
+To start, let's choose a name for our component by adding a _`display_name`_ attribute. This name will appear on the workspace. The name of the class is not relevant, but let's call it _`DocumentProcessor`_.
 
 ```python
 from langflow.custom import CustomComponent
@@ -267,7 +267,7 @@ class DocumentProcessor(CustomComponent):
 
 Here, the build method takes two input parameters: _`document`_, representing the input document to be processed, and _`function`_, a string representing the selected text transformation to be applied (either "Uppercase," "Lowercase," or "Titlecase"). The method processes the text content of the input Document based on the selected function.
 
-The attribute _`repr_value`_ is used to display the result of the component on the canvas. It is optional and can be used to display any string value.
+The attribute _`repr_value`_ is used to display the result of the component on the workspace. It is optional and can be used to display any string value.
 
 The return type is _`Document`_.
 
@@ -399,6 +399,6 @@ Langflow will attempt to load all of the components found in the specified direc
 
 ### Interact with Custom Components
 
-Once your custom components have been loaded successfully, they will appear in Langflow's sidebar. From there, you can add them to your Langflow canvas for use. However, please note that components with errors will not be available for addition to the canvas. Always ensure your code is error-free before attempting to load components.
+Once your custom components have been loaded successfully, they will appear in Langflow's sidebar. From there, you can add them to your Langflow workspace for use. However, please note that components with errors will not be available for addition to the workspace. Always ensure your code is error-free before attempting to load components.
 
 Remember, creating custom components allows you to extend the functionality of Langflow to better suit your unique needs. Happy coding!

docs/docs/tutorials/loading_document.mdx

@@ -39,7 +39,7 @@ This guide takes you through the process of augmenting the "Basic Chat with Prom
 
 6. Next, open the search bar and type "web".
 
-7. Drag and drop a WebBaseLoader (or any other loader of your choice) onto the canvas.
+7. Drag and drop a WebBaseLoader (or any other loader of your choice) onto the workspace.
 
 8. Connect this loader to the `{context}` variable that we just added.
 

docs/docs/tutorials/rag-with-astradb.mdx

@@ -23,7 +23,7 @@ TLDR;
 - Duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
 - Create a new database, get a **Token** and the **API Endpoint**
 - Click on the **New Project** button and look for Vector Store RAG. This will create a new project with the necessary components
-- Import the project into Langflow by dropping it on the Canvas or My Collection page
+- Import the project into Langflow by dropping it on the Workspace or My Collection page
 - Update the **Token** and **API Endpoint** in the **Astra DB** components
 - Update the OpenAI API key in the **OpenAI** components
 - Run the ingestion flow which is the one that uses the **Astra DB** component