Previously emails with url special characters would not get
successfully identified for login. Account creation was fine due to
email being in POST request body. But login with such emails did not
work due to query params not being escaped before being sent to server
This change escapes both the code and email in login URL sent to
server. So login with emails containing special characters like
`email+khoj@gmail.com' works. It fixes both the URL web app sent by
web app directly and the magic link sent to users to their email
This change also fixes accessibility issue of having a DialogTitle in
DialogContent for screen readers.
Resolves#1090
We've been having issues generating diagrams with Excalidraw that are any degree of complexity. By contrast, LLMs are able to handle Mermaid.js syntax a lot better, as it's much more forgiving and has a simpler declarative style. Refer to https://mermaid.js.org/.
Update so that new diagrams are generated with Mermaid.js, while old diagrams generated with Excalidraw can still be viewed.
Fix for #1082 pushed down adding the `data:image/webp;base64' prefix
of the base64 images to the server image gen API. But the code on the
Obsidian and Desktop client still add these prefixes.
This change stops the Desktop, Obsidian clients from adding the prefix
as it is being handled by the API now. It should resolve showing
images inline in those clients as well
- Background
Access to the clipboard API is disabled by certain browsers in non
localhost http scenarios for security reasons.
So the copy API key button doesn't work when khoj is self-hosted
with authentication enabled at a non localhost domain
- Change
This change enables copying API keys by manual text highlight + copy
if copy button is disabled
Resolves#1070
- Add the mermaid package and apply front-end parsing for interpreting the diagrams. Retain processing of the excalidraw type for backwards compatibility
- Translated comments from French to English for better accessibility and understanding.
- Updated CSS comment for loading animation to reflect the change in language.
- Enhanced code readability by ensuring consistent language usage across multiple files.
- Improved user experience by clarifying the purpose of various functions and settings in the codebase.
- Added visual loading indicators to the search modal for improved user experience during search operations.
- Implemented logic to check if search results correspond to files in the vault, with color-coded results for better clarity.
- Refactored the getSuggestions method to handle loading states and abort previous requests if necessary.
- Updated CSS styles to support new loading animations and result file status indicators.
- Improved the renderSuggestion method to display file status and provide feedback for files not in the vault.
- Added a new setting for users to configure the sync interval in minutes, allowing for more flexible automatic synchronization.
- Introduced methods to start and restart the synchronization timer based on the configured interval.
- Updated the synchronization logic to use the user-defined interval instead of a fixed 60 minutes.
- Improved code readability and organization by refactoring the sync timer logic.
- Added a new setting to manage sync folders, allowing users to specify which folders to sync or to sync the entire vault.
- Implemented a modal for folder suggestions to facilitate folder selection.
- Updated the folder list display to show currently selected folders with options to remove them.
- Improved CSS styles for chat interface and folder list for better user experience.
- Refactored code for consistency and readability across multiple files.
- Introduced a new command 'Sync new changes' to allow users to manually synchronize new modifications.
- The command updates the content index without regenerating it, ensuring only new changes are synced.
- User-triggered notifications are displayed upon successful sync.
This tries to decouple the automation query from the chat query. So
the chat model doesn't have to know it is running in an automation
context or figure how to notify user or send automation response. It
just has to respond to the AI generated `query_to_run' corresponding
to the `scheduling_request` automation by the user.
For example, a `scheduling_request' of `notify me when X happens'
results in the automation calling the chat api with a `query_to_run`
like `tell me about X` and deciding if to notify based on information
gathered about X from the scheduled run. If these two are not
decoupled, the chat model may respond with how it can notify about X
instead of just asking about it.
Swap query_to_run with scheduling_request on the automation web page
