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.
- 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
- One current issue in the Khoj application is that managing the files being referenced as the user's knowledge base is slightly opaque and difficult to access
- Add a migration for associating the fileobjects directly with the Entry objects, making it easier to get data via foreign key
- Add the new page that shows all indexed files in the search view, also allowing you to upload new docs directly from that page
- Support new APIs for getting / deleting files
- Add the mermaid package and apply front-end parsing for interpreting the diagrams. Retain processing of the excalidraw type for backwards compatibility
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
