- Create a more dynamic reasoning agent that can evaluate information and understand what it doesn't know, making moves to get that information
- Lots of hacks and code that needs to be reversed later on before submission
Update chat actors to use user's chat model for train of thought. This
requires passing the user info as argument to all the chat actors.
Whether the user is subscribed or not can be inferred from the user
info being passed, so it doesn't need to be passed as a separate
argument to chat actor functions
Let send_message_to_model function infer chat model instead of passing
it as an argument from some chat actors. Better if this logic can be
done in a single place.
Server chat settings can be set for advanced self-hosted or multi-user
cloud setups. They are not necessary anymore as we fallback to use the
users chat model for train of thought now
Fallback to use user chat model for train of thought if server chat
settings not defined.
This simplifies switching chat models for single-user, self-hosted
setups by just changing the chat model on the user settings page.
Server chat settings, when set, controls the default user chat model
and the chat model that is used for Khoj's train of thought.
Previously a self-hosted user had to update both the server chat
settings in the admin panel and their own user chat model in the user
settings panel to explicitly switch to a different chat model (i.e to
switch to a new model for both train of thought & response generation)
You can still set server chat settings to use a different chat
model for train of thought and response generation. But this is only
necessary for advanced self-hosted or cloud hosted setups of Khoj.
Previously you had to refresh the page to see the updated data on
reopening the agents edit card after a save operation.
Now you see the latest saved agent data on reopening the agents edit
card. This should avoid confusion on whether the data was saved
correctly
If a public or protected agent is made private. Other users who were
having conversation with that agent will have to carry on their
conversation using default agent instead
Loading the embeddings model, even locally seems to be taking much
longer. Use timer to track visibility into embedding, cross-encoder
model load times
We should start disambiguating the the max input from output size. Max
prompt size should only be used for the max input context to an LLM.
If required max_output_tokens should be set as a separate new field
Currently, the personality of the agent is only included in the final response that it returns to the user. Historically, this was because models were quite bad at navigating the additional context of personality, and there was a bias towards having more control over certain operations (e.g., tool selection, question extraction).
Going forward, it should be more approachable to have prompts included in the sub tasks that Khoj runs in order to response to a given query. Make this possible in this PR. This also sets us up for agent creation becoming available soon.
Create custom agents in #928
Agents are useful insofar as you can personalize them to fulfill specific subtasks you need to accomplish. In this PR, we add support for using custom agents that can be configured with a custom system prompt (aka persona) and knowledge base (from your own indexed documents). Once created, private agents can be accessible only to the creator, and protected agents can be accessible via a direct link.
Custom tool selection for agents in #930
Expose the functionality to select which tools a given agent has access to. By default, they have all. Can limit both information sources and output modes.
Add new tools to the agent modification form
