- Make search model configurable on server
- Update migration script to get search model from `khoj.yml` to Postgres
- Update first run message on Khoj Desktop and Web app landing page
- Other miscellaneous bug fixes
- Link to Django admin panel for user to create Chat Models on their
Khoj server
- This should only get hit when user is not using Khoj cloud, as Khoj
cloud would already have Chat models configured
- While sigmoid normalization isn't required for reranking.
Normalizing score to distance metrics for both encoder and cross
encoder scores is useful to reason about them
- Softmax wasn't required as don't need probabilities, sigmoid is good
enough to get distance metric
- Expose ability to modify search model via Django admin interface
- Previously the bi_encoder and cross_encoder models to use were set
in code
- Now it's user configurable but with a default config generated by
default
### Overview
Prepare Khoj with multi-user, db support for Khoj Cloud release
### Details
- Add first run experience to configure Khoj via khoj CLI
- Improve Web app settings page: Move files data into content section card. Move content index update button(s) to content section
- Improve OpenAI chat prompts
- Push more general information for OpenAI models into system prompt
- Make it more aware of it's current capabilities
- Weaken asking follow-up questions
- Rate-limit calls to the chat API
- Add back search results quality threshold
- Normalize quality score definitions across cross_encoder, encoder to distance metric
- Remove reference to deprecated button
- Await result of the search query
- Fixed Langchain issue by allowing the Docker image to rebuild with a later package version
- During the migration, the confidence score stopped being used. It
was being passed down from API to some point and went unused
- Remove score thresholding for images as image search confidence
score different from text search model distance score
- Default score threshold of 0.15 is experimentally determined by
manually looking at search results vs distance for a few queries
- Use distance instead of confidence as metric for search result quality
Previously we'd moved text search to a distance metric from a
confidence score.
Now convert even cross encoder, image search scores to distance metric
for consistent results sorting
