- Most important updates include the depedency requirement to setup Postgres when running/setting Khoj up locally
- Add instructiosn for Docker
- Shift to recommend desktop client and update instructions for how to configure Khoj for user
- Adds support for multiple users to be connected to the same Khoj instance using their Google login credentials
- Moves storage solution from in-memory json data to a Postgres db. This stores all relevant information, including accounts, embeddings, chat history, server side chat configuration
- Adds the concept of a Khoj server admin for configuring instance-wide settings regarding search model, and chat configuration
- Miscellaneous updates and fixes to the UX, including chat references, colors, and an updated config page
- Adds billing to allow users to subscribe to the cloud service easily
- Adds a separate GitHub action for building the dockerized production (tag `prod`) and dev (tag `dev`) images, separate from the image used for local building. The production image uses `gunicorn` with multiple workers to run the server.
- Updates all clients (Obsidian, Emacs, Desktop) to follow the client/server architecture. The server no longer reads from the file system at all; it only accepts data via the indexer API. In line with that, removes the functionality to configure org, markdown, plaintext, or other file-specific settings in the server. Only leaves GitHub and Notion for server-side configuration.
- Changes license to GNU AGPLv3
Resolves#467Resolves#488Resolves#303Resolves#345Resolves#195Resolves#280Resolves#461Closes#259Resolves#351Resolves#301Resolves#296
- 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
