Ensure order of new embedding insertion on incremental update
does not affect the order and value of existing embeddings when
normalization is turned off
Asymmetric was older name used to differentiate between symmetric,
asymmetric search.
Now that text search just uses asymmetric search stick to simpler name
Previous regenerate mechanism did not deduplicate entries with same key
So entries looked different between regenerate and update
Having single func, mark_entries_for_update, to handle both scenarios
will avoid this divergence
Update all text_to_jsonl methods to use the above method for
generating index from scratch
- Current incorrect behavior:
All entries with duplicate compiled form are kept on regenerate
but on update only the last of the duplicated entries is kept
This divergent behavior is not ideal to prevent index corruption
across reconfigure and update
Reuse Search Models across Content Types to reduce Memory Consumption
- Memory consumption now only scales with search models used, not with content types.
Previously each content type had it's own copy of the search ML models.
That'd result in 300+ Mb per enabled text content type
- Split model state into 2 separate state objects, `search_models` and `content_index`.
This allows loading text_search and image_search models first
and then reusing them across all content_types in content_index
- The change should cut down memory utilization quite a bit for most users.
I see a >50% drop in memory utilization on my Khoj instance.
But this will vary for each user based on the amount of content indexed vs number of plugins enabled.
- This change does not solve the RAM utilization scaling with size of the index,
as the whole content index is still kept in RAM while Khoj is running
Should help with #195, #301 and #303
Wrap acquire/release locks in try/catch/finally when updating content
index and search models to prevent lock not being released on error
and causing a deadlock
* Add additional telemetry in order to understand which data sources are the most useful
* Make actions side by side in the configuration page
* Restore main run command
* Update links to point to wiki pages for Github, Notion integrations
* Stanardize nomenclature of the api_type to use _config suffix
Remove header fields that aren't actually helpful for understanding config usage
- Memory consumption now only scales with search models used, not with
content types as well. Previously each content type had it's own
copy of the search ML models. That'd result in 300+ Mb per enabled
content type
- Split model state into 2 separate state objects, `search_models' and
`content_index'.
This allows loading text_search and image_search models first and then
reusing them across all content_types in content_index
- This should cut down memory utilization quite a bit for most users.
I see a ~50% drop in memory utilization.
This will, of course, vary for each user based on the amount of
content indexed vs number of plugins enabled
- This does not solve the RAM utilization scaling with size of the index.
As the whole content index is still kept in RAM while Khoj is running
Should help with #195, #301 and #303
* Add a Github workflow that allows you to build dev versions of Desktop applications
* Add pull_request trigger for testing
* Fix errant open quote in Package Khoj App step
* Nix the release step, since this isn't associated with any tags
- Set retention period for uploaded artifacts to 1 day
* Remove pull_request trigger - limit to manual triggers and pushes to master
Just use a random static version for Khoj on the Docker as otherwise
the hatch vcs dynamic versioning requires the .git directory in the
docker image too
My account doesn't have gpt-4 enabled and it wouldn't work as the default value was always used from extract_questions, where the caller could use the configured model.
- Provide more details on what clicking configure, initialize buttons
or changing the results count slider does
- This shows up on user hovering over those buttons
