Reason
--
This abstraction will simplify adding other pre-search filters. E.g A date-time filter
Capabilities
--
- Multiple filters can be applied on the query, entries etc before search
- The filters to apply are configured for each type in the search controller
Details
--
- Move `explicit_filters` function into separate module under `search_filter`
- Update signature of explicit filter to take and return `query`, `entries`, `embeddings`
- Use this `explicit_filter` function from `search_filters` module in
`search` method in controller
- The asymmetric query method now just applies the passed filters to the
`query`, `entries` and `embeddings` before semantic search is performed
Details
--
- The filters to apply are configured for each type in the search controller
- Muliple filters can be applied on the query, entries etc before search
- The asymmetric query method now just applies the passed filters to the
query, entries and embeddings before semantic search is performed
Reason
--
This abstraction will simplify adding other pre-search filters. E.g datetime filter
Details
--
- Move explicit_filters function into separate module under search_filter
- Update signature of explicit filter to take and return query, entries, embeddings
- Use this explicit_filter func from search_filters module in query
Reason
--
Abstraction will simplify adding other pre-search filters. E.g datetime filter
## Issue
- Explicit filtering was being done after search by the bi-encoder
but before re-ranking by the cross-encoder
- This limited the quality of results being returned for queries with explicit filters.
The bi-encoder returned results which were going to be excluded.
So the burden of improving those limited results post filtering was on the
cross-encoder, by re-ranking the remaining results to best match the query
## Fix
- Given that the entry and its embedding are at the same index in their respective lists.
We know which entries map to which embedding tensors.
So we can run the filter for blocked, required words before the bi-encoder search.
And limit entries, embeddings being considered for the current query
## Result
- Semantic search by the bi-encoder returns the most relevant results
for the query, knowing that the results aren't going to be filtered out after.
So the cross-encoder shoulders less of the burden of improving the results
## Corollary
- This pre-filtering technique allows us to apply other explicit filters
on entries relevant for the current query, before calling search
- E.g limit search to entries within date/time specified in query
- Issue
- Explicit filtering was earlier being done after search by bi-encoder
but before re-ranking by cross-encoder
- This was limiting the quality of results being returned. As the
bi-encoder returned results which were going to be excluded. So the
burden of improving those limited results post filtering was on the
cross-encoder by re-ranking the remaining results based on query
- Fix
- Given the embeddings corresponding to an entry are at the same index
in their respective lists. We can run the filter for blocked,
required words before the search by the bi-encoder model. And limit
entries, embeddings being considered for the current query
- Result
- Semantic search by the bi-encoder gets to return most relevant
results for the query, knowing that the results aren't going to be
filtered out after. So the cross-encoder shoulders less of the
burden of improving results
- Corollary
- This pre-filtering technique allows us to apply other explicit
filters on entries relevant for the current query
- E.g limit search for entries within date/time specified in query
- test_regenerate_with_valid_content failed when run after test_asymmetric_search
- test_asymmetric_search did't clean the temporary update to config it had made
- This was resulting in regenerate looking for a file that didn't exist
- Use local variable to pass device to asymmetric.setup method via /reload, /regenerate API
- Set default argument to torch.device('cpu') instead of 'cpu' to be more formal
- The reload API adds the ability to separate out the loading of
embeddings from file without having to restart app or (re-)generate embeddings
- Before this the only way to load model from file was by restarting app
- The other way to reload the model embeddings by regenerating them
was to expensive for larger datasets
- This unlocks at least 1 use-case, where
- we regenerate model via an app instance running on a separate server and
- just reload the generated embeddings on the client device
- This allows us to offload the expensive embedding generation
compute to a background server while letting
- This avoids having to (re-)restart application on client device or
be forced to generate embeddings on the client device itself
- But it requires the model relevant files to be synced to the client device
This can be done with any file syncing application like Syncthing
- We can then call /regenerate on server and /reload client on a
regular schedule to keep our data up to date on semantic search
- This is still clunky but it should be commitable
- General enough that it'll work even when a users notes are not in the home directory
- While solving for the special case where:
- Notes are being processed on a different machine and used on a different machine
- But the notes directory is in the same location relative to home on both the machines
- Use Set for Tags instead of dictionary with empty keys
- No Need to store First Tag separately
- Remove properties methods associated with storing first tag separately
- Simplify extraction of tags string in org_to_jsonl
- Split notes_string creation into multiple f-string in separate line
for code readability
- Now that excluding the times line from the raw body of node,
show it in repr so user can see it for reference
- But the model doesn't need to see it for it's embeddings to be
confused by
- Add links to property drawer
- This ensures results returned by semantic search contain these links
- This allows the user to jump to entry within original file for context
- The ID, file+heading based links are more robust to find relevant
entry in original file than the line no based link,
as edits being done by user to original files between embedding regenerations
Sentence Transformer MSMarco Model isn't date aware
So no use of adding scheduled, deadline dates to model embeddings for consideration
This reverts commit a2a08d1354.
- Introduce prompt for GPT to automatically extract user's search intent
- Expose new search api endpoint to use that to set SearchType being
passed to search API
- Currently meant as an experimental API to gauge usefulness,
extendability. Evaluating for phone or voice use-case
To prompt improve readability:
- Remove newline escape sequence and use actual newline directly
- This avoids one long line of text as prompt and
- Remove escaping of double quotes
