- Issue
- Indent regex was previously catching escape sequences like newlines
- This was resulting in entries with only escape sequences in body to
be prepended to property drawers etc during rendering
- Fix
- Update indent regex to only look for spaces in each line
- Only render body when body contains non-escape characters
- Create test to prevent this regression from silently resurfacing
- Previously heading entries were not indexed to maintain search quality
- But given that there are use-cases for indexing entries with no body
- Add a configurable `index_heading_entries' field to index heading entries
- This `TextContentConfig' field is currently only used for OrgMode content
- Let the specific text_to_jsonl method decide which of the
TextContentConfig fields it needs to convert <text> type to jsonl
- This simplifies extending TextContentConfig for a specific type without
modifying all text_to_jsonl methods
- It keeps the number of args being passed to the `text_to_jsonl'
methods in check
- It's more of a hassle to not let word filter go stale on entry
updates
- Generating index on 120K lines of notes takes 1s. Loading from file
takes 0.2s. For less content load time difference will be even smaller
- Let go of startup time improvement for simplicity for now
- Comparing compiled entries is the appropriately narrow target to
identify entries that need to encode their embedding vectors. Given we
pass the compiled form of the entry to the model for encoding
- Hashing the whole entry along with it's raw form was resulting in a
bunch of entries being marked for updated as LINE: <entry_line_no>
is a string added to each entries raw format.
- This results in an update to a single entry resulting in all entries
below it in the file being marked for update (as all their line
numbers have changed)
- Log performance metrics for steps to convert org entries to jsonl
- Having Tags as sets was returning them in a different order
everytime
- This resulted in spuriously identifying existing entries as new
because their tags ordering changed
- Converting tags to list fixes the issue and identifies updated new
entries for incremental update correctly
- What
- Hash the entries and compare to find new/updated entries
- Reuse embeddings encoded for existing entries
- Only encode embeddings for updated or new entries
- Merge the existing and new entries and embeddings to get the updated
entries, embeddings
- Why
- Given most note text entries are expected to be unchanged
across time. Reusing their earlier encoded embeddings should
significantly speed up embeddings updates
- Previously we were regenerating embeddings for all entries,
even if they had existed in previous runs
- Parsed `level` argument passed to OrgNode during init is expected to
be a string, not an integer
- This was resulting in app failure only when parsing org files with
no headings, like in issue #83, as level is set to string of `*`s
the moment a heading is found in the current file
- Previously we were failing if no valid entries while computing
embeddings. This was obscuring the actual issue of no valid entries
found in the specified content files
- Throwing an exception early with clear message when no entries found
should make clarify the issue to be fixed
- See issue #83 for details
- Default config has `input_files' set to None
- This was being passed to `FileBrowser' on Initialization
- But `FileBrowser' expects `content_files' of list type, not None
- This resulted in an unexpected NoneType failure
- The logging to file code expects the config directory to already be setup
- But parent directory of config file was being set up later in code
- This resulted in app start failing with ~/.khoj dir does not exist error
- Pass file associated with entries in markdown, beancount to json converters
- Add File, Word, Date Filters to Ledger, Markdown Types
- Word, Date Filters were accidently removed from the above types yesterday
- File Filter is the only filter that newly got added
- Filter entries, embeddings by ids satisfying all filters in query
func, after each filter has returned entry ids satisfying their
individual acceptance criteria
- Previously each filter would return a filtered list of entries.
Each filter would be applied on entries filtered by previous filters.
This made the filtering order dependent
- Benefits
- Filters can be applied independent of their order of execution
- Precomputed indexes for each filter is not in danger of running
into index out of bound errors, as filters run on original entries
instead of on entries filtered by filters that have run before it
- Extract entries satisfying filter only once instead of doing
this for each filter
- Costs
- Each filter has to process all entries even if previous filters
may have already marked them as non-satisfactory
- This will help filter query to org content type using file filter
- Do not explicitly specify items being extracted from json of each
entry in text_search as all text search content types do not have
file being set in jsonl converters
- Specify just file name to get all notes associated with file at path
- E.g `query` with `file:"file1.org"` will return `entry1`
if `entry1` is in `file1.org` at `~/notes/file.org`
- Test
- Test converting simple file name filter to regex for path match
- Test file filter with space in file name
- Code Changes
- Use list comprehension and `torch.index_select' methods
- to speed selection of entries, embedding tensors satisfying filter
- avoid deep copy of entries, embeddings
- avoid updating existing lists (of entries, embeddings)
- Use word to entry map and set operations to mark entries satisfying
inclusion, exclusion filters
- Results
- Speed up explicit filtering by two orders of magnitude
- Improve consistency of speed up across inclusion and exclusion filtering
- Only the filter knows when entries, embeddings are to be manipulated.
So move the responsibility to deep copy before manipulating entries,
embeddings to the filters
- Create deep copy in filters. Avoids creating deep copy of entries,
embeddings when filter results are being loaded from cache etc
- Do not run the more expensive explicit filter until the word to be
filtered is completed by user. This requires an end sequence marker
to identify end of explicit word filter to trigger filtering
- Space isn't a good enough delimiter as the explicit filter could be
at the end of the query in which case no space
- Stop passing verbose flag around app methods
- Minor remap of verbosity levels to match python logging framework levels
- verbose = 0 maps to logging.WARN
- verbose = 1 maps to logging.INFO
- verbose >=2 maps to logging.DEBUG
- Minor clean-up of app: unused modules, conversation file opening
- This also pushes the updated URL state to history
- Allows jumping back to the web interface after clicking on an image
and having the type set to image search
- Previously type would get reset to the default search type on
jumping back
- CLIP doesn't need full size images for generating embeddings with
decent search results. The sentence transformers docs use images
scaled to 640px width
- Benefits
- Normalize image sizes
- Increase image embeddings generation speed
- Decrease memory usage while generating embeddings from images
- 5e6625a Fix file browser to not add empty line when no file/dir selected
- 8098b8c Bring main window to Top when open from System Tray
- 1c122a8 Place window near top so buttons are not hidden by OS bottom bar
- dfe2546 Set Khoj Icon on Main Desktop Window
- 1b1f8f9 Move Splash screen text below icon. Set the text color to black
- 450f644 Fix path to remove shared libraries when packaging the Windows app
- When no file selected in file browser an empty line/entry gets added
to input entries list
- Bug got introduced due to insufficient update on change to add
instead of insert
- Update is_none_or_empty helper method to also check for empty string
- It is a non-user configurable, app state that is set on app start
- Reduce passing unneeded arguments around. Just set device where
required by looking for ML compute device in global state
- Note: Support for MPS in Pytorch is currently in v1.13.0 nightly builds
- Users will have to wait for PyTorch MPS support to land in stable builds
- Until then the code can be tweaked and tested to make use of the GPU
acceleration on newer Macs
- Pass device to load models onto from app state.
- SentenceTransformer models accept device to load models onto during initialization
- Pass device to load corpus embeddings onto from app state
- CLIP Image score and XMP metadata score are not combining well.
When combined they give non sensical results. Enable only once
figure how best to combine the two.
- Show scores with higher precision for image search
- Image search scores seem to be mostly be between 0.2 - 0.3 for some reason
- Higher precision scores make it easier to understand the quality
of returned results perceived by the model itself
- Allows adding multiple image directories via GUI
- Allow adding multiple files in different directories via GUI
- Previously users couldn't add multiple directories via GUI
They'd have to manually append to input field if multiple files, directories
- To clear/overwrite is much easier.
The user can just select text to delete in input area
- Issue
Fix configuring image search from Desktop GUI. It was broken before.
The Desktop GUI was updating input-files field under content-type > image.
This field is not used for image search. So image search couldn't be
configured from the Desktop GUI
- Fix
- Set input-directories when field of search type image is set from GUI
- Otherwise set input-files field in config
- Show a helpful error message in the GUI to the user, instead of the
crashing if loading config fails, for e.g if file wasn't found
- Collate GUI errors into an ErrorType enum class
- Remove previous error messages before showing the new one
Previously if the embeddings were already there only the khoj.yml
config file would get updated. The embeddings would remain old.
1. This results in a stale app state where the config doesn't
match the embeddings
2. Currently the user cannot update their config from the config
screen. They'd have to use a combination of config screen and web
interface>regenerate button to trigger it or delete their ~/.khoj dir
This commit should resolve the above issues
- Prevent immediate overwrite of re-ranked results by
incremental-search without rerank triggered via post-command-hook.
- This triggers right after the reranking results are rendered, so
user never ends up seeing them
- Add docstrings, mention args in them. Make docstring crisper
- prefix funcs, variables with khoj--
- Require emacs >27.1 for json-parse-buffer
- Use lexical binding
- Add quickstart docs to elisp file itself
- Bump version of khoj.el
- What
- Convert the config screen into the main application window
with configuration as just one of the functionality it provides
- Rename config screen to main window to match new designation
- Why
- System Tray isn't available everywhere (e.g Linux)
- This requires moving functionality into a normal window for cross-compat
- What
- On Linux
- Show Configure Screen, even if not first run experience
- Do no show system tray on Linux
- Quit app on closing Configure Screen
- On Windows, Mac
- Show Configure screen only if first run experience
- Show system tray always
- Do not quit app on closing Configure Screen
- Why
- Configure screen is the only GUI element on Linux. So closing it
should close the application
- On Windows, Mac the system tray exists, so app should not be closed
on closing configure screen
- Start evolving configure screen away from just being a configure screen
- Update Window Title to just say Khoj
- Allow Opening Web Interface to Search from Khoj configure screen
- Rename "Start" Button to more accurate "Configure"
- Disable Search button on first run and while configuring app
- Issue
- In the previous form, updates to self.current_config would update
default_config as python does a shallow copy
- So self.current_config is just referencing the values of default_config
- Hence updates to current_config updates the default_config values too
- This is not what we want
- Fix
- Deep copy the default_config values. Now updates to
self.current_config wouldn't affect the default_config
- Generating embeddings takes time
- If user enables a content type and clicks start.
The app starts to generate embeddings when loading the new settings
- Run this function in a separate thread to keep config screen responsive
- But disable start button to prevent re-entrant threads
- Also show a minimal visual indication that the app is saving state
- Convert Input Fields into PlainTextEdit
- Display Each Selected File on a Separate Line in Field
- Set Height of FileBrowser Input Field based on Number of Lines/Files
- This fixes the field expanding when configure screen is expanded
- Allows for reusability of the labelled text field
- Simplifies the logic to save settings for conversation processor
- Avoid having to pass the khoj_sample.yml data file into pip, native apps
- Packaging data files into python packages is annoying.
- There's `MANIFEST.in`, `data_files` and `package_data` in setup.py
- Bdist, wheel, generated source tarball use different set of these fields
and put the data files in different locations
- Rather just code the default config into a constant. Avoid
pointless file reads as well this way
- Assume path is absolute in yaml util module while saving, loading file
- This follows same convention as jsonl. Which just operates on
passed file path, assuming it is of appropriate form.
Responsibility to put it in appropriate form is on the caller, for now
- Include khoj_sample.yml in pip package to load default config from
- Create khoj config directory if it doesn't exist
- Load config from khoj_sample.yml if khoj.yml config doesn't exist
Conflicts:
- src/main.py
- router functions have moved to router
- move logic to handle null query perf timer variables into
router.py
- set main.py to current branch, not master
- If a user manually edits the input file lines, clicking start should
use that. Currently it just looks at the files selected last via file
browser
- We want to allow users to manually enter file paths in field. Which
is why the field hasn't been set to read-only
- Track current (saved/loaded) config separate from the new config (to
be written) when user clicks Start
- Fallback to using default config when no config for the specific
content type or processor is specified in khoj.yml
- Earlier were only loading default config on first run, not after
- Create Child CheckBox, LineEdit classes for Processor Widgets
- Create ProcessorType, similar to SearchType
- Track ProcessorType the widgets are associated with
- Simplify update, save, load of config based on type
- Update configuration to use by the backend, while app is running
- Trigger after user hits start button with their config.
The config gets written to khoj.yml file first, then the updated
config is loaded onto memory
- Decouple configuring backend from starting server.
Backend search and processors can be configured after the backend
server has started
- Set global state in main instead of in configure_server method.
This allows the app to start even if configure_server exits early in
the first run scenario, where no config available to configure server
- Now start server, even if no config, before GUI started in main
- This refactor of app startup flow will allow users to configure
backend using the configure screen after server start
- Only pass processor config arg required by configure_processor. Not
the unused full config object
- Type arguments passed to methods configure processors
- Import json for use by conversation processor to load logs
- Results get priority screen real estate
- Allows quick speed key based traversal of results as cursor
on switching to buffer is at top level heading
- E.g C-x o n n o 2 jumps to entry in actual file of second result
- Unlike before when it is at the #+STARTUP org buffer customization
settings
- Only allow adding files with appropriate file extension for each search type
- e.g .org for org-mode search, directory for image search
- Extract file paths added to config and enablement state of each search type
- This extracted state will be used to populate the khoj.yml config file
- Simplifies the configure screen layout and allows it to be of constant width
- It was buggy, the configure screen would dynamically expand but not
restore back to original size on disabling search type after enable
- Follow convention, two hyphens indicate variable private to library
- Defcustom are user configurable variables. So they should have single -
- Use khoj-results-count variable directly in code
- Make config_file an optional arg. It defaults to default khoj config dir
- Return args.config as None if no config_file explicitly passed by user
- Parent can use args.config = None as signal to trigger first run experience
- Fix regression since moving to use `which-key-show-full-keymap~
- The above function reads user keypress, so eats up 1 keypress
before starting to enter query
- No way to pass no-paging config via the external function to the
internally used which-key--show-keymap function that does allow
setting no-paging to not read user keypress
- So use the internal function instead and set no-paging arg to t
- The keybindings to select search types was previously confusing as
it only highlighted the final symbol to press (the C-x was shown but
it wasn't made apparent that it had to be pressed before)
- Previously some keybindings unrelated to khoj were also being shown
in the which-key popup. Now only the khoj keybindings are visible
- Search is being reconfigured multiple times in /regenerate and
n/reload. More appropriate name is configure_ rather than initialize_
for it
- Standardize name of methods under configure.py
- Main.py was becoming too big to manage. It had both
controllers/routers and component configurations (search, processors)
in it
- Now that the native app GUI code is also getting added to the main
path, good time to split/modularize/clean main.py
- Put global state into a separate file to share across modules
- Run FastAPI server in a separate thread.
- This allows starting both the server and gui in parallel
- Create System Tray for Khoj
- Contains menu items that open search or config pages in browser
- Rearrange code to have only the code required to start Backend and
GUI in the run() method
- Move the backend setup code into a separate method
- More generally, this allows configuring the khoj search anytime
while in khoj minibuffer window
- Earlier could only configure search type at the start of the search
- What
- Default to last used search type, when no search type specified
- Allow user to change search type before they enter query (and
after they've called khoj), if they want
- Why
- Reduce time from intent to results by using reasonable defaults
- Make interactions smoother, more intuitive
- Test invalid config file path throws. Remove redundant cli test
- Simplify cli parser code
- Do not need to explicitly check if args.config_file set.
argparser checks for positional arguments automatically
- Use standard semantics for cli args
- All positional args are required. Non positional args are optional
- Improve command line --help description
- Add custom validator to throw if neither input_filter or
input_<files|directories> are specified
- Set field expecting paths to type Path
- Now that default_config isn't used in code. We can update
fields in rawconfig to specify whether they're required or not.
This lets pydantic validate config file and throw appropriate error
- Reason
- Simplifies code. No merge_dict required
- 1 place for user to see all configurables, defaults and required values
- Details
- Remove default_config from code. Set defaults in khoj_sample.yml itself
- Keep fields required to be set by user as empty in khoj_sample to YAML
- Set defaults for fields not requiring configuration by user
- Do not want browsers to use the small, grainy favicons
- Firefox for Android does use the bigger icon, when it's the only one available
- Update svg to match the 144x144 ratio just for consistency
Currently only get into this state when debug breakpoints on backend
are keeping the connection open and user exits khoj search from Emacs
Results in a number of open connections that slow khoj down.
- Most concretely right now,
it eliminates the re-rank latency hit
on re-rank triggered on user hitting enter
after re-rank is already done on user idle
in the emacs interface
- Improves search latency of (incremental) search
- Makes it easier to fold/unfold, traverse and read results
- This 2 level nesting is already being used on the web interface
- Previously we were using the original nesting depth of the entry.
This was aimed at providing more of the orginal context of the
results. But currently this additional information does not provide
as much, for the decreased legibility of the results
- Improve code layout by ensuring all web interface specific code
under the src/interface/web directory
- Rename config API to more specifi /config instead of /ui
- Rename config data GET, POST api to /config/data instead of /config
- Previously we were statically populating types dropdown field in the web interface with all available search types
- This change populates the type dropdown field with only search types that are enabled/configured
- It queries the `/config` backend API to see which of the available search types are configured
- Populate via `.then` after enabled search types in dropdown are
populated
- Call to `/config` API is async and will usually complete after the value of type field is set from url
- So value of type field would earlier be overridden when search types
dropdown is populated after the call to `/config` API completes
- Get /config API and check config for which available search types is
populated. This gives us the list of enabled search types
- Dynamically populate search type field with enabled search types only
- Setting query value to default option when query param wasn't
passed via URL was overriding placeholder text in query field
- We wanted placeholder text in field, not the query field to actually
be populated by placeholder text
- This clears field when user starts typing query into the query field,
instead of them having to manually delete the default text populated
- Setting up default compressed-jsonl, embeddings-file was only required
for org search_type, while org-files and org-filter were allowed to be
passed as command line argument
- This avoided having to set compressed-jsonl and embeddings-file via
command line argument as well for org search type
- Now that all search types are only configurable via config file, We
can default all search types to None. The default config for the
rest of the search types wasn't being used anyway
- Previously org-files were configurable via cmdline args.
Where as none of the other search types are
- This is an artifact of how the application grew
- It can be removed for better consistency and
equal preference given all search types
Having org-mode result headings change size based on their depth in
the source document makes is a confusing UI experience.
Improve font-size, line-spacing and margins of results to make
delineation between entries, and differntiating between entry heading
and it's body easier to visually infer.
Do not white-space: pre-line. Improves rendering of Markdown results
## Support Incremental Search on Khoj Web Interface
- Use default, fast path to query /search API while user is typing
- Upgrade to cross-encoder re-ranked results once user hits enter on search box
## Improve Render of Org Results on Web Interface
- We were previously just wrapping results from /search API into a pre formatted div field. This was not easy to read
- Use [org.js](https://mooz.github.io/org-js/) to render results from Khoj `/search` API as proper HTML
- Improve org.js to render all task states, stylize task tags and make org-mode results look more like original content
Closes#42#41
In current state:
- Rerank results:
- If user idles while entering query OR
- exits normally
- Do not rerank results:
- If user exits abnormally, e.g via C-g from query
- Rename functions to more standard, descriptive names
- Keep known, required code for incremental search
- E.g Do not set buffer local flag in hooks on minibuffer setup
- Only query when user in khoj minibuffer
- Use active-minibuffer-window and track khoj minibuffer
- (minibuffer-prompt) is not useful for our use-case here
- (For now) Run re-rank only if user idle while querying
- Do not run rerank on teardown/completion
- The reranking lag (~2s) is annoying; hit enter,
wait to see results
- Also triggered when user exits abnormally,
so C-g also results in rerank which is even more annoying
- Emacs will still hang if re-ranking gets triggered on idle but
that's better than always getting triggered. And better than not
having mechanism to get results re-ranked via cross-encoder at all
- Update khoj-simple to work cross-encoder re-ranked results like before
- Increment major version as incremental search considered a breaking
change and a major update to search capability
- Improve search speed by ~10x
Tested on corpus of 125K lines, 12.5K entries
- Allow cross-encoder to re-rank results by settings &?r=true when querying /search API
- It's an optional param that default to False
- Earlier all results were re-ranked by cross-encoder
- Making this configurable allows for much faster results, if desired
but for lower accuracy
- Formalize filters into class with can_filter() and filter() methods
- Use can_filter() method to decide whether to apply filter and
create deep copies of entries and embeddings for it
- Improve search speed for queries with no filters
as deep copying entries, embeddings takes the most time
after cross-encodes scoring when calling the /search API
Earlier we would create deep copies of entries, embeddings
even if the query did not contain any filter keywords
- Reason:
Allow natural search on markdown based notes, documentation,
websites etc
- Details:
- Create markdown processor to extract Markdown entries (identified by
Heading) into standard jsonl format required by text_search
- Update API, Configs to support interfacing with new markdown type
- Update Emacs, Web clients to support interfacing with new markdown
type via API
- Update Readme to mentiond markdown is also supported
Closes#35
- The code for both the text search types were mostly the same
It was earlier done this way for expedience while experimenting
- The minor differences were reconciled and merged into a single
text_search type
- This simplifies the app and making it easier to process other
text types
Now that the logic to compile entries is in the processor layer, the
extract_entries method is standard across (text) search_types
Extract the load_jsonl method as a utility helper method.
Use it in (a)symmetric search types
- The logic for compiling a beancount entry (for later encoding) now
completely resides in the org-to-jsonl processor layer
- This allows symmetric search to be generic and not be aware of
beancount specific properties that were extracted by the
beancount-to-jsonl processor layer
- Now symmetric search just expects the jsonl to (at least) have the
'compiled' and 'raw' keys for each entry. What original text the
entry was compiled from is irrelevant to it. The original text
could be location, transaction, chat etc, it doesn't have to care
- The logic for compiling an org-mode entry (for later encoding) now
completely resides in the org-to-jsonl processor layer
- This allows asymmetric search to be generic and not be aware of
org-mode specific properties that were extracted by the org-to-jsonl
processor layer
- Now asymmetric search just expects the jsonl to (at least) have the
'compiled' and 'raw' keys for each entry. What original text the
entry was compiled from is irrelevant to it. The original text
could be mail, chat, markdown, org-mode etc, it doesn't have to care
- Pass Scheduled, Closed Dates of Entries to Include in Embeddings
- The (new?) model seems to understand dates. So can give more
relevant entries if date in natural language mentioned in query
- E.g "Went Surfing with Friends" vs "Went Surfing with Friends in 1984"
will give different results, with the second prioritizing entries
mentioning any entries with closed, scheduled dates from 1984
- While it's true those strings are going to be used to generated
embeddings, the more generic term allows them to be used elsewhere as
well
- Their main property is that they are processed, compiled for
usage by semantic search
- Unlike the 'raw' string which contains the external representation
of the data, as is
- Had already made some progress on this earlier by updating the image
search responses. But needed to update the text search responses to
use lowercase entry and score
- Update khoj.el to consume the updated json response keys for text
search
- Image order returned by glob is OS dependent
- This prevented sharing image embeddings across machines running different OS
- A stable sort order for processed images allows sharing embeddings
across machines.
- Use case:
A more powerful, always on machine actually computes the image embeddings regularly
The client machine just load these periodically to provide semantic search functionality
- Handle case where current image batch smaller than batch_size
- Handle case where no XMP metadata for current image
- return empty strings in such a scenario instead of ". "
Issue:
- Had different schema of extracted entries for symmetric_ledger vs asymmetric
- Entry extraction for asymmetric was dirty, relying on cryptic
indices to store raw entry vs cleaned entry meant to be passed to embeddings
- This was pushing the load of figuring out what property to extract
from each entry to downstream processes like the filters
- This limited the filters to only work for asymmetric search, not for
symmetric_ledger
- Fix
- Use consistent format for extracted entries
{
'embed': entry_string_meant_to_be_passed_to_model_and_get_embeddings,
'raw' : raw_entry_string_meant_to_be_passed_to_use
}
- Result
- Now filters can be applied across search types, and the specific
field they should be applied on can be configured by each search
type
- The all-MiniLM-L6-v2 is more accurate
- The exact previous model isn't benchmarked but based on the
performance of the closest model to it. Seems like the new model
maybe similar in speed and size
- On very preliminary evaluation of the model, the new model seems
faster, with pretty decent results
- The multi-qa-MiniLM-L6-cos-v1 is more extensively benchmarked[1]
- It has the right mix of model query speed, size and performance on benchmarks
- On hugging face it has way more downloads and likes than the msmarco model[2]
- On very preliminary evaluation of the model
- It doubles the encoding speed of all entries (down from ~8min to 4mins)
- It gave more entries that stay relevant to the query (3/5 vs 1/5 earlier)
[1]: https://www.sbert.net/docs/pretrained_models.html
[2]: https://huggingface.co/sentence-transformers
- Avoids having to click the query input box
- Just open page, type whatever and hit enter to do image search
- For other search types select appropriate type from dropdown
- Use shr to render image response from html in result buffer
Earlier was using org-mode. But rendering HTML with shr seems cleaner
- Use Headings to Add highlights
- Use Random to Force fetch of Image. Similar to what was done for Web interface
- Remove trailing elisp brackets from response
- Show query match scores by image model for each image in results
- Metadata match score were consistently giving higher scores by a
factor of ~3x wrt to image match score. This was resulting in all
results being from the metadata match with query and none from the
image match with query.
- Scaling the metadata match scores down by scaling factor seems to
give more consistently give a blend of results from both image and
metadata matches
Adding a random, unused url param at the end of the img.src string
fixes the issue. As the browser thinks it's a new image and doesn't
use the image data that's already cached because of which it wasn't
even making the fetch call for the image
- Allow viewing image results returned by Semantic Search.
Until now there wasn't any interface within the app to view image
search results. For text results, we at least had the emacs interface
- This should help with debugging issues with image search too
For text the Swagger interface was good enough
- Copy images to accessible directory
- Return URL paths to them to ease access
- This is to be used in the web interface to render image results
directly in browser
- Return image, metadata scores for each image in response as well
This should help get a better sense of image scores along both
XMP metadata and whole image axis
- With \t Last Word in Headings was suffixed by \t and so couldn't be
filtered by
- User interacts with raw entries, so run explicit filters on raw entry
- For semantic search using the filtered entry is cleaner, still
- Fix date_filter date_in_entry within query range check
- Extracted_date_range is in [included_date, excluded_date) format
- But check was checking for date_in_entry <= excluded_date
- Fixed it to do date_in_entry < excluded_date
- Fix removal of date filter from query
- Add tests for date_filter
- Default to looking at dates from past, as most notes are from past
- Look for dates in future for cases where it's obvious query is for
dates in the future but dateparser's parse doesn't parse it at all.
E.g parse('5 months from now') returns nothing
- Setting PREFER_DATES_FROM_FUTURE in this case and passing just
parse('5 months') to dateparser.parse works as expected
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 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
- 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
- Add search query to top of buffer as Beancount comment
- Remove trailing ) from response
- Separate entries by empty line
- Load beancount-mode in semantic search on ledger buffer
- Fix loading entries from jsonl in extract_entries method
- Only extract Title from jsonl of each entry
This is the only thing written to the jsonl for symmetric ledger
- This fixes the trailing escape seq in loaded entries
- Remove the need for semantic-search.el response reader to do pointless complicated cleanup
- Make symmetric_ledger:extract_entries use beancount_to_jsonl:load_jsonl
Both methods were doing similar work
- Make load_jsonl handle loading entries from both gzip and uncompressed jsonl
Conversation logs structure now has session info too instead of just chat info
Session info will allow loading past conversation summaries as context for AI in new conversations
{
"session": [
{
"summary": <chat_session_summary>,
"session-start": <session_start_index_in_chat_log>,
"session-end": <session_end_index_in_chat_log>
}],
"chat": [
{
"intent": <intent-object>
"trigger-emotion": <emotion-triggered-by-message>
"by": <AI|Human>
"message": <chat_message>
"created": <message_created_date>
}]
}
- Allow conversing with user using GPT's contextually aware, generative capability
- Extract metadata, user intent from user's messages using GPT's general understanding
Details
- Rename method query_* to query in search_types for standardization
- Wrapping Config code in classes simplified mocking test config
- Reduce args beings passed to a function by passing it as single
argument wrapped in a class
- Minimize setup in main.py:__main__. Put most of it into functions
These functions can be mocked if required in tests later too
Setup Flow:
CLI_Args|Config_YAML -> (Text|Image)SearchConfig -> (Text|Image)SearchModel
- Wrap Image, Music, Ledger search into the type of SearchModel they use
Similar to what was done for notes model by wrapping it's config
into an AsymmetricSearchModel.
- Use the uber wrapper class to expose all type specific search models
- Wrap asymmetric search model parameters into AsymmetricSearchModel class
- Create wrapper for all search type models. Put notes search model into it
- Test notes search end-to-end from client API layer to results.
Use model build on test data
- Cleaner, more idiomatic usage of a global variable
- Simplifies mocking when testing client in pytest as setting wrapped
in object rather than a simple type. So passed around by reference
- Use a SearchType to limit types that can be passed by user
- FastAPI automatically validates type passed in query param
- Available type options show up in Swagger UI, FastAPI docs
- controller code looks neater instead of doing string comparisons for type
- Test invalid, valid search types via pytest
- Break the compute embeddings method into separate methods:
compute_image_embeddings and compute_metadata_embeddings
- If image_metadata_embeddings isn't defined, do not use it to enhance
search results. Given image_metadata_embeddings wouldn't be defined
if use_xmp_metadata is False, we can avoid unnecessary addition of
args to query method
- Issue:
Process would get killed while encoding images
for consuming too much memory
- Fix:
- Encode images in batches and append to image_embeddings
- No need to use copy or deep_copy anymore with batch processing.
It would earlier throw too many files open error
Other Changes:
- Use tqdm to see progress even when using batch
- See progress bar of encoding independent of verbosity (for now)
- Details
- The CLIP model can represent images, text in the same vector space
- Enhance CLIP's image understanding by augmenting the plain image
with it's text based metadata.
Specifically with any subject, description XMP tags on the image
- Improve results by combining plain image similarity score with
metadata similarity scores for the highest ranked images
- Minor Fixes
- Convert verbose to integer from bool in image_search.
It's already passed as integer from the main program entrypoint
- Process images with ".jpeg" extensions too
- Previously:
The text the model was trained on was being used to
re-create a semblance of the original org-mode entry.
- Now:
- Store raw entry as another key:value in each entry json too
Only return actual raw org entries in results
But create embeddings like before
- Also add link to entry in file:<filename>::<line_number> form
in property drawer of returned results
This can be used to jump to actual entry in it's original file
- YAML Config
- Can specify all params[1] earlier being passed via cmd args in config YAML
- Can now also configure sentence-transformer models to use etc for search
- [1] Config params
- org files
- compressed entries file config path
- embeddings file config path
- Include sample_config.yaml
- Include sample .org file from this repos readmes
- CLI
- Configuration Priority: Config via cmd > Config via YAML > Default Config
- Test CLI, include test config.yml for the tests
- Set default type to None unless set via query param to API
Run notes search if search_enabled, also if type is None (default)
Prepares for running queries on all search types unless type
specified in API query param
- Update Readme
