- Issue
ML Models truncate entries exceeding some max token limit.
This lowers the quality of search results
- Fix
Split entries by max tokens before indexing.
This should improve searching for content in longer entries.
- Miscellaneous
- Test method to split entries by max tokens
- Context
- The app maintains all text content in a standard, intermediate format
- The intermediate format was loaded, passed around as a dictionary
for easier, faster updates to the intermediate format schema initially
- The intermediate format is reasonably stable now, given it's usage
by all 3 text content types currently implemented
- Changes
- Concretize text entries into `Entries' class instead of using dictionaries
- Code is updated to load, pass around entries as `Entries' objects
instead of as dictionaries
- `text_search' and `text_to_jsonl' methods are annotated with
type hints for the new `Entries' type
- Code and Tests referencing entries are updated to use class style
access patterns instead of the previous dictionary access patterns
- Move `mark_entries_for_update' method into `TextToJsonl' base class
- This is a more natural location for the method as it is only
(to be) used by `text_to_jsonl' classes
- Avoid circular reference issues on importing `Entries' class
- Start standardizing implementation of the `text_to_jsonl' processors
- `text_to_jsonl; scripts already had a shared structure
- This change starts to codify that implicit structure
- Benefits
- Ease adding more `text_to_jsonl; processors
- Allow merging shared functionality
- Help with type hinting
- Drawbacks
- Lower agility to change. But this was already an implicit issue as
the text_to_jsonl processors got more deeply wired into the app
- Image search already uses a sorted list of images to process
- Prevents index of entries to desync when entries, embeddings
generated by a separate server/app instance
- Update existings code, tests to process input-filters as list
instead of str
- Test `text_to_jsonl' get files methods to work with combination of
`input-files' and `input-filters'
Resolves#84
- 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
- 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
- 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
- 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
- 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
- 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
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
