- 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
Create asymmetric.setup method to
- initialize model
- generate compressed jsonl
- compute embeddings
put input_files, input_file_filter validation in org_to_jsonl for
reuse in main.py, asymmetic.py
- Provides mechanism to update notes from within application
- Instead of having to pass the same arguments multiple times
Pass it once (or rely on defaults when possible) and let app keep
state and location of intermediary files
- Allows user to not have to deal with the internals of the application
- E.g user doesn't have to specify the jsonl.gz or embeddings file path
The app will still put those files in a default location
- The user doesn't have to run the generation from the commandline
as a separate step
To allow importing org-to-jsonl as module
- Wrap code in __main__ into a org-to-jsonl method
- Rename processor/org-mode to processor/org_mode
- Add __init__.py to processor directory
- Remove non-standard, unnecessary argument for org-directory
Pass path each file in org-files and org-files-filter argument directly
- Allow shorthand -i, -o for input files, output files
- Default to compress, unless user explicitly specifies not to
Too much overhead to maintain multiple repositories, especially when
the Emacs library for semantic-search is a single file.
Import Readme from the emacs-semantic-search repository too
