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Debanjum Singh Solanky f4bde75249 Decouple results shown to user and text the model is trained on 
- 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
2021-08-29 06:06:54 -07:00
src Decouple results shown to user and text the model is trained on 2021-08-29 06:06:54 -07:00
.gitignore Add Readme, License. Update .gitignore 2021-08-15 22:52:37 -07:00
environment.yml Enable Semantic Search on Images 2021-08-22 21:42:37 -07:00
LICENSE Add Readme, License. Update .gitignore 2021-08-15 22:52:37 -07:00
README.org Update Readme to state can now query beancount transactions, images 2021-08-22 21:50:27 -07:00
sample_config.yml Update sample config to add minimal config for ledger, image search 2021-08-22 21:54:49 -07:00

README.org

Semantic Search

Allow natural language search on user content like notes, images, transactions using transformer based models

All data is processed locally. User can interface with semantic-search app via Emacs, API or Commandline

Dependencies

Install 

git clone https://github.com/debanjum/semantic-search && cd semantic-search
conda env create -f environment.yml
conda activate semantic-search

Run

Load ML model, generate embeddings and expose API to query specified org-mode files

python3 src/main.py -c=sample_config.yml --verbose

Use

Upgrade 

  cd semantic-search
  git pull origin master
  conda env update -f environment.yml
  conda activate semantic-search

Acknowledgments