* Khoj
  /Allow natural language search on user content like notes, images using transformer based models/

  All data is processed locally. User can interface with khoj app via [[./interface/emacs/khoj.el][Emacs]], API or Commandline

** Dependencies
   - Python3
   - [[https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links][Miniconda]]

** Install
   #+begin_src shell
   git clone https://github.com/khoj-ai/khoj && cd khoj
   conda env create -f environment.yml
   conda activate khoj
   #+end_src

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

   #+begin_src shell
   python3 main.py --input-files ~/Notes/Schedule.org ~/Notes/Incoming.org --verbose
   #+end_src

** Use
*** *Khoj via Emacs*
     - [[https://github.com/khoj-ai/khoj/tree/master/interface/emacs#installation][Install]] [[./interface/emacs/khoj.el][khoj.el]]
     - Run ~M-x khoj <user-query>~ or Call ~C-c C-s~

*** *Khoj via API*
     - Query: ~GET~ [[http://localhost:42110/api/search?q=%22what%20is%20the%20meaning%20of%20life%22][http://localhost:42110/api/search?q="What is the meaning of life"]]
     - Update Index: ~GET~ [[http://localhost:42110/api/update][http://localhost:42110/api/update]]
     - [[http://localhost:42110/docs][Khoj API Docs]]

*** *Call Khoj via Python Script Directly*
     #+begin_src shell
     python3 search_types/asymmetric.py \
     --compressed-jsonl .notes.jsonl.gz \
     --embeddings .notes_embeddings.pt \
     --results-count 5 \
     --verbose \
     --interactive
     #+end_src

** Acknowledgments
   - [[https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1][MiniLM Model]] for Asymmetric Text Search. See [[https://www.sbert.net/examples/applications/retrieve_rerank/README.html][SBert Documentation]]
   - [[https://github.com/openai/CLIP][OpenAI CLIP Model]] for Image Search. See [[https://www.sbert.net/examples/applications/image-search/README.html][SBert Documentation]]
   - Charles Cave for [[http://members.optusnet.com.au/~charles57/GTD/orgnode.html][OrgNode Parser]]