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79aff85fcb
- Setup now happens on first run of application - Embeddings can now be regenerated without killing app by calling API
2.1 KiB
2.1 KiB
Semantic Search
Provide natural language search on user personal content like notes, images using ML models
All data is processed locally. User can interface with semantic-search app via Emacs, API or Commandline
Dependencies
- Python3
- Miniconda
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 main.py --input-files ~/Notes/Schedule.org ~/Notes/Incoming.org --verbose
Use
-
Semantic Search via Emacs
- Install semantic-search.el
- Run
M-x semantic-search "<user-query>"
or CallC-c C-s
-
Semantic Search via API
- Query:
GET
http://localhost:8000/search?q="What is the meaning of life" - Regenerate Embeddings:
GET
http://localhost:8000/regenerate - Semantic Search API Docs
- Query:
-
Call Semantic Search via Python Script Directly
python3 search_types/asymmetric.py \ --compressed-jsonl .notes.jsonl.gz \ --embeddings .notes_embeddings.pt \ --results-count 5 \ --verbose \ --interactive
Acknowledgments
- MiniLM Model for Asymmetric Text Search. See SBert Documentation
- OpenAI CLIP Model for Image Search. See SBert Documentation
- Charles Cave for OrgNode Parser