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- Break the compute embeddings method into separate methods: compute_image_embeddings and compute_metadata_embeddings - If image_metadata_embeddings isn't defined, do not use it to enhance search results. Given image_metadata_embeddings wouldn't be defined if use_xmp_metadata is False, we can avoid unnecessary addition of args to query method
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
- Python3
- Miniconda
Install
git clone https://github.com/debanjum/semantic-search && cd semantic-search
conda env create -f environment.yml
conda activate semantic-searchRun
Load ML model, generate embeddings and expose API to query specified org-mode files
python3 src/main.py -c=sample_config.yml --verboseUse
-
Semantic Search via Emacs
- Install semantic-search.el
- Run
M-x semantic-search <user-query>or CallC-c s
-
Semantic Search via API
- Query:
GEThttp://localhost:8000/search?q="What is the meaning of life" - Regenerate Embeddings:
GEThttp://localhost:8000/regenerate - Semantic Search API Docs
- Query:
Upgrade
cd semantic-search
git pull origin master
conda env update -f environment.yml
conda activate semantic-searchAcknowledgments
- MiniLM Model for Asymmetric Text Search. See SBert Documentation
- OpenAI CLIP Model for Image Search. See SBert Documentation
- Charles Cave for OrgNode Parser