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- 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
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