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

Setup

Setup using Docker

1. Clone Repository

git clone https://github.com/debanjum/semantic-search && cd semantic-search

2. Configure

Add Content Directories for Semantic Search to Docker-Compose Update docker-compose.yml to mount your images, org-mode notes, ledger/beancount directories If required, edit config settings in docker_sample_config.yml.

3. Run

docker-compose up -d

Setup on Local Machine

1. Install Dependencies

1. Install Python3 [Required[
2. Install Conda [Required]
3. Install Exiftool [Optional]

   sudo apt-get -y install libimage-exiftool-perl
       

2. Install Semantic Search

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

3. Configure

Configure application search types and their underlying data source/files in sample_config.yml Use the sample_config.yml as reference

4. Run

Load ML model, generate embeddings and expose API to query notes, images, transactions etc specified in config YAML

python3 -m src.main -c=sample_config.yml -vv

Use

• Semantic Search via Emacs
  • Install semantic-search.el
  • Run M-x semantic-search <user-query>
• Semantic Search via API
  • Query: GET http://localhost:8000/search?q=”What is the meaning of life”&t=notes
  • Regenerate Embeddings: GET http://localhost:8000/regenerate
  • Semantic Search API Docs
• UI to Edit Config
  • Config UI

Upgrade

Using Docker

docker-compose up

On Local Machine

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

Acknowledgments

• MiniLM Model for Asymmetric Text Search. See SBert Documentation
• OpenAI CLIP Model for Image Search. See SBert Documentation
• Charles Cave for OrgNode Parser
• Sven Marnach for PyExifTool