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Your AI second brain. A copilot to get answers to your questions, whether they be from your own notes or from the internet. Use powerful, online (e.g gpt4) or private, local (e.g llama3) LLMs. Self-host locally or use our web app. Access from Obsidian, Emacs, Desktop app, Web or Whatsapp.

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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

Install

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

Install Environmental Dependencies

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

Configure

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

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

Upgrade

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

Acknowledgments

About

Your AI second brain. A copilot to get answers to your questions, whether they be from your own notes or from the internet. Use powerful, online (e.g gpt4) or private, local (e.g llama3) LLMs. Self-host locally or use our web app. Access from Obsidian, Emacs, Desktop app, Web or Whatsapp.

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  • Python 53.4%
  • HTML 34.4%
  • Emacs Lisp 3.8%
  • TypeScript 3.6%
  • JavaScript 2.9%
  • CSS 1.4%
  • Other 0.5%