Khoj – Chat offline with your second brain using Llama 2

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Khoj – Chat offline with your second brain using Llama 2

Hi folks, we're Debanjum and Saba. We created Khoj as a hobby project 2+ years ago because: (1) Search on the desktop sucked; we just had keyword search on the desktop vs google for the internet; and (2) Natural language search models had become good and easy to run on consumer hardware by this point. Once we made Khoj search incremental, I completely stopped using the default incremental search (C-s) in Emacs. Since then Khoj has grown to support more content types, deeper integrations and chat (using ChatGPT). With Llama 2 released last week, chat models are finally good and easy enough to use on consumer hardware for the chat with docs scenario. Khoj is a desktop application to search and chat with your personal notes, documents and images. It is accessible from within Emacs, Obsidian or your Web browser. It works with org-mode, markdown, pdf, jpeg files and notion, github repositories. It is open-source and can work without internet access (e.g on a plane). Our chat feature allows you to extract answers and create content from your existing knowledge base. Example: "What was that book Trillian mentioned at Zaphod's birthday last week" . We personally use the chat feature regularly to find links, names and addresses (especially on mobile) and collate content across multiple, messy notes. It works online or offline: you can chat without internet using Llama 2 or with internet using GPT3.5+ depending on your requirements. Our search feature lets you quickly find relevant notes, documents or images using natural language. It does not use the internet. Example: Search for "bought flowers at grocery store" will find notes about "roses at wholefoods" . Quickstart: pip install khoj-assistant && khoj See https://docs.khoj.dev/#/setup for detailed instructions We also have desktop apps (in beta) at https://github.com/khoj-ai/khoj/releases/tag/0.10.0 if you want to try them out. Please do try out Khoj and let us know if it works for your use cases? Looking forward to the feedback!