mirror of
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466 lines
20 KiB
Markdown
466 lines
20 KiB
Markdown
# Khoj 🦅
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[](https://github.com/debanjum/khoj/actions/workflows/test.yml)
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[](https://github.com/debanjum/khoj/pkgs/container/khoj)
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[](https://pypi.org/project/khoj-assistant/)
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*A natural language search engine for your personal notes, transactions and images*
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**Supported Plugins**
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[](https://github.com/debanjum/khoj/tree/master/src/interface/obsidian#readme)
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[](https://github.com/debanjum/khoj/tree/master/src/interface/emacs#readme)
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## Table of Contents
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- [Features](#Features)
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- [Demos](#Demos)
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- [Khoj in Obsidian](#khoj-in-obsidian)
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- [Khoj in Emacs, Browser](#khoj-in-emacs-browser)
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- [Interfaces](#Interfaces)
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- [Architecture](#Architecture)
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- [Setup](#Setup)
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- [Install](#1-Install)
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- [Run](#2-Run)
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- [Configure](#3-Configure)
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- [Use](#Use)
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- [Interfaces](#Interfaces-1)
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- [Query Filters](#Query-filters)
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- [Upgrade](#Upgrade)
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- [Khoj Server](#upgrade-khoj-server)
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- [Khoj.el](#upgrade-khoj-on-emacs)
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- [Khoj Obsidian](#upgrade-khoj-on-obsidian)
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- [Uninstall](#uninstall)
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- [Troubleshoot](#Troubleshoot)
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- [Advanced Usage](#advanced-usage)
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- [Access Khoj on Mobile](#access-khoj-on-mobile)
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- [Chat with Notes](#chat-with-notes)
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- [Use OpenAI Models for Search](#use-openai-models-for-search)
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- [Search across Different Languages](#search-across-different-languages)
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- [Miscellaneous](#Miscellaneous)
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- [Setup OpenAI API key in Khoj](#set-your-openai-api-key-in-khoj)
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- [Beta API](#beta-api)
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- [Performance](#Performance)
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- [Query Performance](#Query-performance)
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- [Indexing Performance](#Indexing-performance)
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- [Miscellaneous](#Miscellaneous-1)
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- [Development](#Development)
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- [Visualize Codebase](#visualize-codebase)
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- [Setup](#Setup)
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- [Using Pip](#Using-Pip)
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- [Using Docker](#Using-Docker)
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- [Using Conda](#Using-Conda)
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- [Validate](#Validate)
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- [Credits](#Credits)
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## Features
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- **Natural**: Advanced natural language understanding using Transformer based ML Models
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- **Local**: Your personal data stays local. All search, indexing is done on your machine[\*](https://github.com/debanjum/khoj#beta-api)
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- **Incremental**: Incremental search for a fast, search-as-you-type experience
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- **Pluggable**: Modular architecture makes it easy to plug in new data sources, frontends and ML models
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- **Multiple Sources**: Search your Org-mode and Markdown notes, Beancount transactions and Photos
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- **Multiple Interfaces**: Search from your [Web Browser](./src/khoj/interface/web/index.html), [Emacs](./src/interface/emacs/khoj.el) or [Obsidian](./src/interface/obsidian/)
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## Demos
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### Khoj in Obsidian
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https://user-images.githubusercontent.com/6413477/210486007-36ee3407-e6aa-4185-8a26-b0bfc0a4344f.mp4
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<details><summary>Description</summary>
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- Install Khoj via `pip` and start Khoj backend in non-gui mode
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- Install Khoj plugin via Community Plugins settings pane on Obsidian app
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- Check the new Khoj plugin settings
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- Let Khoj backend index the markdown files in the current Vault
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- Open Khoj plugin on Obsidian via Search button on Left Pane
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- Search \"*Announce plugin to folks*\" in the [Obsidian Plugin docs](https://marcus.se.net/obsidian-plugin-docs/)
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- Jump to the [search result](https://marcus.se.net/obsidian-plugin-docs/publishing/submit-your-plugin)
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</details>
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### Khoj in Emacs, Browser
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https://user-images.githubusercontent.com/6413477/184735169-92c78bf1-d827-4663-9087-a1ea194b8f4b.mp4
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<details><summary>Description</summary>
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- Install Khoj via pip
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- Start Khoj app
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- Add this readme and [khoj.el readme](https://github.com/debanjum/khoj/tree/master/src/interface/emacs) as org-mode for Khoj to index
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- Search \"*Setup editor*\" on the Web and Emacs. Re-rank the results for better accuracy
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- Top result is what we are looking for, the [section to Install Khoj.el on Emacs](https://github.com/debanjum/khoj/tree/master/src/interface/emacs#2-Install-Khojel)
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</details>
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<details><summary>Analysis</summary>
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- The results do not have any words used in the query
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- *Based on the top result it seems the re-ranking model understands that Emacs is an editor?*
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- The results incrementally update as the query is entered
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- The results are re-ranked, for better accuracy, once user hits enter
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</details>
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### Interfaces
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## Architecture
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## Setup
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These are the general setup instructions for Khoj.
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- Check the [Khoj.el Readme](https://github.com/debanjum/khoj/tree/master/src/interface/emacs#Setup) to setup Khoj with Emacs
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- Check the [Khoj Obsidian Readme](https://github.com/debanjum/khoj/tree/master/src/interface/obsidian#Setup) to setup Khoj with Obsidian<br />
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Its simpler as it can skip the configure step below.
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### 1. Install
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```shell
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pip install khoj-assistant
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```
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### 2. Run
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```shell
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khoj
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```
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Note: To start Khoj automatically in the background use [Task scheduler](https://www.windowscentral.com/how-create-automated-task-using-task-scheduler-windows-10) on Windows or [Cron](https://en.wikipedia.org/wiki/Cron) on Mac, Linux (e.g with `@reboot khoj`)
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### 3. Configure
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1. Enable content types and point to files to search in the First Run Screen that pops up on app start
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2. Click `Configure` and wait. The app will download ML models and index the content for search
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## Use
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### Interfaces
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- **Khoj via Obsidian**
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- [Install](https://github.com/debanjum/khoj/tree/master/src/interface/obsidian#2-Setup-Plugin) the Khoj Obsidian plugin
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- Click the *Khoj search* icon 🔎 on the [Ribbon](https://help.obsidian.md/User+interface/Workspace/Ribbon) or Search for *Khoj: Search* in the [Command Palette](https://help.obsidian.md/Plugins/Command+palette)
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- **Khoj via Emacs**
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- [Install](https://github.com/debanjum/khoj/tree/master/src/interface/emacs#installation) [khoj.el](./src/interface/emacs/khoj.el)
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- Run `M-x khoj <user-query>`
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- **Khoj via Web**
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- Open <http://localhost:8000/> via desktop interface or directly
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- **Khoj via API**
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- See the Khoj FastAPI [Swagger Docs](http://localhost:8000/docs), [ReDocs](http://localhost:8000/redocs)
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### Query Filters
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Use structured query syntax to filter the natural language search results
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- **Word Filter**: Get entries that include/exclude a specified term
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- Entries that contain term_to_include: `+"term_to_include"`
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- Entries that contain term_to_exclude: `-"term_to_exclude"`
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- **Date Filter**: Get entries containing dates in YYYY-MM-DD format from specified date (range)
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- Entries from April 1st 1984: `dt:"1984-04-01"`
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- Entries after March 31st 1984: `dt>="1984-04-01"`
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- Entries before April 2nd 1984 : `dt<="1984-04-01"`
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- **File Filter**: Get entries from a specified file
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- Entries from incoming.org file: `file:"incoming.org"`
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- Combined Example
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- `what is the meaning of life? file:"1984.org" dt>="1984-01-01" dt<="1985-01-01" -"big" -"brother"`
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- Adds all filters to the natural language query. It should return entries
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- from the file *1984.org*
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- containing dates from the year *1984*
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- excluding words *"big"* and *"brother"*
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- that best match the natural language query *"what is the meaning of life?"*
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## Upgrade
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### Upgrade Khoj Server
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```shell
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pip install --upgrade khoj-assistant
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```
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*Note: To upgrade to the latest pre-release version of the khoj server run below command*
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```shell
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# Maps to the latest commit on the master branch
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pip install --upgrade --pre khoj-assistant
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```
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### Upgrade Khoj on Emacs
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- Use your Emacs Package Manager to Upgrade
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- See [khoj.el readme](https://github.com/debanjum/khoj/tree/master/src/interface/emacs#Upgrade) for details
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### Upgrade Khoj on Obsidian
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- Upgrade via the Community plugins tab on the settings pane in the Obsidian app
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- See the [khoj plugin readme](https://github.com/debanjum/khoj/tree/master/src/interface/obsidian#2-Setup-Plugin) for details
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## Uninstall
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1. (Optional) Hit `Ctrl-C` in the terminal running the khoj server to stop it
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2. Delete the khoj directory in your home folder (i.e `~/.khoj` on Linux, Mac or `C:\Users\<your-username>\.khoj` on Windows)
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3. Uninstall the khoj server with `pip uninstall khoj-assistant`
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4. (Optional) Uninstall khoj.el or the khoj obsidian plugin in the standard way on Emacs, Obsidian
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## Troubleshoot
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#### Install fails while building Tokenizer dependency
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- **Details**: `pip install khoj-assistant` fails while building the `tokenizers` dependency. Complains about Rust.
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- **Fix**: Install Rust to build the tokenizers package. For example on Mac run:
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```shell
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brew install rustup
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rustup-init
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source ~/.cargo/env
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```
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- **Refer**: [Issue with Fix](https://github.com/debanjum/khoj/issues/82#issuecomment-1241890946) for more details
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#### Search starts giving wonky results
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- **Fix**: Open [/api/update?force=true](http://localhost:8000/api/update?force=true)[^2] in browser to regenerate index from scratch
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- **Note**: *This is a fix for when you percieve the search results have degraded. Not if you think they've always given wonky results*
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#### Khoj in Docker errors out with \"Killed\" in error message
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- **Fix**: Increase RAM available to Docker Containers in Docker Settings
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- **Refer**: [StackOverflow Solution](https://stackoverflow.com/a/50770267), [Configure Resources on Docker for Mac](https://docs.docker.com/desktop/mac/#resources)
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#### Khoj errors out complaining about Tensors mismatch or null
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- **Mitigation**: Disable `image` search using the desktop GUI
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## Advanced Usage
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### Access Khoj on Mobile
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1. [Setup Khoj](#Setup) on your personal server. This can be any always-on machine, i.e an old computer, RaspberryPi(?) etc
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2. [Install](https://tailscale.com/kb/installation/) [Tailscale](tailscale.com/) on your personal server and phone
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3. Open the Khoj web interface of the server from your phone browser.<br /> It should be `http://tailscale-ip-of-server:8000` or `http://name-of-server:8000` if you've setup [MagicDNS](https://tailscale.com/kb/1081/magicdns/)
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4. Click the [Add to Homescreen](https://developer.mozilla.org/en-US/docs/Web/Progressive_web_apps/Add_to_home_screen) button
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5. Enjoy exploring your notes, transactions and images from your phone!
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### Chat with Notes
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#### Overview
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- Provides a chat interface to inquire and engage with your notes
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- Chat Types:
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- **Summarize**: Pulls the most relevant note from your notes and summarizes it
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- **Chat**: Also does general chat. It guesses whether to give a general response or search, summarizes from your note. <br />
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E.g *"how was your day?"* will give a general response. But *When did I go surfing?* should give a response from your notes
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- **Note**: *Your query and top note from search result will be sent to OpenAI for processing*
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#### Use
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1. [Setup your OpenAI API key in Khoj](#set-your-openai-api-key-in-khoj)
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2. Open [/chat?t=summarize](http://localhost:8000/chat?t=summarize)[^2]
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3. Type your queries, see summarized response by Khoj from your notes
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#### Demo
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### Use OpenAI Models for Search
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#### Setup
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1. Set `encoder-type`, `encoder` and `model-directory` under `asymmetric` and/or `symmetric` `search-type` in your `khoj.yml`[^1]:
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```diff
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asymmetric:
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- encoder: "sentence-transformers/multi-qa-MiniLM-L6-cos-v1"
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+ encoder: text-embedding-ada-002
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+ encoder-type: src.khoj.utils.models.OpenAI
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cross-encoder: "cross-encoder/ms-marco-MiniLM-L-6-v2"
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- encoder-type: sentence_transformers.SentenceTransformer
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- model_directory: "~/.khoj/search/asymmetric/"
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+ model-directory: null
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```
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2. [Setup your OpenAI API key in Khoj](#set-your-openai-api-key-in-khoj)
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3. Restart Khoj server to generate embeddings. It will take longer than with offline models.
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#### Warnings
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This configuration *uses an online model*
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- It will **send all notes to OpenAI** to generate embeddings
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- **All queries will be sent to OpenAI** when you search with Khoj
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- You will be **charged by OpenAI** based on the total tokens processed
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- It *requires an active internet connection* to search and index
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### Search across Different Languages
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To search for notes in multiple, different languages, you can use a [multi-lingual model](https://www.sbert.net/docs/pretrained_models.html#multi-lingual-models).<br />
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For example, the [paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) supports [50+ languages](https://www.sbert.net/docs/pretrained_models.html#:~:text=we%20used%20the%20following%2050%2B%20languages), has good search quality and speed. To use it:
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1. Manually update `search-type > asymmetric > encoder` to `sentence-transformer/paraphrase-multilingual-MiniLM-L12-v2` in your `~/.khoj/khoj.yml` file for now. See diff of `khoj.yml` below for illustration:
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```diff
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asymmetric:
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- encoder: "sentence-transformers/multi-qa-MiniLM-L6-cos-vi"
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+ encoder: "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
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cross-encoder: "cross-encoder/ms-marco-MiniLM-L-6-v2"
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model_directory: "~/.khoj/search/asymmetric/"
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```
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2. Regenerate your content index. For example, by opening [\<khoj-url\>/api/update?t=force](http://localhost:8000/api/update?t=force)
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## Miscellaneous
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### Set your OpenAI API key in Khoj
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If you want, Khoj can be configured to use OpenAI for search and chat.<br />
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Add your OpenAI API to Khoj by using either of the two options below:
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- Open the Khoj desktop GUI, add your [OpenAI API key](https://beta.openai.com/account/api-keys) and click *Configure*
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Ensure khoj is started without the `--no-gui` flag. Check your system tray to see if Khoj 🦅 is minimized there.
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- Set `openai-api-key` field under `processor.conversation` section in your `khoj.yml`[^1] to your [OpenAI API key](https://beta.openai.com/account/api-keys) and restart khoj:
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```diff
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processor:
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conversation:
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- openai-api-key: # "YOUR_OPENAI_API_KEY"
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+ openai-api-key: sk-aaaaaaaaaaaaaaaaaaaaaaaahhhhhhhhhhhhhhhhhhhhhhhh
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model: "text-davinci-003"
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conversation-logfile: "~/.khoj/processor/conversation/conversation_logs.json"
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```
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**Warning**: *This will enable khoj to send your query and note(s) to OpenAI for processing*
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### Beta API
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- The beta [chat](http://localhost:8000/api/beta/chat), [summarize](http://localhost:8000/api/beta/summarize) and [search](http://localhost:8000/api/beta/search) API endpoints use [OpenAI API](https://openai.com/api/)
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- They are disabled by default
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- To use them:
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1. [Setup your OpenAI API key in Khoj](#set-your-openai-api-key-in-khoj)
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2. Interact with them from the [Khoj Swagger docs](http://locahost:8000/docs)[^2]
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## Performance
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### Query performance
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- Semantic search using the bi-encoder is fairly fast at \<50 ms
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- Reranking using the cross-encoder is slower at \<2s on 15 results. Tweak `top_k` to tradeoff speed for accuracy of results
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- Filters in query (e.g by file, word or date) usually add \<20ms to query latency
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### Indexing performance
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- Indexing is more strongly impacted by the size of the source data
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- Indexing 100K+ line corpus of notes takes about 10 minutes
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- Indexing 4000+ images takes about 15 minutes and more than 8Gb of RAM
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- Note: *It should only take this long on the first run* as the index is incrementally updated
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### Miscellaneous
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- Testing done on a Mac M1 and a \>100K line corpus of notes
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- Search, indexing on a GPU has not been tested yet
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## Development
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### Visualize Codebase
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*[Interactive Visualization](https://mango-dune-07a8b7110.1.azurestaticapps.net/?repo=debanjum%2Fkhoj)*
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### Setup
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#### Using Pip
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##### 1. Install
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```shell
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# Get Khoj Code
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git clone https://github.com/debanjum/khoj && cd khoj
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# Create, Activate Virtual Environment
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python3 -m venv .venv && source .venv/bin/activate
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# Install Khoj for Development
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pip install -e .[dev]
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```
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##### 2. Run
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1. Start Khoj
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```shell
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khoj -vv
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```
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2. Configure Khoj
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- **Via GUI**: Add files, directories to index in the GUI window that pops up on starting Khoj, then Click Configure
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- **Manually**:
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- Copy the `config/khoj_sample.yml` to `~/.khoj/khoj.yml`
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- Set `input-files` or `input-filter` in each relevant `content-type` section of `~/.khoj/khoj.yml`
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- Set `input-directories` field in `image` `content-type` section
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- Delete `content-type` and `processor` sub-section(s) irrelevant for your use-case
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- Restart khoj
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Note: Wait after configuration for khoj to Load ML model, generate embeddings and expose API to query notes, images, transactions etc specified in config YAML
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#### Using Docker
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##### 1. Clone
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```shell
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git clone https://github.com/debanjum/khoj && cd khoj
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```
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##### 2. Configure
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- **Required**: Update [docker-compose.yml](./docker-compose.yml) to mount your images, (org-mode or markdown) notes and beancount directories
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- **Optional**: Edit application configuration in [khoj_docker.yml](./config/khoj_docker.yml)
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##### 3. Run
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```shell
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docker-compose up -d
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```
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*Note: The first run will take time. Let it run, it\'s mostly not hung, just generating embeddings*
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##### 4. Upgrade
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```shell
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docker-compose build --pull
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```
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#### Using Conda
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##### 1. Install Dependencies
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- [Install Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html)
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##### 2. Install Khoj
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```shell
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git clone https://github.com/debanjum/khoj && cd khoj
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conda env create -f config/environment.yml
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conda activate khoj
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python3 -m pip install pyqt6 # As conda does not support pyqt6 yet
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```
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##### 3. Configure
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|
- Copy the `config/khoj_sample.yml` to `~/.khoj/khoj.yml`
|
|
- Set `input-files` or `input-filter` in each relevant `content-type` section of `~/.khoj/khoj.yml`
|
|
- Set `input-directories` field in `image` `content-type` section
|
|
- Delete `content-type`, `processor` sub-sections irrelevant for your use-case
|
|
|
|
##### 4. Run
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|
```shell
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|
python3 -m src.khoj.main -vv
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|
```
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Load ML model, generate embeddings and expose API to query notes, images, transactions etc specified in config YAML
|
|
|
|
##### 5. Upgrade
|
|
```shell
|
|
cd khoj
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|
git pull origin master
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|
conda deactivate khoj
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conda env update -f config/environment.yml
|
|
conda activate khoj
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|
```
|
|
|
|
### Validate
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|
#### Before Make Changes
|
|
1. Install Git Hooks for Validation
|
|
```shell
|
|
pre-commit install -t pre-push -t pre-commit
|
|
```
|
|
- This ensures standard code formatting fixes and other checks run automatically on every commit and push
|
|
- Note 1: If [pre-commit](https://pre-commit.com/#intro) didn't already get installed, [install it](https://pre-commit.com/#install) via `pip install pre-commit`
|
|
- Note 2: To run the pre-commit changes manually, use `pre-commit run --hook-stage manual --all` before creating PR
|
|
|
|
#### Before Creating PR
|
|
|
|
1. Run Tests
|
|
```shell
|
|
pytest
|
|
```
|
|
|
|
2. Run MyPy to check types
|
|
```shell
|
|
mypy --config-file pyproject.toml
|
|
```
|
|
|
|
#### After Creating PR
|
|
- Automated [validation workflows](.github/workflows) run for every PR.
|
|
|
|
Ensure any issues seen by them our fixed
|
|
|
|
- Test the python packge created for a PR
|
|
1. Download and extract the zipped `.whl` artifact generated from the pypi workflow run for the PR.
|
|
2. Install (in your virtualenv) with `pip install /path/to/download*.whl>`
|
|
3. Start and use the application to see if it works fine
|
|
|
|
|
|
## Credits
|
|
|
|
- [Multi-QA MiniLM Model](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1), [All MiniLM Model](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) for Text Search. See [SBert Documentation](https://www.sbert.net/examples/applications/retrieve_rerank/README.html)
|
|
- [OpenAI CLIP Model](https://github.com/openai/CLIP) for Image Search. See [SBert Documentation](https://www.sbert.net/examples/applications/image-search/README.html)
|
|
- Charles Cave for [OrgNode Parser](http://members.optusnet.com.au/~charles57/GTD/orgnode.html)
|
|
- [Org.js](https://mooz.github.io/org-js/) to render Org-mode results on the Web interface
|
|
- [Markdown-it](https://github.com/markdown-it/markdown-it) to render Markdown results on the Web interface
|
|
|
|
|
|
[^1]: Default Khoj config file @ `~/.khoj/khoj.yml`
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|
|
|
[^2]: Default Khoj url @ http://localhost:8000
|