# Khoj 🦅
[![build](https://github.com/debanjum/khoj/actions/workflows/build.yml/badge.svg)](https://github.com/debanjum/khoj/actions/workflows/build.yml)
[![test](https://github.com/debanjum/khoj/actions/workflows/test.yml/badge.svg)](https://github.com/debanjum/khoj/actions/workflows/test.yml)
[![publish](https://github.com/debanjum/khoj/actions/workflows/publish.yml/badge.svg)](https://github.com/debanjum/khoj/actions/workflows/publish.yml)

*A natural language search engine for your personal notes, transactions and images*

## Table of Contents

- [Features](#Features)
- [Demo](#Demo)
  - [Description](#Description)
  - [Analysis](#Analysis)
  - [Interfaces](#Interfaces)
- [Architecture](#Architecture)
- [Setup](#Setup)
  - [Install](#1-Install)
  - [Configure](#2-Configure)
  - [Run](#3-Run)
- [Use](#Use)
  - [Interfaces](#Interfaces-1)
  - [Query Filters](#Query-filters)
- [Upgrade](#Upgrade)
- [Troubleshoot](#Troubleshoot)
- [Miscellaneous](#Miscellaneous)
- [Performance](#Performance)
  - [Query Performance](#Query-performance)
  - [Indexing Performance](#Indexing-performance)
  - [Miscellaneous](#Miscellaneous-1)
- [Development](#Development)
  - [Setup](#Setup)
    - [Using Pip](#Using-Pip)
    - [Using Docker](#Using-Docker)
    - [Using Conda](#Test)
  - [Test](#Test)
- [Credits](#Credits)

## Features

- **Natural**: Advanced natural language understanding using Transformer based ML Models
- **Local**: Your personal data stays local. All search, indexing is done on your machine[\*](https://github.com/debanjum/khoj#miscellaneous)
- **Incremental**: Incremental search for a fast, search-as-you-type experience
- **Pluggable**: Modular architecture makes it easy to plug in new data sources, frontends and ML models
- **Multiple Sources**: Search your Org-mode and Markdown notes, Beancount transactions and Photos
- **Multiple Interfaces**: Search using a [Web Browser](./src/interface/web/index.html), [Emacs](./src/interface/emacs/khoj.el) or the [API](http://localhost:8000/docs)

## Demo

https://user-images.githubusercontent.com/6413477/184735169-92c78bf1-d827-4663-9087-a1ea194b8f4b.mp4

### Description

- Install Khoj via pip
- Start Khoj app
- Add this readme and [khoj.el readme](https://github.com/debanjum/khoj/tree/master/src/interface/emacs) as org-mode for Khoj to index
- Search \"*Setup editor*\" on the Web and Emacs. Re-rank the results for better accuracy
- 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#installation)

### Analysis

- The results do not have any words used in the query
  - *Based on the top result it seems the re-ranking model understands that Emacs is an editor?*
- The results incrementally update as the query is entered
- The results are re-ranked, for better accuracy, once user hits enter

### Interfaces

![](https://github.com/debanjum/khoj/blob/master/docs/interfaces.png)

## Architecture

![](https://github.com/debanjum/khoj/blob/master/docs/khoj_architecture.png)

## Setup
### 1. Install

```shell
pip install khoj-assistant
```

### 2. Start App

```shell
khoj
```

### 3. Configure

1. Enable content types and point to files to search in the First Run Screen that pops up on app start
2. Click `Configure` and wait. The app will download ML models and index the content for search

## Use
### Interfaces

- **Khoj via Web**
  - Open <http://localhost:8000/> via desktop interface or directly
- **Khoj via Emacs**
  - [Install](https://github.com/debanjum/khoj/tree/master/src/interface/emacs#installation) [khoj.el](./src/interface/emacs/khoj.el)
  - Run `M-x khoj <user-query>`
- **Khoj via API**
  - See the Khoj FastAPI [Swagger Docs](http://localhost:8000/docs), [ReDocs](http://localhost:8000/redocs)

### Query Filters
Use structured query syntax to filter the natural language search results
- **Word Filter**: Get entries that include/exclude a specified term
  - Entries that contain term_to_include: `+"term_to_include"`
  - Entries that contain term_to_exclude: `-"term_to_exclude"`
- **Date Filter**: Get entries containing dates in YYYY-MM-DD format from specified date (range)
  - Entries from April 1st 1984: `dt:"1984-04-01"`
  - Entries after March 31st 1984: `dt>="1984-04-01"`
  - Entries before April 2nd 1984 : `dt<="1984-04-01"`
- **File Filter**: Get entries from a specified file
  - Entries from incoming.org file: `file:"incoming.org"`
- Combined Example
  - `what is the meaning of life? file:"1984.org" dt>="1984-01-01" dt<="1985-01-01" -"big" -"brother"`
  - Adds all filters to the natural language query. It should return entries
    - from the file *1984.org*
    - containing dates from the year *1984*
    - excluding words *"big"* and *"brother"*
    - that best match the natural language query *"what is the meaning of life?"*

## Upgrade

```shell
pip install --upgrade khoj-assistant
```

## Troubleshoot

- Symptom: Errors out complaining about Tensors mismatch, null etc
  - Mitigation: Disable `image` search using the desktop GUI
- Symptom: Errors out with \"Killed\" in error message in Docker
  - Fix: Increase RAM available to Docker Containers in Docker Settings
  - Refer: [StackOverflow Solution](https://stackoverflow.com/a/50770267), [Configure Resources on Docker for Mac](https://docs.docker.com/desktop/mac/#resources)

## Miscellaneous

- The beta [chat](http://localhost:8000/api/beta/chat) and [search](http://localhost:8000/api/beta/search) API endpoints use [OpenAI API](https://openai.com/api/)
  - It is disabled by default
  - To use it add your `openai-api-key` via the app configure screen
  - Warning: *If you use the above beta APIs, your query and top result(s) will be sent to OpenAI for processing*

## Performance

### Query performance

- Semantic search using the bi-encoder is fairly fast at \<50 ms
- Reranking using the cross-encoder is slower at \<2s on 15 results. Tweak `top_k` to tradeoff speed for accuracy of results
- Filters in query (e.g by file, word or date) usually add \<20ms to query latency

### Indexing performance

- Indexing is more strongly impacted by the size of the source data
- Indexing 100K+ line corpus of notes takes about 10 minutes
- Indexing 4000+ images takes about 15 minutes and more than 8Gb of RAM
- Note: *It should only take this long on the first run* as the index is incrementally updated

### Miscellaneous

- Testing done on a Mac M1 and a \>100K line corpus of notes
- Search, indexing on a GPU has not been tested yet

## Development
### Setup
#### Using Pip
##### 1. Install

```shell
git clone https://github.com/debanjum/khoj && cd khoj
python3 -m venv .venv && source .venv/bin/activate
pip install -e .
```

##### 2. Configure

- 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` and `processor` sub-section(s) irrelevant for your use-case

##### 3. Run

```shell
khoj -vv
```
Load ML model, generate embeddings and expose API to query notes, images, transactions etc specified in config YAML

##### 4. Upgrade

```shell
# To Upgrade To Latest Stable Release
# Maps to the latest tagged version of khoj on master branch
pip install --upgrade khoj-assistant

# To Upgrade To Latest Pre-Release
# Maps to the latest commit on the master branch
pip install --upgrade --pre khoj-assistant

# To Upgrade To Specific Development Release.
# Useful to test, review a PR.
# Note: khoj-assistant is published to test PyPi on creating a PR
pip install -i https://test.pypi.org/simple/ khoj-assistant==0.1.5.dev57166025766
```

#### Using Docker
##### 1. Clone

```shell
git clone https://github.com/debanjum/khoj && cd khoj
```

##### 2. Configure

- **Required**: Update [docker-compose.yml](./docker-compose.yml) to mount your images, (org-mode or markdown) notes and beancount directories
- **Optional**: Edit application configuration in [khoj_docker.yml](./config/khoj_docker.yml)

##### 3. Run

```shell
docker-compose up -d
```

*Note: The first run will take time. Let it run, it\'s mostly not hung, just generating embeddings*

##### 4. Upgrade

```shell
docker-compose build --pull
```

#### Using Conda
##### 1. Install Dependencies
- [Install Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html)

##### 2. Install Khoj
```shell
git clone https://github.com/debanjum/khoj && cd khoj
conda env create -f config/environment.yml
conda activate khoj
python3 -m pip install pyqt6  # As conda does not support pyqt6 yet
```

##### 3. Configure
- 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
```shell
python3 -m src.main -vv
```
  Load ML model, generate embeddings and expose API to query notes, images, transactions etc specified in config YAML

##### 5. Upgrade
```shell
cd khoj
git pull origin master
conda deactivate khoj
conda env update -f config/environment.yml
conda activate khoj
```

### Test
```shell
pytest
```

## 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