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223 lines
No EOL
8 KiB
Markdown
223 lines
No EOL
8 KiB
Markdown
# Khoj 🦅
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![](https://github.com/debanjum/khoj/actions/workflows/test.yml/badge.svg)
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![](https://github.com/debanjum/khoj/actions/workflows/build.yml/badge.svg)
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*A natural language search engine for your personal notes, transactions and images*
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## Table of Contents
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- [Features](#Features)
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- [Demo](#Demo)
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- [Description](#Description)
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- [Analysis](#Analysis)
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- [Architecture](#Architecture)
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- [Setup](#Setup)
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- [Clone](#1.-Clone)
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- [Configure](#2.-Configure)
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- [Run](#3.-Run)
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- [Use](#Use)
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- [Upgrade](#Upgrade)
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- [Troubleshoot](#Troubleshoot)
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- [Miscellaneous](#Miscellaneous)
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- [Development Setup](#Development-setup)
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- [Setup on Local Machine](#Setup-on-local-machine)
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- [Upgrade on Local Machine](#Upgrade-on-local-machine)
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- [Run Unit Tests](#Run-unit-tests)
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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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- [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#miscellaneous)
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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 using a [Web Browser](./src/interface/web/index.html), [Emacs](./src/interface/emacs/khoj.el) or the [API](http://localhost:8000/docs)
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## Demo
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<https://user-images.githubusercontent.com/6413477/181664862-31565b0a-0e64-47e1-a79a-599dfc486c74.mp4>
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### Description
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- User searches for \"*Setup editor*\"
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- The demo looks for the most relevant section in this readme and the [khoj.el readme](https://github.com/debanjum/khoj/tree/master/src/interface/emacs)
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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#installation)
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### Analysis
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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 is idle
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## Architecture
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![](https://github.com/debanjum/khoj/blob/master/docs/khoj_architecture.png)
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## Setup
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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 [sample_config.yml](./config/sample_config.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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## Use
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- **Khoj via Web**
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- Go to <http://localhost:8000/> or open [index.html](./src/interface/web/index.html) in your browser
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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 API**
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- See [Khoj FastAPI Docs](http://localhost:8000/docs)
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- [Query](http://localhost:8000/search?q=%22what%20is%20the%20meaning%20of%20life%22)
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- [Regenerate Embeddings](http://localhost:8000/regenerate?t=ledger)
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- [Configure Application](https://localhost:8000/ui)
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## Upgrade
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``` shell
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docker-compose build --pull
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```
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## Troubleshoot
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- Symptom: 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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- Symptom: Errors out complaining about Tensors mismatch, null etc
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- Mitigation: Delete content-type > image section from `docker_sample_config.yml`
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## Miscellaneous
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- The experimental [chat](localhost:8000/chat) API endpoint uses the [OpenAI API](https://openai.com/api/)
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- It is disabled by default
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- To use it add your `openai-api-key` to config.yml
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## Development Setup
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### Setup on Local Machine
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#### Using Pip
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1. Install Dependencies
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1. Python3, Pip \[Required\]
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2. Virualenv \[Optional\]
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3. Install Exiftool \[Optional\]
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``` shell
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sudo apt-get -y install libimage-exiftool-perl
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```
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2. Install Khoj
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``` shell
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virtualenv -m python3 .venv && source .venv/bin/activate # Optional
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pip install khoj-assistant
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```
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3. Configure
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- Configure files/directories to search in `content-type` section of `sample_config.yml`
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- To run application on test data, update file paths containing `/data/` to `tests/data/` in `sample_config.yml`
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- Example replace `/data/notes/*.org` with `tests/data/notes/*.org`
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4. Run
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Load ML model, generate embeddings and expose API to query notes, images, transactions etc specified in config YAML
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``` shell
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khoj -c=config/sample_config.yml -vv
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```
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#### Using Conda
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1. Install Dependencies
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1. Install Python3 \[Required\]
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2. [Install Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) \[Required\]
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3. Install Exiftool \[Optional\]
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``` shell
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sudo apt-get -y install libimage-exiftool-perl
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```
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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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```
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3. Configure
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- Configure files/directories to search in `content-type` section of `sample_config.yml`
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- To run application on test data, update file paths containing `/data/` to `tests/data/` in `sample_config.yml`
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- Example replace `/data/notes/*.org` with `tests/data/notes/*.org`
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4. Run
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Load ML model, generate embeddings and expose API to query notes, images, transactions etc specified in config YAML
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``` shell
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python3 -m src.main -c=config/sample_config.yml -vv
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```
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### Upgrade On Local Machine
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#### Using Pip
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``` shell
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pip install --upgrade khoj-assistant
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```
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#### Using Conda
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``` shell
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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
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conda activate khoj
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```
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### Run Unit Tests
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``` shell
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pytest
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```
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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 \<5 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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- Applying explicit filters is very slow currently at \~6s. This is because the filters are rudimentary. Considerable speed-ups can be achieved using indexes etc
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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 6 minutes
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- Indexing 4000+ images takes about 15 minutes and more than 8Gb of RAM
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- Once <https://github.com/debanjum/khoj/issues/36> is implemented, it should only take this long on first run
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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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## Credits
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- [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)
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- [OpenAI CLIP Model](https://github.com/openai/CLIP) for Image Search. See [SBert Documentation](https://www.sbert.net/examples/applications/image-search/README.html)
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- Charles Cave for [OrgNode Parser](http://members.optusnet.com.au/~charles57/GTD/orgnode.html)
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- [Org.js](https://mooz.github.io/org-js/) to render Org-mode results on the Web interface
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- [Markdown-it](https://github.com/markdown-it/markdown-it) to render Markdown results on the Web interface
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- Sven Marnach for [PyExifTool](https://github.com/smarnach/pyexiftool/blob/master/exiftool.py) |